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Sample records for human electroencephalogram eeg

  1. EEG (Electroencephalogram)

    Science.gov (United States)

    ... in diagnosing brain disorders, especially epilepsy or another seizure disorder. An EEG might also be helpful for diagnosing ... Sometimes seizures are intentionally triggered in people with epilepsy during the test, but appropriate medical care is ...

  2. Educational simulation of the electroencephalogram (EEG)

    NARCIS (Netherlands)

    Beer, de N.A.M.; Meurs, van W.L.; Grit, M.B.M.; Good, M.L.; Gravenstein, D.

    2001-01-01

    We describe a model for simulating a spontaneous electroencephalogram (EEG) and for simulating the effects of anesthesia on the EEG, to allow anesthesiologists and EEG technicians to learn and practice intraoperative EEG monitoring. For this purpose, we developed a linear model to manipulate the

  3. Analyzing Electroencephalogram Signal Using EEG Lab

    Directory of Open Access Journals (Sweden)

    Mukesh BHARDWAJ

    2009-01-01

    Full Text Available The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact forms a unique topography onto the scalp – ‘scalp map‘. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.

  4. Dimensionality of the human electroencephalogram

    Energy Technology Data Exchange (ETDEWEB)

    Mayer-Kress, G.; Layne, S.P.

    1986-01-01

    The goal was to evaluate anesthetic depth in patients by dimensional analysis. Although it was difficult to obtain clean EEG records from the operating room due to noise of electrocautery and movement of the patient's head by operating room personnel. The results are presented on one case of our calculations, followed by a discussion of problems associated with dimensional analysis of the EEG. We consider only two states: aware but quiet, and medium anesthesia. The EEG data we use comes from Hanley and Walts. It was selected because anesthesia was induced by a single agent, and because of its uninterrupted length and lack of artifacts. 26 refs., 27 figs., 1 tab.

  5. Using Electroencephalogram (EEG to Understand The Effect of Price Perception on Consumer Preference

    Directory of Open Access Journals (Sweden)

    Fitri Aprilianty

    2016-06-01

    Full Text Available The research examines the influence of price as product cues on consumer’s perception and evaluation by using the application of electroencephalogram (EEG. This method can give objective information about consumer reactions towards product cues that will drive consumer’s choice. The main research objective was to observe and evaluate consumer’s brain activity in different brain regions while they were being exposed by several price levels (low, medium, high of underwear as stimuli and focused mainly on liking/disliking the stimuli. The participants consist of 10 female and 10 male consumers within 18-24 years old, have normal vision, right handed, and considered as potential purchasers of underwear. The participant’s brain activity was collected using Emotiv EPOC neuroheadset (EEG with international 10/20 system and was obtained in Beta frequency bands (13–30 Hz. The result indicated that there was a clear and significant change (p<0.05 in the EEG brain spectral activities of right and left hemisphere in the frontal (F3 & F4, temporal (T7 & T8, and parietal (P7 & P8 regions when participants indicated their attentiveness towards each price level stimulus. The results show, the male and female participant’s tactile sensations in parietal lobe does not give more favorable attention towards particular price stimulus, but the difference price perceptions in parietal lobe can lead to rational preference and give most favored response towards high price stimulus. Analyzing of price perception may help to understand the differences in price-related emotions and preference, which can gain insights into an alternative pricing strategy that can lead to influence consumers buying decision.

  6. Performance evaluation for epileptic electroencephalogram (EEG) detection by using Neyman-Pearson criteria and a support vector machine

    Science.gov (United States)

    Wang, Chun-mei; Zhang, Chong-ming; Zou, Jun-zhong; Zhang, Jian

    2012-02-01

    The diagnosis of several neurological disorders is based on the detection of typical pathological patterns in electroencephalograms (EEGs). This is a time-consuming task requiring significant training and experience. A lot of effort has been devoted to developing automatic detection techniques which might help not only in accelerating this process but also in avoiding the disagreement among readers of the same record. In this work, Neyman-Pearson criteria and a support vector machine (SVM) are applied for detecting an epileptic EEG. Decision making is performed in two stages: feature extraction by computing the wavelet coefficients and the approximate entropy (ApEn) and detection by using Neyman-Pearson criteria and an SVM. Then the detection performance of the proposed method is evaluated. Simulation results demonstrate that the wavelet coefficients and the ApEn are features that represent the EEG signals well. By comparison with Neyman-Pearson criteria, an SVM applied on these features achieved higher detection accuracies.

  7. Multi-Channel Electroencephalogram (EEG) Signal Acquisition and its Effective Channel selection with De-noising Using AWICA for Biometric System

    OpenAIRE

    B.Sabarigiri; D.Suganyadevi

    2014-01-01

    the embedding of low cost electroencephalogram (EEG) sensors in wireless headsets gives improved authentication based on their brain wave signals has become a practical opportunity. In this paper signal acquisition along with effective multi-channel selection from a specific area of the brain and denoising using AWICA methods are proposed for EEG based personal identification. At this point, to develop identification system the steps are as follows. (i) the high-quality device with the least ...

  8. Changes of the Prefrontal EEG (Electroencephalogram) Activities According to the Repetition of Audio-Visual Learning.

    Science.gov (United States)

    Kim, Yong-Jin; Chang, Nam-Kee

    2001-01-01

    Investigates the changes of neuronal response according to a four time repetition of audio-visual learning. Obtains EEG data from the prefrontal (Fp1, Fp2) lobe from 20 subjects at the 8th grade level. Concludes that the habituation of neuronal response shows up in repetitive audio-visual learning and brain hemisphericity can be changed by…

  9. Electroencephalogram (EEG spectral features discriminate between Alzheimer’s (AD and Vascular dementia (VaD

    Directory of Open Access Journals (Sweden)

    Emanuel eNeto

    2015-02-01

    Full Text Available Alzheimer’s disease (AD and vascular dementia (VaD present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n=77 and VaD (n=77 and healthy elderly normal controls (NC (n=77. We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a MANCOVA with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is a useful to parameterize spectra which allowed extracting relevant clinical EEG key features that move towards simple and interpretable diagnostic criteria.

  10. Consensus on the use of neurophysiological tests in the intensive care unit (ICU): electroencephalogram (EEG), evoked potentials (EP), and electroneuromyography (ENMG)

    DEFF Research Database (Denmark)

    Guørit, J.M.; Amantini, A.; Amodio, P.

    2009-01-01

    STUDY AIM: To provide a consensus of European leading authorities about the optimal use of clinical neurophysiological (CN) tests (electroencephalogram [EEG]; evoked potentials [EP]; electroneuromyography [ENMG]) in the intensive care unit (ICU) and, particularly, about the way to make these tests...... contribution to all other experts. A complete consensus has been reached when submitting the manuscript. RESULTS: What the group considered as the best classification systems for EEG and EP abnormalities in the ICU is first presented. CN tests are useful for diagnosis (epilepsy, brain death, and neuromuscular...

  11. Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding

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    Naoyuki eSato

    2013-05-01

    Full Text Available Theta band power (4-8Hz in the scalp electroencephalogram (EEG is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course.

  12. Fast entrainment of human electroencephalogram to a theta-band photic flicker during successful memory encoding.

    Science.gov (United States)

    Sato, Naoyuki

    2013-01-01

    Theta band power (4-8 Hz) in the scalp electroencephalogram (EEG) is thought to be stronger during memory encoding for subsequently remembered items than for forgotten items. According to simultaneous EEG-functional magnetic resonance imaging (fMRI) measurements, the memory-dependent EEG theta is associated with multiple regions of the brain. This suggests that the multiple regions cooperate with EEG theta synchronization during successful memory encoding. However, a question still remains: What kind of neural dynamic organizes such a memory-dependent global network? In this study, the modulation of the EEG theta entrainment property during successful encoding was hypothesized to lead to EEG theta synchronization among a distributed network. Then, a transient response of EEG theta to a theta-band photic flicker with a short duration was evaluated during memory encoding. In the results, flicker-induced EEG power increased and decreased with a time constant of several hundred milliseconds following the onset and the offset of the flicker, respectively. Importantly, the offset response of EEG power was found to be significantly decreased during successful encoding. Moreover, the offset response of the phase locking index was also found to associate with memory performance. According to computational simulations, the results are interpreted as a smaller time constant (i.e., faster response) of a driven harmonic oscillator rather than a change in the spontaneous oscillatory input. This suggests that the fast response of EEG theta forms a global EEG theta network among memory-related regions during successful encoding, and it contributes to a flexible formation of the network along the time course.

  13. Analyzing power spectral of electroencephalogram (EEG) signal to identify motoric arm movement using EMOTIV EPOC+

    Science.gov (United States)

    Bustomi, A.; Wijaya, S. K.; Prawito

    2017-07-01

    Rehabilitation of motoric dysfunction from the body becomes the main objective of developing Brain Computer Interface (BCI) technique, especially in the field of medical rehabilitation technology. BCI technology based on electrical activity of the brain, allow patient to be able to restore motoric disfunction of the body and help them to overcome the shortcomings mobility. In this study, EEG signal phenomenon was obtained from EMOTIV EPOC+, the signals were generated from the imagery of lifting arm, and look for any correlation between the imagery of motoric muscle movement against the recorded signals. The signals processing were done in the time-frequency domain, using Wavelet relative power (WRP) as feature extraction, and Support vector machine (SVM) as the classifier. In this study, it was obtained the result of maximum accuracy of 81.3 % using 8 channel (AF3, F7, F3, FC5, FC6, F4, F8, and AF4), 6 channel remaining on EMOTIV EPOC + does not contribute to the improvement of the accuracy of the classification system

  14. Permanency analysis on human electroencephalogram signals for pervasive Brain-Computer Interface systems.

    Science.gov (United States)

    Sadeghi, Koosha; Junghyo Lee; Banerjee, Ayan; Sohankar, Javad; Gupta, Sandeep K S

    2017-07-01

    Brain-Computer Interface (BCI) systems use some permanent features of brain signals to recognize their corresponding cognitive states with high accuracy. However, these features are not perfectly permanent, and BCI system should be continuously trained over time, which is tedious and time consuming. Thus, analyzing the permanency of signal features is essential in determining how often to repeat training. In this paper, we monitor electroencephalogram (EEG) signals, and analyze their behavior through continuous and relatively long period of time. In our experiment, we record EEG signals corresponding to rest state (eyes open and closed) from one subject everyday, for three and a half months. The results show that signal features such as auto-regression coefficients remain permanent through time, while others such as power spectral density specifically in 5-7 Hz frequency band are not permanent. In addition, eyes open EEG data shows more permanency than eyes closed data.

  15. EEG: Origin and measurement

    NARCIS (Netherlands)

    Lopes da Silva, F.; Mulert, C.; Lemieux, L.

    2010-01-01

    The existence of the electrical activity of the brain (i.e. the electroencephalogram or EEG) was discovered more than a century ago by Caton. After the demonstration that the EEG could be recorded from the human scalp by Berger in the 1920s, it made a slow start before it became accepted as a method

  16. Effects of green and black tea consumption on brain wave activities in healthy volunteers as measured by a simplified Electroencephalogram (EEG): A feasibility study.

    Science.gov (United States)

    Okello, Edward J; Abadi, Awatf M; Abadi, Saad A

    2016-06-01

    Tea has been associated with many mental benefits, such as attention enhancement, clarity of mind, and relaxation. These psychosomatic states can be measured in terms of brain activity using an electroencephalogram (EEG). Brain activity can be assessed either during a state of passive activity or when performing attention tasks and it can provide useful information about the brain's state. This study investigated the effects of green and black consumption on brain activity as measured by a simplified EEG, during passive activity. Eight healthy volunteers participated in the study. The EEG measurements were performed using a two channel EEG brain mapping instrument - HeadCoach™. Fast Fourier transform algorithm and EEGLAB toolbox using the Matlab software were used for data processing and analysis. Alpha, theta, and beta wave activities were all found to increase after 1 hour of green and black tea consumption, albeit, with very considerable inter-individual variations. Our findings provide further evidence for the putative beneficial effects of tea. The highly significant increase in theta waves (P by 'from field to shelf practices'.

  17. Assessing the Quality of Steady-state Visual-evoked Potentials for Moving Humans Using a Mobile Electroencephalogram Headset

    Directory of Open Access Journals (Sweden)

    Yuan-Pin eLin

    2014-03-01

    Full Text Available Recent advances in mobile electroencephalogram (EEG systems, featuring non-prep dry electrodes and wireless telemetry, have urged the needs of mobile brain-computer interfaces (BCIs for applications in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs, which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systemati-cally explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s per hour (MPH while con-currently perceiving visual flickers (11 and 12 Hz. Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87±13.55% to walking (1 MPH: 83.03±13.24%, 2 MPH: 79.47±13.53%, and 3 MPH: 75.26±17.89%. These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications.

  18. Time reversibility of intracranial human EEG recordings in mesial temporal lobe epilepsy

    Science.gov (United States)

    van der Heyden, M. J.; Diks, C.; Pijn, J. P. M.; Velis, D. N.

    1996-02-01

    Intracranial electroencephalograms from patients suffering from mesial temporal lobe epilepsy were tested for time reversibility. If the recorded time series is irreversible, the input of the recording system cannot be a realisation of a linear Gaussian random process. We confirmed experimentally that the measurement equipment did not introduce irreversibility in the recorded output when the input was a realisation of a linear Gaussian random process. In general, the non-seizure recordings are reversible, whereas the seizure recordings are irreversible. These results suggest that time reversibility is a useful property for the characterisation of human intracranial EEG recordings in mesial temporal lobe epilepsy.

  19. Detrended Fluctuation Analysis of the Human EEG during Listening to Emotional Music

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A nonlinear method named detrended fluctuation analysis (DFA) was utilized to investigate the scaling behavior of the human electroencephalogram (EEG) in three emotional music conditions (fear, happiness, sadness) and a rest condition (eyes-closed). The results showed that the EEG exhibited scaling behavior in two regions with two scaling exponents β1 and β2 which represented the complexity of higher and lower frequency activity besides β band respectively. As the emotional intensity decreased the value of β1 increased and the value of β2 decreased. The change of β1 was weakly correlated with the 'approach-withdrawal' model of emotion and both of fear and sad music made certain differences compared with the eyes-closed rest condition. The study shows that music is a powerful elicitor of emotion and that using nonlinear method can potentially contribute to the investigation of emotion.

  20. Extracting message inter-departure time distributions from the human electroencephalogram.

    Directory of Open Access Journals (Sweden)

    Bratislav Mišić

    2011-06-01

    Full Text Available The complex connectivity of the cerebral cortex is a topic of much study, yet the link between structure and function is still unclear. The processing capacity and throughput of information at individual brain regions remains an open question and one that could potentially bridge these two aspects of neural organization. The rate at which information is emitted from different nodes in the network and how this output process changes under different external conditions are general questions that are not unique to neuroscience, but are of interest in multiple classes of telecommunication networks. In the present study we show how some of these questions may be addressed using tools from telecommunications research. An important system statistic for modeling and performance evaluation of distributed communication systems is the time between successive departures of units of information at each node in the network. We describe a method to extract and fully characterize the distribution of such inter-departure times from the resting-state electroencephalogram (EEG. We show that inter-departure times are well fitted by the two-parameter Gamma distribution. Moreover, they are not spatially or neurophysiologically trivial and instead are regionally specific and sensitive to the presence of sensory input. In both the eyes-closed and eyes-open conditions, inter-departure time distributions were more dispersed over posterior parietal channels, close to regions which are known to have the most dense structural connectivity. The biggest differences between the two conditions were observed at occipital sites, where inter-departure times were significantly more variable in the eyes-open condition. Together, these results suggest that message departure times are indicative of network traffic and capture a novel facet of neural activity.

  1. Human EEG responses to controlled alterations of the Earth's magnetic field.

    Science.gov (United States)

    Sastre, Antonio; Graham, Charles; Cook, Mary R; Gerkovich, Mary M; Gailey, Paul

    2002-09-01

    Examine the effects of controlled changes in the Earth's magnetic field on electroencephalogram (EEG) and subjective report. Fifty volunteers were exposed double-blind to changes in field magnitude, angle of inclination, and angle of deviation. Volunteers were also exposed to magnetic field conditions found near the North and South Pole. EEG recorded over temporal and occipital sites was compared across 4s baseline, field exposure, and no-change control trials. No EEG spectral differences as a function of gender or recording site were found. Geomagnetic field alterations had no effect on total energy (0.5-42 Hz), energy within traditional EEG analysis bands, or on the 95% spectral edge. Most volunteers reported no sensations; others reported non-specific symptoms unrelated to type of field change. Three hypothesized field detection mechanisms were not supported: (1) mechanical reception through torque exerted on the ferromagnetic material magnetite; (2) movement-induced induction of an electric field in the body; and (3) enhanced sensitivity due to alterations in the rates of chemical reactions involving electron spin states. Humans have little ability to detect brief alterations in the geomagnetic field, even if these alteration are of a large magnitude.

  2. Comparison of coherence and phase synchronization of the human sleep electroencephalogram

    Czech Academy of Sciences Publication Activity Database

    Mezeiová, K.; Paluš, Milan

    2012-01-01

    Roč. 123, č. 9 (2012), s. 1821-1830 ISSN 1388-2457 R&D Projects: GA MŠk 7E08027 EU Projects: European Commission(XE) 200728 - BRAINSYNC Grant - others:AV ČR - SAS(CZ-SK) Modern Methods for Analysis of Electrophysiological Signals Institutional research plan: CEZ:AV0Z10300504 Keywords : phase synchronization * complete synchronization * mean phase coherence * permutation surrogate data * coherence * human sleep EEG Subject RIV: FH - Neurology Impact factor: 3.144, year: 2012

  3. Household wireless electroencephalogram hat

    Science.gov (United States)

    Szu, Harold; Hsu, Charles; Moon, Gyu; Yamakawa, Takeshi; Tran, Binh

    2012-06-01

    We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming. Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of the measuring electrodes to create the proximity of measurement to the source effect. We computing a spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data, using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA). Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at 0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.

  4. Genetic variability in the human cannabinoid receptor 1 is associated with resting state EEG theta power in humans.

    Science.gov (United States)

    Heitland, I; Kenemans, J L; Böcker, K B E; Baas, J M P

    2014-11-01

    It has long been postulated that exogenous cannabinoids have a profound effect on human cognitive functioning. These cannabinoid effects are thought to depend, at least in parts, on alterations of phase-locking of local field potential neuronal firing. The latter can be measured as activity in the theta frequency band (4-7Hz) by electroencephalogram. Theta oscillations are supposed to serve as a mechanism in neural representations of behaviorally relevant information. However, it remains unknown whether variability in endogenous cannabinoid activity is involved in theta rhythms and therefore, may serve as an individual differences index of human cognitive functioning. To clarify this issue, we recorded resting state EEG activity in 164 healthy human subjects and extracted EEG power across frequency bands (δ, θ, α, and β). To assess variability in the endocannabinoid system, two genetic polymorphisms (rs1049353, rs2180619) within the cannabinoid receptor 1 (CB1) were determined in all participants. As expected, we observed significant effects of rs1049353 on EEG power in the theta band at frontal, central and parietal electrode regions. Crucially, these effects were specific for the theta band, with no effects on activity in the other frequency bands. Rs2180619 showed no significant associations with theta power after Bonferroni correction. Taken together, we provide novel evidence in humans showing that genetic variability in the cannabinoid receptor 1 is associated with resting state EEG power in the theta frequency band. This extends prior findings of exogenous cannabinoid effects on theta power to the endogenous cannabinoid system. Copyright © 2014 Elsevier B.V. All rights reserved.

  5. Did You Listen to the Beat? Auditory Steady-State Responses in the Human Electroencephalogram at 4 and 7 Hz Modulation Rates Reflect Selective Attention.

    Science.gov (United States)

    Jaeger, Manuela; Bleichner, Martin G; Bauer, Anna-Katharina R; Mirkovic, Bojana; Debener, Stefan

    2018-02-27

    The acoustic envelope of human speech correlates with the syllabic rate (4-8 Hz) and carries important information for intelligibility, which is typically compromised in multi-talker, noisy environments. In order to better understand the dynamics of selective auditory attention to low frequency modulated sound sources, we conducted a two-stream auditory steady-state response (ASSR) selective attention electroencephalogram (EEG) study. The two streams consisted of 4 and 7 Hz amplitude and frequency modulated sounds presented from the left and right side. One of two streams had to be attended while the other had to be ignored. The attended stream always contained a target, allowing for the behavioral confirmation of the attention manipulation. EEG ASSR power analysis revealed a significant increase in 7 Hz power for the attend compared to the ignore conditions. There was no significant difference in 4 Hz power when the 4 Hz stream had to be attended compared to when it had to be ignored. This lack of 4 Hz attention modulation could be explained by a distracting effect of a third frequency at 3 Hz (beat frequency) perceivable when the 4 and 7 Hz streams are presented simultaneously. Taken together our results show that low frequency modulations at syllabic rate are modulated by selective spatial attention. Whether attention effects act as enhancement of the attended stream or suppression of to be ignored stream may depend on how well auditory streams can be segregated.

  6. EEG

    Science.gov (United States)

    ... brain dead. EEG cannot be used to measure intelligence. Normal Results Brain electrical activity has a certain ... 2018, A.D.A.M., Inc. Duplication for commercial use must be authorized in writing by ADAM ...

  7. Graph Theoretical Analysis of BOLD Functional Connectivity during Human Sleep without EEG Monitoring.

    Directory of Open Access Journals (Sweden)

    Jun Lv

    Full Text Available Functional brain networks of human have been revealed to have small-world properties by both analyzing electroencephalogram (EEG and functional magnetic resonance imaging (fMRI time series.In our study, by using graph theoretical analysis, we attempted to investigate the changes of paralimbic-limbic cortex between wake and sleep states. Ten healthy young people were recruited to our experiment. Data from 2 subjects were excluded for the reason that they had not fallen asleep during the experiment. For each subject, blood oxygen level dependency (BOLD images were acquired to analyze brain network, and peripheral pulse signals were obtained continuously to identify if the subject was in sleep periods. Results of fMRI showed that brain networks exhibited stronger small-world characteristics during sleep state as compared to wake state, which was in consistent with previous studies using EEG synchronization. Moreover, we observed that compared with wake state, paralimbic-limbic cortex had less connectivity with neocortical system and centrencephalic structure in sleep.In conclusion, this is the first study, to our knowledge, has observed that small-world properties of brain functional networks altered when human sleeps without EEG synchronization. Moreover, we speculate that paralimbic-limbic cortex organization owns an efficient defense mechanism responsible for suppressing the external environment interference when humans sleep, which is consistent with the hypothesis that the paralimbic-limbic cortex may be functionally disconnected from brain regions which directly mediate their interactions with the external environment. Our findings also provide a reasonable explanation why stable sleep exhibits homeostasis which is far less susceptible to outside world.

  8. Individual Differences in EEG Spectral Power Reflect Genetic Variance in Gray and White Matter Volumes

    NARCIS (Netherlands)

    Smit, D.J.A.; Boomsma, D.I.; Schnack, H.G.; Hulshoff Pol, H.E.; de Geus, E.J.C.

    2012-01-01

    The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes,

  9. Increased Prevalence of Intermittent Rhythmic Delta or Theta Activity (IRDA/IRTA) in the Electroencephalograms (EEGs) of Patients with Borderline Personality Disorder

    OpenAIRE

    Tebartz van Elst, Ludger; Fleck, Max; Bartels, Susanne; Altenm?ller, Dirk-Matthias; Riedel, Andreas; Bubl, Emanuel; Matthies, Swantje; Feige, Bernd; Perlov, Evgeniy; Endres, Dominique

    2016-01-01

    Introduction: An increased prevalence of pathological electroencephalography (EEG) signals has been reported in patients with borderline personality disorder (BPD). In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA), the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormal...

  10. Increased Prevalence of Intermittent Rhythmic Delta or Theta Activity (IRDA/IRTA) in the Electroencephalograms (EEGs) of Patients with Borderline Personality Disorder.

    Science.gov (United States)

    Tebartz van Elst, Ludger; Fleck, Max; Bartels, Susanne; Altenmüller, Dirk-Matthias; Riedel, Andreas; Bubl, Emanuel; Matthies, Swantje; Feige, Bernd; Perlov, Evgeniy; Endres, Dominique

    2016-01-01

    An increased prevalence of pathological electroencephalography (EEG) signals has been reported in patients with borderline personality disorder (BPD). In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA), the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormalities in BPD patients. The aim of this retrospective study was to identify the frequency of EEG abnormalities in a carefully analyzed psychiatric collective. Following earlier reports, we hypothesized an increased prevalence of EEG abnormalities in BPD patients. We recruited 96 consecutive patients with BPD from the archive of a university clinic for psychiatry and psychotherapy, and compared the prevalence of EEG abnormalities to those of 76 healthy controls subjects. The EEGs were rated by three different blinded clinicians, including a consultant specializing in epilepsy from the local epilepsy center. We found a significant increase in the prevalence of IRDAs and IRTAs in BPD patients (14.6%) compared to the control subjects (3.9%; p = 0.020). In this blinded retrospective case-control study, we were able to confirm an increased prevalence of pathological EEG findings (IRDAs/IRTAs only) in BPD patients. The major limitation of this study is that the control group was not matched on age and gender. Therefore, the results should be regarded as preliminary findings of an open uncontrolled, retrospective study. Future research performing prospective, controlled studies is needed to verify our findings and answer the question of whether such EEG findings might predict a positive response to anticonvulsive pharmacological treatment.

  11. Increased prevalence of intermittent rhythmic delta or theta activity (IRDA/IRTA in the electroencephalograms (EEGs of patients with borderline personality disorder

    Directory of Open Access Journals (Sweden)

    Ludger eTebartz Van Elst

    2016-02-01

    Full Text Available Introduction: An increased prevalence of pathological electroencephalography (EEG signals has been reported in patients with borderline personality disorder (BPD. In an elaborative case description of such a patient with intermittent rhythmic delta and theta activity (IRDA/IRTA, the BPD symptoms where linked to the frequency of the IRDAs/IRTAs and vanished with the IRDAs/IRTAs following anticonvulsive therapy. This observation raised a question regarding the prevalence of such EEG abnormalities in BPD patients. The aim of this retrospective study was to identify the frequency of EEG abnormalities in a carefully analyzed psychiatric collective. Following earlier reports, we hypothesized an increased prevalence of EEG abnormalities in BPD patients.Participants and Methods: We recruited 96 consecutive patients with BPD from the archive of a university clinic for psychiatry and psychotherapy, and compared the prevalence of EEG abnormalities to those of 76 healthy controls subjects. The EEGs were rated by three different blinded clinicians, including a consultant specializing in epilepsy from the local epilepsy center.Results: We found a significant increase in the prevalence of IRDAs and IRTAs in BPD patients (14.6% compared to the control subjects (3.9%; p=0.020. Discussion: In this blinded retrospective case-control study, we were able to confirm an increased prevalence of pathological EEG findings (IRDAs/IRTAs only in BPD patients. The major limitation of this study is that the control group was not matched on age and gender. Therefore, the results should be regarded as preliminary findings of an open uncontrolled, retrospective study. Future research performing prospective, controlled studies is needed to verify our findings and answer the question of whether such EEG findings might predict a positive response to anticonvulsive pharmacological treatment.

  12. Random matrix analysis of human EEG data

    Czech Academy of Sciences Publication Activity Database

    Šeba, Petr

    2003-01-01

    Roč. 91, - (2003), s. 198104-1 - 198104-4 ISSN 0031-9007 R&D Projects: GA ČR GA202/02/0088 Institutional research plan: CEZ:AV0Z1010914 Keywords : random matrix theory * EEG signal Subject RIV: BE - Theoretical Physics Impact factor: 7.035, year: 2003

  13. Microneedle array electrode for human EEG recording.

    NARCIS (Netherlands)

    Lüttge, Regina; van Nieuwkasteele-Bystrova, Svetlana Nikolajevna; van Putten, Michel Johannes Antonius Maria; Vander Sloten, Jos; Verdonck, Pascal; Nyssen, Marc; Haueisen, Jens

    2009-01-01

    Microneedle array electrodes for EEG significantly reduce the mounting time, particularly by circumvention of the need for skin preparation by scrubbing. We designed a new replication process for numerous types of microneedle arrays. Here, polymer microneedle array electrodes with 64 microneedles,

  14. EEG

    African Journals Online (AJOL)

    2017-09-03

    Sep 3, 2017 ... However, very few studies have examined the use of EEG in developing countries, including Ni- ... of evoked potentials from brain neurons, referred to as .... Percentage. Gender. Male. 89. 62.7. Female. 53. 37.3. Age. 0-10. 59.

  15. The fast detection of rare auditory feature conjunctions in the human brain as revealed by cortical gamma-band electroencephalogram.

    Science.gov (United States)

    Ruusuvirta, T; Huotilainen, M

    2005-01-01

    Natural environments typically contain temporal scatters of sounds emitted from multiple sources. The sounds may often physically stand out from one another in their conjoined rather than simple features. This poses a particular challenge for the brain to detect which of these sounds are rare and, therefore, potentially important for survival. We recorded gamma-band (32-40 Hz) electroencephalographic (EEG) oscillations from the scalp of adult humans who passively listened to a repeated tone carrying frequent and rare conjunctions of its frequency and intensity. EEG oscillations that this tone induced, rather than evoked, differed in amplitude between the two conjunction types within the 56-ms analysis window from tone onset. Our finding suggests that, perhaps with the support of its non-phase-locked synchrony in the gamma band, the human brain is able to detect rare sounds as feature conjunctions very rapidly.

  16. A statistically robust EEG re-referencing procedure to mitigate reference effect

    OpenAIRE

    Lepage, Kyle Q.; Kramer, Mark A.; Chu, Catherine J.

    2014-01-01

    Background: The electroencephalogram (EEG) remains the primary tool for diagnosis of abnormal brain activity in clinical neurology and for in vivo recordings of human neurophysiology in neuroscience research. In EEG data acquisition, voltage is measured at positions on the scalp with respect to a reference electrode. When this reference electrode responds to electrical activity or artifact all electrodes are affected. Successful analysis of EEG data often involves re-referencing procedures th...

  17. Multifractal analysis of real and imaginary movements: EEG study

    Science.gov (United States)

    Pavlov, Alexey N.; Maksimenko, Vladimir A.; Runnova, Anastasiya E.; Khramova, Marina V.; Pisarchik, Alexander N.

    2018-04-01

    We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.

  18. Smartphone Household Wireless Electroencephalogram Hat

    Directory of Open Access Journals (Sweden)

    Harold Szu

    2013-01-01

    Full Text Available Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m≪N≪10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences.

  19. Dealing with noise and physiological artifacts in human EEG recordings: empirical mode methods

    Science.gov (United States)

    Runnova, Anastasiya E.; Grubov, Vadim V.; Khramova, Marina V.; Hramov, Alexander E.

    2017-04-01

    In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.

  20. An examination of the effects of various noise on physiological sensibility responses by using human EEG

    International Nuclear Information System (INIS)

    Cho, W. H.; Lee, J. K.; Son, T. Y.; Hwang, S. H.; Choi, H.; Lee, M. S.

    2013-01-01

    This study investigated human stress levels based on electroencephalogram (EEG) data and carried out a subjective evaluation analysis about noise. Visual information is very important for finding human's emotional state. And relatively more previous works have been done than those using auditory stimulus. Since there are fewer previous works, we thought that using auditory stimulus is good choice for our study. Twelve human subjects were exposed to classic piano, ocean wave, army alarm, ambulance, and mosquito noises. We used two groups of comfortable and uncomfortable noises are to see the difference between the definitely different two groups to confirm usefulness of using this setting of experiment. EEG data were collected during the experimental session. The subjects were tested in a soundproof chamber and asked to minimize blinking, head movement, and swallowing during the experiment. The total time of the noise experiment included the time of the relaxation phase, during which the subjects relaxed in silence for 10 minutes. The relaxation phase was followed by a 20 -second noise exposure. The alpha band activities of the subjects were significantly decreased for the ambulance and mosquito noises, as it compared to the classic piano and ocean wave noises. The alpha band activities of the subjects decreased by 12.8 ± 2.3% for the ocean wave noise, decreased by 32.0 ± 5.4% for the army alarm noise, decreased by 34.5 ± 6.7% for the ambulance noise and decreased by 58.3 ± 9.1% for the mosquito noise compared to that of classic piano. On the other hand, their beta band activities were significantly increased for the ambulance and mosquito noises as it compared to classic piano and ocean wave. The beta band activities of the subjects increased by 7.9 ± 1.7% for the ocean wave noise, increased by 20.6 ± 5.3% for the army alarm noise, increased by 48.0 ± 7.5% for the ambulance noise and increased by 61.9 ± 11.2% for the mosquito noise, as it is compared to

  1. An examination of the effects of various noise on physiological sensibility responses by using human EEG

    Energy Technology Data Exchange (ETDEWEB)

    Cho, W. H.; Lee, J. K.; Son, T. Y.; Hwang, S. H.; Choi, H. [Sungkyunkwan University, Suwon (Korea, Republic of); Lee, M. S. [Hyundai Motor Company, Hwaseong (Korea, Republic of)

    2013-12-15

    This study investigated human stress levels based on electroencephalogram (EEG) data and carried out a subjective evaluation analysis about noise. Visual information is very important for finding human's emotional state. And relatively more previous works have been done than those using auditory stimulus. Since there are fewer previous works, we thought that using auditory stimulus is good choice for our study. Twelve human subjects were exposed to classic piano, ocean wave, army alarm, ambulance, and mosquito noises. We used two groups of comfortable and uncomfortable noises are to see the difference between the definitely different two groups to confirm usefulness of using this setting of experiment. EEG data were collected during the experimental session. The subjects were tested in a soundproof chamber and asked to minimize blinking, head movement, and swallowing during the experiment. The total time of the noise experiment included the time of the relaxation phase, during which the subjects relaxed in silence for 10 minutes. The relaxation phase was followed by a 20 -second noise exposure. The alpha band activities of the subjects were significantly decreased for the ambulance and mosquito noises, as it compared to the classic piano and ocean wave noises. The alpha band activities of the subjects decreased by 12.8 ± 2.3% for the ocean wave noise, decreased by 32.0 ± 5.4% for the army alarm noise, decreased by 34.5 ± 6.7% for the ambulance noise and decreased by 58.3 ± 9.1% for the mosquito noise compared to that of classic piano. On the other hand, their beta band activities were significantly increased for the ambulance and mosquito noises as it compared to classic piano and ocean wave. The beta band activities of the subjects increased by 7.9 ± 1.7% for the ocean wave noise, increased by 20.6 ± 5.3% for the army alarm noise, increased by 48.0 ± 7.5% for the ambulance noise and increased by 61.9 ± 11.2% for the mosquito noise, as it is compared

  2. Early effect of NEURAPAS® balance on current source density (CSD of human EEG

    Directory of Open Access Journals (Sweden)

    Koch Klaus

    2011-08-01

    Full Text Available Abstract Psychiatric patients often suffer from stress, anxiety and depression. Various plant extracts are known to fight stress (valerian, anxiety (passion flower or depression (St. John's wort. NEURAPAS® balance is a mixture of these three extracts and has been designed to cover this complex of psychiatric conditions. The study was initiated to quantitatively assess the effect of this combination on brain electric activity. Method Quantitative electroencephalogram (EEG current source density (CSD recording from 16 healthy male and female human volunteers (average age 49 years was used in a randomized, placebo-controlled cross over study. Recordings were performed 0. 5, 1. 5, 3 and 4 hours after administration of the preparations under the conditions of 6 min eyes open and 5 min d2 concentration test, mathematical calculation test and memory test, respectively. All variables (electric power within 6 frequency ranges at 17 electrode positions were fed into a linear discriminant analysis (eyes open condition. In the presence of mental load these variables were used to construct brain maps of frequency changes. Results Under the condition of mental load, centro-parietal spectral power remained statistically significantly lower within alpha1, alpha2 and beta1 frequencies in the presence of verum in comparison to placebo. Discriminant analysis revealed a difference to placebo 3 and 4 hours after intake of 6 tablets of NEURAPAS® balance. Data location within the polydimensional space was projected into the area of the effects of sedative and anti-depressive reference drugs tested earlier under identical conditions. Results appeared closer to the effects of fluoxetine than to St. John's wort. Conclusions Analysis of the neurophysiological changes following the intake of NEURAPAS® balance revealed a similarity of frequency changes to those of calming and anti-depressive drugs on the EEG without impairment of cognition. Trial registration Clinical

  3. Filtration of human EEG recordings from physiological artifacts with empirical mode method

    Science.gov (United States)

    Grubov, Vadim V.; Runnova, Anastasiya E.; Khramova, Marina V.

    2017-03-01

    In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.

  4. EEG analyses with SOBI.

    Energy Technology Data Exchange (ETDEWEB)

    Glickman, Matthew R.; Tang, Akaysha (University of New Mexico, Albuquerque, NM)

    2009-02-01

    The motivating vision behind Sandia's MENTOR/PAL LDRD project has been that of systems which use real-time psychophysiological data to support and enhance human performance, both individually and of groups. Relevant and significant psychophysiological data being a necessary prerequisite to such systems, this LDRD has focused on identifying and refining such signals. The project has focused in particular on EEG (electroencephalogram) data as a promising candidate signal because it (potentially) provides a broad window on brain activity with relatively low cost and logistical constraints. We report here on two analyses performed on EEG data collected in this project using the SOBI (Second Order Blind Identification) algorithm to identify two independent sources of brain activity: one in the frontal lobe and one in the occipital. The first study looks at directional influences between the two components, while the second study looks at inferring gender based upon the frontal component.

  5. Naive scoring of human sleep based on a hidden Markov model of the electroencephalogram.

    Science.gov (United States)

    Yaghouby, Farid; Modur, Pradeep; Sunderam, Sridhar

    2014-01-01

    Clinical sleep scoring involves tedious visual review of overnight polysomnograms by a human expert. Many attempts have been made to automate the process by training computer algorithms such as support vector machines and hidden Markov models (HMMs) to replicate human scoring. Such supervised classifiers are typically trained on scored data and then validated on scored out-of-sample data. Here we describe a methodology based on HMMs for scoring an overnight sleep recording without the benefit of a trained initial model. The number of states in the data is not known a priori and is optimized using a Bayes information criterion. When tested on a 22-subject database, this unsupervised classifier agreed well with human scores (mean of Cohen's kappa > 0.7). The HMM also outperformed other unsupervised classifiers (Gaussian mixture models, k-means, and linkage trees), that are capable of naive classification but do not model dynamics, by a significant margin (p < 0.05).

  6. Research on the relation of EEG signal chaos characteristics with high-level intelligence activity of human brain.

    Science.gov (United States)

    Wang, Xingyuan; Meng, Juan; Tan, Guilin; Zou, Lixian

    2010-04-27

    Using phase space reconstruct technique from one-dimensional and multi-dimensional time series and the quantitative criterion rule of system chaos, and combining the neural network; analyses, computations and sort are conducted on electroencephalogram (EEG) signals of five kinds of human consciousness activities (relaxation, mental arithmetic of multiplication, mental composition of a letter, visualizing a 3-dimensional object being revolved about an axis, and visualizing numbers being written or erased on a blackboard). Through comparative studies on the determinacy, the phase graph, the power spectra, the approximate entropy, the correlation dimension and the Lyapunov exponent of EEG signals of 5 kinds of consciousness activities, the following conclusions are shown: (1) The statistic results of the deterministic computation indicate that chaos characteristic may lie in human consciousness activities, and central tendency measure (CTM) is consistent with phase graph, so it can be used as a division way of EEG attractor. (2) The analyses of power spectra show that ideology of single subject is almost identical but the frequency channels of different consciousness activities have slight difference. (3) The approximate entropy between different subjects exist discrepancy. Under the same conditions, the larger the approximate entropy of subject is, the better the subject's innovation is. (4) The results of the correlation dimension and the Lyapunov exponent indicate that activities of human brain exist in attractors with fractional dimensions. (5) Nonlinear quantitative criterion rule, which unites the neural network, can classify different kinds of consciousness activities well. In this paper, the results of classification indicate that the consciousness activity of arithmetic has better differentiation degree than that of abstract.

  7. EEG-based characterization of flicker perception

    OpenAIRE

    Lazo, M.; Tsoneva, T.; Garcia Molina, G.

    2013-01-01

    Steady-State Visual Evoked Potential (SSVEP) is an oscillatory electrical response appearing in the electroencephalogram (EEG) in response to flicker stimulation. The SSVEP manifests more prominently in electrodes located near the visual cortex and has oscillatory components at the stimulation frequency and/or harmonics. The phase and amplitude of the SSVEP are sensitive to stimulus parameters such as frequency, modu-lation depth, and spatial frequency. Research related to SSVEP and the human...

  8. Sleep in the human hippocampus: a stereo-EEG study.

    Directory of Open Access Journals (Sweden)

    Fabio Moroni

    Full Text Available BACKGROUND: There is compelling evidence indicating that sleep plays a crucial role in the consolidation of new declarative, hippocampus-dependent memories. Given the increasing interest in the spatiotemporal relationships between cortical and hippocampal activity during sleep, this study aimed to shed more light on the basic features of human sleep in the hippocampus. METHODOLOGY/PRINCIPAL FINDINGS: We recorded intracerebral stereo-EEG directly from the hippocampus and neocortical sites in five epileptic patients undergoing presurgical evaluations. The time course of classical EEG frequency bands during the first three NREM-REM sleep cycles of the night was evaluated. We found that delta power shows, also in the hippocampus, the progressive decrease across sleep cycles, indicating that a form of homeostatic regulation of delta activity is present also in this subcortical structure. Hippocampal sleep was also characterized by: i a lower relative power in the slow oscillation range during NREM sleep compared to the scalp EEG; ii a flattening of the time course of the very low frequencies (up to 1 Hz across sleep cycles, with relatively high levels of power even during REM sleep; iii a decrease of power in the beta band during REM sleep, at odds with the typical increase of power in the cortical recordings. CONCLUSIONS/SIGNIFICANCE: Our data imply that cortical slow oscillation is attenuated in the hippocampal structures during NREM sleep. The most peculiar feature of hippocampal sleep is the increased synchronization of the EEG rhythms during REM periods. This state of resonance may have a supportive role for the processing/consolidation of memory.

  9. Radiotelemetry recording of electroencephalogram in piglets during rest.

    Science.gov (United States)

    Saito, Toshiyuki; Watanabe, Yasuko; Nemoto, Tetsu; Kasuya, Etsuko; Sakumoto, Ryosuke

    2005-04-13

    A wireless recording system was developed to study the electroencephalogram (EEG) in unrestrained, male Landrace piglets. Under general anesthesia, ball-tipped silver/silver chloride electrodes for EEG recording were implanted onto the dura matter of the parietal and frontal cortex of the piglets. A pair of miniature preamplifiers and transmitters was then mounted on the surface of the skull. To examine whether other bioelectrical activities interfere with the EEG measurements, an electrocardiogram (ECG) or electromyogram (EMG) of the neck was simultaneously recorded with the EEG. Next, wire electrodes for recording movement of the eyelid were implanted with EEG electrodes, and EEG and eyelid movements were simultaneously measured. Power spectral analysis using a Fast Fourier Transformation (FFT) algorithm indicates that EEG was successfully recorded in unrestrained piglets, at rest, during the daytime in the absence of interference from ECG, EMG or eyelid movements. These data indicate the feasibility of using our radiotelemetry system for measurement of EEG under these conditions.

  10. Demonstration of brain noise on human EEG signals in perception of bistable images

    Science.gov (United States)

    Grubov, Vadim V.; Runnova, Anastasiya E.; Kurovskaya, Maria K.; Pavlov, Alexey N.; Koronovskii, Alexey A.; Hramov, Alexander E.

    2016-03-01

    In this report we studied human brain activity in the case of bistable visual perception. We proposed a new approach for quantitative characterization of this activity based on analysis of EEG oscillatory patterns and evoked potentials. Accordingly to theoretical background, obtained experimental EEG data and results of its analysis we studied a characteristics of brain activity during decision-making. Also we have shown that decisionmaking process has the special patterns on the EEG data.

  11. Measurement of neurovascular coupling in human motor cortex using simultaneous transcranial doppler (TCD) and electroencephalography (EEG).

    Science.gov (United States)

    Alam, Monzurul; Ahmed, Ghazanfar; Ling, Yan To; Zheng, Yong-Ping

    2018-05-25

    Event-related desynchronization (ERD) is a relative power decrease of electroencephalogram (EEG) signals in a specific frequency band during physical motor execution, while transcranial Doppler (TCD) measures cerebral blood flow velocity. The objective of this study was to investigate the neurovascular coupling in the motor cortex by using an integrated EEG and TCD system, and to find any difference in hemodynamic responses in healthy young male and female adults. Approach: 30 healthy volunteers, aged 20-30 years were recruited for this study. The subjects were asked to perform a motor task for the duration of a provided visual cue. Simultaneous EEG and TCD recording was carried out using a new integrated system to detect the ERD arising from the EEG signals, and to measure the mean blood flow velocity of the left and right middle cerebral arteries from bilateral TCD signals. Main Results: The results showed a significant decrease in EEG power in mu band (7.5-12.5 Hz) during the motor task compared to the resting phase. It showed significant increase in desynchronization on the contralateral side of the motor task compared to the ipsilateral side. Mean blood flow velocity during the task phase was significantly higher in comparison with the resting phase at the contralateral side. The results also showed a significantly higher increase in the percentage of mean blood flow velocity in the contralateral side of motor task compared to the ipsilateral side. However, no significant difference in desynchronization, or change of mean blood flow velocity was found between males and females. Significance: A combined TCD-EEG system successfully detects ERD and blood flow velocity in cerebral arteries, and can be used as a useful tool to study neurovascular coupling in the brain. There is no significant difference in the hemodynamic responses in healthy young males and females. © 2018 Institute of Physics and Engineering in Medicine.

  12. Controller design for Robotic hand through Electroencephalogram

    OpenAIRE

    Pandelidis P.; Kiriazis N.; Orgianelis K.; Koulios N.

    2016-01-01

    - This paper deals with the designing, the construction and the control of a robotic hand via an electroencephalogram sensor. First a robotic device that is able to mimic a real human hand is constructed. A PID controller is designed in order to improve the performance of the robotic arm for grabbing objects. Furthermore, a novel design approach is presented for controlling the motion of the robotic arm using signals produced from an innovative electroencephalogram sensor that detects the con...

  13. Decoding natural reach-and-grasp actions from human EEG

    Science.gov (United States)

    Schwarz, Andreas; Ofner, Patrick; Pereira, Joana; Ioana Sburlea, Andreea; Müller-Putz, Gernot R.

    2018-02-01

    Objective. Despite the high number of degrees of freedom of the human hand, most actions of daily life can be executed incorporating only palmar, pincer and lateral grasp. In this study we attempt to discriminate these three different executed reach-and-grasp actions utilizing their EEG neural correlates. Approach. In a cue-guided experiment, 15 healthy individuals were asked to perform these actions using daily life objects. We recorded 72 trials for each reach-and-grasp condition and from a no-movement condition. Main results. Using low-frequency time domain features from 0.3 to 3 Hz, we achieved binary classification accuracies of 72.4%, STD  ±  5.8% between grasp types, for grasps versus no-movement condition peak performances of 93.5%, STD  ±  4.6% could be reached. In an offline multiclass classification scenario which incorporated not only all reach-and-grasp actions but also the no-movement condition, the highest performance could be reached using a window of 1000 ms for feature extraction. Classification performance peaked at 65.9%, STD  ±  8.1%. Underlying neural correlates of the reach-and-grasp actions, investigated over the primary motor cortex, showed significant differences starting from approximately 800 ms to 1200 ms after the movement onset which is also the same time frame where classification performance reached its maximum. Significance. We could show that it is possible to discriminate three executed reach-and-grasp actions prominent in people’s everyday use from non-invasive EEG. Underlying neural correlates showed significant differences between all tested conditions. These findings will eventually contribute to our attempt of controlling a neuroprosthesis in a natural and intuitive way, which could ultimately benefit motor impaired end users in their daily life actions.

  14. [DESCRIPTION AND PRESENTATION OF THE RESULTS OF ELECTROENCEPHALOGRAM PROCESSING USING AN INFORMATION MODEL].

    Science.gov (United States)

    Myznikov, I L; Nabokov, N L; Rogovanov, D Yu; Khankevich, Yu R

    2016-01-01

    The paper proposes to apply the informational modeling of correlation matrix developed by I.L. Myznikov in early 1990s in neurophysiological investigations, such as electroencephalogram recording and analysis, coherence description of signals from electrodes on the head surface. The authors demonstrate information models built using the data from studies of inert gas inhalation by healthy human subjects. In the opinion of the authors, information models provide an opportunity to describe physiological processes with a high level of generalization. The procedure of presenting the EEG results holds great promise for the broad application.

  15. Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

    Science.gov (United States)

    Antoniades, Andreas; Spyrou, Loukianos; Martin-Lopez, David; Valentin, Antonio; Alarcon, Gonzalo; Sanei, Saeid; Took, Clive Cheong

    2018-03-19

    Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.

  16. Signal Quality Evaluation of Emerging EEG Devices

    Directory of Open Access Journals (Sweden)

    Thea Radüntz

    2018-02-01

    Full Text Available Electroencephalogram (EEG registration as a direct measure of brain activity has unique potentials. It is one of the most reliable and predicative indicators when studying human cognition, evaluating a subject's health condition, or monitoring their mental state. Unfortunately, standard signal acquisition procedures limit the usability of EEG devices and narrow their application outside the lab. Emerging sensor technology allows gel-free EEG registration and wireless signal transmission. Thus, it enables quick and easy application of EEG devices by users themselves. Although a main requirement for the interpretation of an EEG is good signal quality, there is a lack of research on this topic in relation to new devices. In our work, we compared the signal quality of six very different EEG devices. On six consecutive days, 24 subjects wore each device for 60 min and completed tasks and games on the computer. The registered signals were evaluated in the time and frequency domains. In the time domain, we examined the percentage of artifact-contaminated EEG segments and the signal-to-noise ratios. In the frequency domain, we focused on the band power variation in relation to task demands. The results indicated that the signal quality of a mobile, gel-based EEG system could not be surpassed by that of a gel-free system. However, some of the mobile dry-electrode devices offered signals that were almost comparable and were very promising. This study provided a differentiated view of the signal quality of emerging mobile and gel-free EEG recording technology and allowed an assessment of the functionality of the new devices. Hence, it provided a crucial prerequisite for their general application, while simultaneously supporting their further development.

  17. Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.

    Science.gov (United States)

    Cho, Sung Pil; Song, Mi Hye; Park, Young Cheol; Choi, Ho Seon; Lee, Kyoung Joung

    2007-01-01

    A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

  18. Adenosine A1 receptors in human sleep regulation studied by electroencephalography (EEG) and positron emission tomography (PET)

    International Nuclear Information System (INIS)

    Geissler, E.

    2007-01-01

    Sleep is an essential physiological process. However, the functions of sleep and the endogenous mechanisms involved in sleep regulation are only partially understood. Convergent lines of evidence support the hypothesis that the build-up of sleep propensity during wakefulness and its decline during sleep are associated with alterations in brain adenosine levels and adenosine receptor concentrations. The non-selective A 1 and A 2A adenosine receptor antagonist caffeine stimulates alertness and is known to attenuate changes in the waking and sleep electroencephalogram (EEG) typically observed after prolonged waking. Several findings point to an important function of the adenosine A 1 receptor (A 1 AR) in the modulation of vigilance states. The A 1 AR is densely expressed in brain regions involved in sleep regulation, and pharmacological manipulations affecting the A 1 AR were shown to influence sleep propensity and sleep depth. However, an involvement of the A 2A adenosine receptor (A 2A AR) is also assumed. The distinct functions of the A 1 and A 2A receptor subtypes in sleep-wake regulation and in mediating the effects of caffeine have not been identified so far. The selective adenosine A 1 receptor antagonist, 8-cyclopentyl-3-(3- 18 Ffluoropropyl)- 1-propylxanthine ( 18 F-CPFPX), offers the opportunity to get further insights into adenosinergic mechanisms by in vivo imaging of the A 1 AR subtype with positron emission tomography (PET). The aim of this thesis was to elucidate the role of adenosine A 1 receptors in human sleep regulation, combining 18 F-CPFPX PET brain imaging and EEG recordings, the gold standard in sleep research. It was hypothesized that sleep deprivation would induce adenosine accumulation and/or changes in A 1 AR density. Thus, the question was addressed whether these effects of prolonged wakefulness can be visualized by altered 18 F-CPFPX binding. Moreover, it was investigated whether radioligand uptake might be influenced by caffeine, since

  19. Controller design for Robotic hand through Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Pandelidis P.

    2016-01-01

    Full Text Available - This paper deals with the designing, the construction and the control of a robotic hand via an electroencephalogram sensor. First a robotic device that is able to mimic a real human hand is constructed. A PID controller is designed in order to improve the performance of the robotic arm for grabbing objects. Furthermore, a novel design approach is presented for controlling the motion of the robotic arm using signals produced from an innovative electroencephalogram sensor that detects the concentration of the brain

  20. Three Experiments Examining the Use of Electroencephalogram,Event-Related Potentials, and Heart-Rate Variability for Real-Time Human-Centered Adaptive Automation Design

    Science.gov (United States)

    Prinzel, Lawrence J., III; Parasuraman, Raja; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.; Pope, Alan T.

    2003-01-01

    Adaptive automation represents an advanced form of human-centered automation design. The approach to automation provides for real-time and model-based assessments of human-automation interaction, determines whether the human has entered into a hazardous state of awareness and then modulates the task environment to keep the operator in-the-loop , while maintaining an optimal state of task engagement and mental alertness. Because adaptive automation has not matured, numerous challenges remain, including what the criteria are, for determining when adaptive aiding and adaptive function allocation should take place. Human factors experts in the area have suggested a number of measures including the use of psychophysiology. This NASA Technical Paper reports on three experiments that examined the psychophysiological measures of event-related potentials, electroencephalogram, and heart-rate variability for real-time adaptive automation. The results of the experiments confirm the efficacy of these measures for use in both a developmental and operational role for adaptive automation design. The implications of these results and future directions for psychophysiology and human-centered automation design are discussed.

  1. Simultaneous trimodal MR-PET-EEG imaging for the investigation of resting state networks in humans

    Energy Technology Data Exchange (ETDEWEB)

    Neuner, Irene [RWTH Aachen (Germany); Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH (Germany); Mauler, Joerg; Arrubla, Jorge; Kops, Elena Rota; Tellmann, Lutz; Scheins, Jurgen; Herzog, Hans [Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH (Germany); Langen, Karl Josef; Shah, Jon [RWTH Aachen (Germany)

    2015-05-18

    Glucose is the principal source of energy for the brain and its relationship to neuronal activity are poorly understood. The human brain uses 80% of its energy for ongoing neural activity that occurs in isolation from any particular stimulus. A promising tool for the investigation of glucose metabolism and its relationship to neuronal activity is simultaneous trimodal MR-PET-EEG data imaging. We here demonstrate the first in vivo human trimodal data at 3T. In one session MR, FDG-PET and EEG data were recorded simultaneously at a 3T hybrid MR-BrainPET scanner (Siemens, Germany) equipped with a 32 channel MR-compatible EEG system (Brain Products, Germany) in 11 healthy volunteers (11 males, mean age: 25.2 years SD: 1.2). MR and EEG data acquisition MP-RAGE (TR = 2250 ms, TE= 3.03 ms, 176 sagittal slices. 1 mm, GRAPPA factor 2. MR-based attenuation correction of PET data via UTE: flip angle=15. Two different echo times TE1=0.07 and TE2=2.46 ms, TR=200 ms. EPI sequence (TR: 2.2 s, TE: 30 ms, FOV: 200 mm, 165 volumes, The subjects were requested to close their eyes and relax EEG data were recorded using a 32-channel MR compatible EEG system. App. 200 MBq/μmol FDG were injected, data were acquired in list mode and iteratively reconstructed with all necessary corrections into 153 slices with 256 x 256 voxels sized 1.25 mm{sup 3}. The trimodal approach, recording PET data, MR data and EEG data simultaneously was successful. The high neuronal activity of the structures within the default mode network occurs on the basis of a high glucose consumption rate within the default node network. The activity of the default mode is not tied to a special EEG frequency band.

  2. Simultaneous trimodal MR-PET-EEG imaging for the investigation of resting state networks in humans

    International Nuclear Information System (INIS)

    Neuner, Irene; Mauler, Joerg; Arrubla, Jorge; Kops, Elena Rota; Tellmann, Lutz; Scheins, Jurgen; Herzog, Hans; Langen, Karl Josef; Shah, Jon

    2015-01-01

    Glucose is the principal source of energy for the brain and its relationship to neuronal activity are poorly understood. The human brain uses 80% of its energy for ongoing neural activity that occurs in isolation from any particular stimulus. A promising tool for the investigation of glucose metabolism and its relationship to neuronal activity is simultaneous trimodal MR-PET-EEG data imaging. We here demonstrate the first in vivo human trimodal data at 3T. In one session MR, FDG-PET and EEG data were recorded simultaneously at a 3T hybrid MR-BrainPET scanner (Siemens, Germany) equipped with a 32 channel MR-compatible EEG system (Brain Products, Germany) in 11 healthy volunteers (11 males, mean age: 25.2 years SD: 1.2). MR and EEG data acquisition MP-RAGE (TR = 2250 ms, TE= 3.03 ms, 176 sagittal slices. 1 mm, GRAPPA factor 2. MR-based attenuation correction of PET data via UTE: flip angle=15. Two different echo times TE1=0.07 and TE2=2.46 ms, TR=200 ms. EPI sequence (TR: 2.2 s, TE: 30 ms, FOV: 200 mm, 165 volumes, The subjects were requested to close their eyes and relax EEG data were recorded using a 32-channel MR compatible EEG system. App. 200 MBq/μmol FDG were injected, data were acquired in list mode and iteratively reconstructed with all necessary corrections into 153 slices with 256 x 256 voxels sized 1.25 mm 3 . The trimodal approach, recording PET data, MR data and EEG data simultaneously was successful. The high neuronal activity of the structures within the default mode network occurs on the basis of a high glucose consumption rate within the default node network. The activity of the default mode is not tied to a special EEG frequency band.

  3. The relation between electroencephalogram asymmetry and attention biases to threat at baseline and under stress

    OpenAIRE

    Pérez-Edgar, Koraly; Kujawa, Autumn; Nelson, S. Katherine; Cole, Claire; Zapp, Daniel J.

    2013-01-01

    Electroencephalogram (EEG) asymmetry in the alpha frequency band has been implicated in emotion processing and broad approach-withdrawal motivation systems. Questions remain regarding the cognitive mechanisms that may help elucidate the observed links between EEG asymmetry and patterns of socioemotional functioning. The current study observed frontal EEG asymmetry patterns at rest and under social threat among young adults (N=45, M=21.1 years). Asymmetries were, in turn, associated with perfo...

  4. Comparing the effect of hypercapnia and hypoxia on the electroencephalogram during wakefulness.

    Science.gov (United States)

    Wang, David; Yee, Brendon J; Wong, Keith K; Kim, Jong Won; Dijk, Derk-Jan; Duffin, James; Grunstein, Ronald R

    2015-01-01

    Hypoxia has been postulated as a key mechanism for neurocognitive impairment in sleep-disordered breathing. However, the effect of hypoxia on the electroencephalogram (EEG) is not clear. We examined quantitative EEG recordings from 20 normal volunteers under three 5-min ventilatory control protocols: progressive hypercapnia with iso-hyperoxia (pO2=150mmHg) (Protocol 1), progressive hypercapnia with iso-hypoxia (pO2=50mmHg) (Protocol 2), and progressive hypoxia with a CO2 scrubber in the circuit (Protocol 3). Each protocol started with a 5-min session of breathing room air as baseline. In Protocol 1, compared to its baseline, iso-hyperoxia hypercapnia led to a lower Alpha% and higher Delta/Alpha (D/A) ratio. Similarly, in Protocol 2, the iso-hypoxia hypercapnia induced a higher Delta%, a lower Alpha% and higher D/A ratio. No difference was found in any EEG spectral band including the D/A ratio when Protocols 1 & 2 were compared. In Protocol 3, the Delta%, Alpha% and D/A ratio recorded during hypoxia were not significantly different from baseline. We found that hypercapnia, but not hypoxia, may play a key role in slowing of the EEG in healthy humans. Hypercapnia may be a greater influence than hypoxia on brain neuroelectrical activities. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  5. [French guidelines on electroencephalogram].

    Science.gov (United States)

    André-Obadia, N; Sauleau, P; Cheliout-Heraut, F; Convers, P; Debs, R; Eisermann, M; Gavaret, M; Isnard, J; Jung, J; Kaminska, A; Kubis, N; Lemesle, M; Maillard, L; Mazzola, L; Michel, V; Montavont, A; N'Guyen, S; Navarro, V; Parain, D; Perin, B; Rosenberg, S D; Sediri, H; Soufflet, C; Szurhaj, W; Taussig, D; Touzery-de Villepin, A; Vercueil, L; Lamblin, M D

    2014-12-01

    Electroencephalography allows the functional analysis of electrical brain cortical activity and is the gold standard for analyzing electrophysiological processes involved in epilepsy but also in several other dysfunctions of the central nervous system. Morphological imaging yields complementary data, yet it cannot replace the essential functional analysis tool that is EEG. Furthermore, EEG has the great advantage of being non-invasive, easy to perform and allows control tests when follow-up is necessary, even at the patient's bedside. Faced with the advances in knowledge, techniques and indications, the Société de Neurophysiologie Clinique de Langue Française (SNCLF) and the Ligue Française Contre l'Épilepsie (LFCE) found it necessary to provide an update on EEG recommendations. This article will review the methodology applied to this work, refine the various topics detailed in the following chapters. It will go over the summary of recommendations for each of these chapters and underline proposals for writing an EEG report. Some questions could not be answered by the review of the literature; in those cases, an expert advice was given by the working and reading groups in addition to the guidelines. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

  6. Developmental changes of BOLD signal correlations with global human EEG power and synchronization during working memory.

    Directory of Open Access Journals (Sweden)

    Lars Michels

    Full Text Available In humans, theta band (5-7 Hz power typically increases when performing cognitively demanding working memory (WM tasks, and simultaneous EEG-fMRI recordings have revealed an inverse relationship between theta power and the BOLD (blood oxygen level dependent signal in the default mode network during WM. However, synchronization also plays a fundamental role in cognitive processing, and the level of theta and higher frequency band synchronization is modulated during WM. Yet, little is known about the link between BOLD, EEG power, and EEG synchronization during WM, and how these measures develop with human brain maturation or relate to behavioral changes. We examined EEG-BOLD signal correlations from 18 young adults and 15 school-aged children for age-dependent effects during a load-modulated Sternberg WM task. Frontal load (in-dependent EEG theta power was significantly enhanced in children compared to adults, while adults showed stronger fMRI load effects. Children demonstrated a stronger negative correlation between global theta power and the BOLD signal in the default mode network relative to adults. Therefore, we conclude that theta power mediates the suppression of a task-irrelevant network. We further conclude that children suppress this network even more than adults, probably from an increased level of task-preparedness to compensate for not fully mature cognitive functions, reflected in lower response accuracy and increased reaction time. In contrast to power, correlations between instantaneous theta global field synchronization and the BOLD signal were exclusively positive in both age groups but only significant in adults in the frontal-parietal and posterior cingulate cortices. Furthermore, theta synchronization was weaker in children and was--in contrast to EEG power--positively correlated with response accuracy in both age groups. In summary we conclude that theta EEG-BOLD signal correlations differ between spectral power and

  7. Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

    Directory of Open Access Journals (Sweden)

    Cauchy Pradhan

    2012-01-01

    Full Text Available The fundamental nature of the brain's electrical activities recorded as electroencephalogram (EEG remains unknown. Linear stochastic models and spectral estimates are the most common methods for the analysis of EEG because of their robustness, simplicity of interpretation, and apparent association with rhythmic behavioral patterns in nature. In this paper, we extend the use of higher-order spectrum in order to indicate the hidden characteristics of EEG signals that simply do not arise from random processes. The higher-order spectrum is an extension Fourier spectrum that uses higher moments for spectral estimates. This essentially nullifies all Gaussian random effects, therefore, can reveal non-Gaussian and nonlinear characteristics in the complex patterns of EEG time series. The paper demonstrates the distinguishing features of bispectral analysis for chaotic systems, filtered noises, and normal background EEG activity. The bispectrum analysis detects nonlinear interactions; however, it does not quantify the coupling strength. The squared bicoherence in the nonredundant region has been estimated to demonstrate nonlinear coupling. The bicoherence values are minimal for white Gaussian noises (WGNs and filtered noises. Higher bicoherence values in chaotic time series and normal background EEG activities are indicative of nonlinear coupling in these systems. The paper shows utility of bispectral methods as an analytical tool in understanding neural process underlying human EEG patterns.

  8. Recording human cortical population spikes non-invasively--An EEG tutorial.

    Science.gov (United States)

    Waterstraat, Gunnar; Fedele, Tommaso; Burghoff, Martin; Scheer, Hans-Jürgen; Curio, Gabriel

    2015-07-30

    Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis. The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online. Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG. sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Decoding Individual Finger Movements from One Hand Using Human EEG Signals

    Science.gov (United States)

    Gonzalez, Jania; Ding, Lei

    2014-01-01

    Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (pEEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies. PMID:24416360

  10. High-Frequency EEG Variations in Children with Autism Spectrum Disorder during Human Faces Visualization

    Directory of Open Access Journals (Sweden)

    Celina A. Reis Paula

    2017-01-01

    Full Text Available Autism spectrum disorder (ASD is a neuropsychiatric disorder characterized by the impairment in the social reciprocity, interaction/language, and behavior, with stereotypes and signs of sensory function deficits. Electroencephalography (EEG is a well-established and noninvasive tool for neurophysiological characterization and monitoring of the brain electrical activity, able to identify abnormalities related to frequency range, connectivity, and lateralization of brain functions. This research aims to evidence quantitative differences in the frequency spectrum pattern between EEG signals of children with and without ASD during visualization of human faces in three different expressions: neutral, happy, and angry. Quantitative clinical evaluations, neuropsychological evaluation, and EEG of children with and without ASD were analyzed paired by age and gender. The results showed stronger activation in higher frequencies (above 30 Hz in frontal, central, parietal, and occipital regions in the ASD group. This pattern of activation may correlate with developmental characteristics in the children with ASD.

  11. Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

    Directory of Open Access Journals (Sweden)

    Psyche eLoui

    2014-10-01

    Full Text Available Sonification refers to a process by which data are converted into sound, providing an auditory alternative to visual display. Currently, the prevalent method for diagnosing seizures in epilepsy is by visually reading a patient’s electroencephalogram (EEG. However, sonification of the EEG data provides certain advantages due to the nature of human auditory perception. We hypothesized that human listeners will be able to identify seizures from EEGs using the auditory modality alone, and that accuracy of seizure identification will increase after a short training session. Here we describe an algorithm we have used to sonify EEGs of both seizure and non-seizure activity, followed by a training study in which subjects listened to short clips of sonified EEGs and determine whether each clip was of seizure or normal activity, both before and after a short training session. Results show that before training subjects performed at chance level in differentiating seizures vs. non-seizures, but there was a significant improvement of accuracy after the training session. After training, subjects successfully distinguished seizures from non-seizures using the auditory modality alone. Further analyses using signal detection theory demonstrated improvement in sensitivity and reduction in response bias as a result of training. This study demonstrates the potential of sonified EEGs to be used for the detection of seizures. Future studies will attempt to increase accuracy using novel training and sonification modifications, with the goals of managing, predicting, and ultimately controlling seizures using sonification as a possible biofeedback-based intervention for epilepsy.

  12. Chaos based encryption system for encrypting electroencephalogram signals.

    Science.gov (United States)

    Lin, Chin-Feng; Shih, Shun-Han; Zhu, Jin-De

    2014-05-01

    In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

  13. Auto-correlation in the motor/imaginary human EEG signals: A vision about the FDFA fluctuations.

    Directory of Open Access Journals (Sweden)

    Gilney Figueira Zebende

    Full Text Available In this paper we analyzed, by the FDFA root mean square fluctuation (rms function, the motor/imaginary human activity produced by a 64-channel electroencephalography (EEG. We utilized the Physionet on-line databank, a publicly available database of human EEG signals, as a standardized reference database for this study. Herein, we report the use of detrended fluctuation analysis (DFA method for EEG analysis. We show that the complex time series of the EEG exhibits characteristic fluctuations depending on the analyzed channel in the scalp-recorded EEG. In order to demonstrate the effectiveness of the proposed technique, we analyzed four distinct channels represented here by F332, F637 (frontal region of the head and P349, P654 (parietal region of the head. We verified that the amplitude of the FDFA rms function is greater for the frontal channels than for the parietal. To tabulate this information in a better way, we define and calculate the difference between FDFA (in log scale for the channels, thus defining a new path for analysis of EEG signals. Finally, related to the studied EEG signals, we obtain the auto-correlation exponent, αDFA by DFA method, that reveals self-affinity at specific time scale. Our results shows that this strategy can be applied to study the human brain activity in EEG processing.

  14. An Approach for Pattern Recognition of EEG Applied in Prosthetic Hand Drive

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Zhang

    2011-12-01

    Full Text Available For controlling the prosthetic hand by only electroencephalogram (EEG, it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open. Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

  15. [Consciousness and the electroencephalogram].

    Science.gov (United States)

    Faber, J; Vladyka, V; Subrt, O

    1991-08-01

    In the course of 12 years the authors subjected to clinical EEG and stereo-EEG (SEEG) 72 patients (66 epileptics with the diagnosis of psychomotor epilepsy and grand mal) and six psychotic patients suffering from schizophrenia. With the exception of five epileptics and two psychotic patients all subjects had epileptic foci in the amygdalohippocampal complex (AHK). After coagulation of these foci marked improvement of the fits and the mental state occurred in half the patients. During EEG and SEEG recording the authors used different activation methods (hyperventilation through the nose and mouth, sleep, listening to music) and above all direct electric stimulation (ES) of one of the AHK. Secondary epileptic foci had, as a rule, more spikes and a lower threshold for ES than primary ones which contained more delta and slow theta waves. The ES led as a rule to an emotional response, such as anxiety and fear, more rarely to illusions, depersonalization and oneiroid hallucinations and twice to a hedonic response of non-sexual character. The purpose of ES was to assess the site from where it is possible to start the original aura or typical parox. The authors considered these foci, consistent with data in the literature, as the leading focus and it was subsequently coagulated. The authors investigated the reactivity and vigility by the patient's response to sound (the patient had to press a button) and by an interview with the patient. It was revealed that in isolated discharges of the spikes and waves in the scalp electrodes, i.e. in the neocortex, reactivity is lacking. In isolated discharges in the AHK the reactivity was satisfactory, but as a rule anxiety developed. It is thus possible to divide consciousness into emotional consciousness with its site in the AHK, i.e. in the limbic system, and rational consciousness which is a function of the neocrotical system. Congenital changes of consciousness such as vigility or sleep are described as "states" of consciousness

  16. Decoding sequence learning from single-trial intracranial EEG in humans.

    Directory of Open Access Journals (Sweden)

    Marzia De Lucia

    Full Text Available We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and one after a night of sleep in two patients with depth electrodes implanted in several brain areas. They performed a visuomotor sequence (serial reaction time task, SRTT using the fingers of their non-dominant hand. Our results show that the decoding algorithm correctly classified single iEEG trials from the trained sequence as belonging to either the initial training phase (day 1, before sleep or a later consolidated phase (day 2, after sleep, whereas it failed to do so for trials belonging to a control condition (pseudo-random sequence. Accurate single-trial classification was achieved by taking advantage of the distributed pattern of neural activity. However, across all the contacts the hippocampus contributed most significantly to the classification accuracy for both patients, and one fronto-striatal contact for one patient. Together, these human intracranial findings demonstrate that a multivariate decoding approach can detect learning-related changes at the level of single-trial iEEG. Because it allows an unbiased identification of brain sites contributing to a behavioral effect (or experimental condition at the level of single subject, this approach could be usefully applied to assess the neural correlates of other complex cognitive functions in patients implanted with multiple electrodes.

  17. The added value of simultaneous EEG and amplitude-integrated EEG recordings in three newborn infants

    NARCIS (Netherlands)

    de Vries, Nathalie K. S.; ter Horst, Hendrik J.; Bos, Arend F.

    2007-01-01

    Amplitude-integrated electroencephalograms (aEEGs) recorded by cerebral function monitors (CFMs) are used increasingly to monitor the cerebral activity of newborn infants with encephalopathy. Recently, new CFM devices became available which also reveal the original EEG signals from the same leads.

  18. Intracranial EEG fluctuates over months after implanting electrodes in human brain

    Science.gov (United States)

    Ung, Hoameng; Baldassano, Steven N.; Bink, Hank; Krieger, Abba M.; Williams, Shawniqua; Vitale, Flavia; Wu, Chengyuan; Freestone, Dean; Nurse, Ewan; Leyde, Kent; Davis, Kathryn A.; Cook, Mark; Litt, Brian

    2017-10-01

    Objective. Implanting subdural and penetrating electrodes in the brain causes acute trauma and inflammation that affect intracranial electroencephalographic (iEEG) recordings. This behavior and its potential impact on clinical decision-making and algorithms for implanted devices have not been assessed in detail. In this study we aim to characterize the temporal and spatial variability of continuous, prolonged human iEEG recordings. Approach. Intracranial electroencephalography from 15 patients with drug-refractory epilepsy, each implanted with 16 subdural electrodes and continuously monitored for an average of 18 months, was included in this study. Time and spectral domain features were computed each day for each channel for the duration of each patient’s recording. Metrics to capture post-implantation feature changes and inflexion points were computed on group and individual levels. A linear mixed model was used to characterize transient group-level changes in feature values post-implantation and independent linear models were used to describe individual variability. Main results. A significant decline in features important to seizure detection and prediction algorithms (mean line length, energy, and half-wave), as well as mean power in the Berger and high gamma bands, was observed in many patients over 100 d following implantation. In addition, spatial variability across electrodes declines post-implantation following a similar timeframe. All selected features decreased by 14-50% in the initial 75 d of recording on the group level, and at least one feature demonstrated this pattern in 13 of the 15 patients. Our findings indicate that iEEG signal features demonstrate increased variability following implantation, most notably in the weeks immediately post-implant. Significance. These findings suggest that conclusions drawn from iEEG, both clinically and for research, should account for spatiotemporal signal variability and that properly assessing the iEEG in

  19. Human-machine interfaces based on EMG and EEG applied to robotic systems

    Directory of Open Access Journals (Sweden)

    Sarcinelli-Filho Mario

    2008-03-01

    Full Text Available Abstract Background Two different Human-Machine Interfaces (HMIs were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Results Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Conclusion Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

  20. Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space.

    Directory of Open Access Journals (Sweden)

    Gernot G Supp

    Full Text Available The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz elicited by familiar (meaningful objects is well established in electroencephalogram (EEG research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs during the presentation of familiar objects relative to unfamiliar controls, which was localized to inferior-temporal, superior-parietal and frontal brain areas by means of distributed source reconstruction. The multivariate analysis of PDC evaluated each possible direction of brain interaction and revealed widespread reciprocal information-transfer during familiar

  1. EEG evaluation of humaneness of asphyxia and decapitation euthanasia of the laboratory rat.

    Science.gov (United States)

    Mikeska, J A; Klemm, W R

    1975-04-01

    The relative humaneness of asphyxia and decapitation was objectively evaluated in rats by EEG monitoring. EEG activation (low voltage, fast activity) was considered to indicate discomfort, pain, and affective responses to euthansia. Such activation was present 37.3 plus or minus 7.5 sec after asphyxia and 13.6 plus or minus 4,6 sec after decapitation. Decapitation was also characterized by an immediate large, and relatively long-lasting, ultra-slow voltage, detected by non-polarizable scalp electrodes. Isoelectric activity (death) occurred 69.4 plus or minus 9.9 sec after onset of asphyxia and 27.2 plus or minus 4.4 sec after decapitation.

  2. Neurodevelopment in newborns: a sample entropy analysis of electroencephalogram

    International Nuclear Information System (INIS)

    Zhang, Dandan; Ding, Haiyan; Liu, Yunfeng; Ding, Haishu; Zhou, Congle; Ye, Datian

    2009-01-01

    The present paper investigates the neural ontogeny of newborns in view of electroencephalogram (EEG) complexity during active sleep (AS) and quiet sleep (QS). Sample entropy (SampEn) is applied to EEG recordings from 168 newborns with postmenstrual age (PMA) ranging from 25 to 60 weeks. The relationship between neurodevelopment and PMA is then explored according to the statistical analysis of the median and interquartile range of SampEn curves. It is found that SampEn of EEG during AS is higher than that during QS. SampEn increases during both AS and QS before about 42 weeks in PMA while it ceases its increase in QS and even decreases in AS after newborns reaching term age. A distinct decrease in the interquartile range of SampEn is found with increasing PMA (from 25 to about 50 weeks), followed by maintenance of low fluctuation in SampEn curves. The study in this paper sets the stage for exhaustive investigation of the SampEn of EEG during brain maturation in newborns. And it could be hoped that SampEn in sleep EEG might be a useful parameter against which delays and aberrations in brain maturation might be tested. The SampEn changes during brain maturation also offer functional clues about neurodevelopment, based on which further explorations could be done. The significance of this paper is the discovery of the decrease in EEG complexity after newborns reaching term. Although some potential neurophysiologic reasons are given, this new discovery might require more study to investigate. In addition, the fluctuation of EEG complexity is analyzed for the first time, which helps to understand the EEG maturation in neurodevelopment

  3. EEG Controlled Wheelchair

    Directory of Open Access Journals (Sweden)

    Swee Sim Kok

    2016-01-01

    Full Text Available This paper describes the development of a brainwave controlled wheelchair. The main objective of this project is to construct a wheelchair which can be directly controlled by the brain without requires any physical feedback as controlling input from the user. The method employed in this project is the Brain-computer Interface (BCI, which enables direct communication between the brain and the electrical wheelchair. The best method for recording the brain’s activity is electroencephalogram (EEG. EEG signal is also known as brainwaves signal. The device that used for capturing the EEG signal is the Emotiv EPOC headset. This headset is able to transmit the EEG signal wirelessly via Bluetooth to the PC (personal computer. By using the PC software, the EEG signals are processed and converted into mental command. According to the mental command (e.g. forward, left... obtained, the output electrical signal is sent out to the electrical wheelchair to perform the desired movement. Thus, in this project, a computer software is developed for translating the EEG signal into mental commands and transmitting out the controlling signal wirelessly to the electrical wheelchair.

  4. Decoding English Alphabet Letters Using EEG Phase Information

    Directory of Open Access Journals (Sweden)

    YiYan Wang

    2018-02-01

    Full Text Available Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8. These results may provide a new approach for brain-computer interface techniques (BCI and may deepen our understanding of EEG oscillations in cognition.

  5. Millivolt-scale DC shifts in the human scalp EEG: evidence for a nonneuronal generator.

    Science.gov (United States)

    Voipio, Juha; Tallgren, Pekka; Heinonen, Erkki; Vanhatalo, Sampsa; Kaila, Kai

    2003-04-01

    Slow shifts in the human scalp-recorded EEG, including those related to changes in brain CO(2) levels, have been generally assumed to result from changes in the level of tonic excitation of apical dendrites of cortical pyramidal neurons. We readdressed this issue using DC-EEG shifts elicited in healthy adult subjects by hypo- or hypercapnia. A 3-min period of hyperventilation resulted in a prompt negative shift with a rate of up to 10 microV/s at the vertex (Cz) and an extremely steep dependence (up to 100 microV/mmHg) on the end-tidal Pco(2). This shift had a maximum of up to -2 mV at Cz versus the temporal derivations (T3/T4). Hyperventilation-like breathing of 5% CO(2)-95% O(2), which does not lead to a significant hypocapnia, resulted in a near-complete block of the negative DC shift at Cz. Hypoventilation, or breathing 5% CO(2) in air at normal respiratory rate, induced a positive shift. The high amplitude of the voltage gradients on the scalp induced by hyperventilation is not consistent with a neuronal origin. Instead, the present data suggest that they are generated by extracortical volume currents driven by a Pco(2)-dependent potential difference across epithelia separating the cerebrospinal fluid and blood. Since changes in respiratory patterns and, hence, in the level of brain Pco(2), are likely to occur under a number of experimental conditions in which slow EEG responses have been reported (e.g., attention shifts, preparatory states, epileptic seizures, and hypoxic episodes), the present results call for a thorough reexamination of the mechanisms underlying scalp-recorded DC-EEG responses.

  6. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording.

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  7. Open Ephys electroencephalography (Open Ephys  +  EEG): a modular, low-cost, open-source solution to human neural recording

    Science.gov (United States)

    Black, Christopher; Voigts, Jakob; Agrawal, Uday; Ladow, Max; Santoyo, Juan; Moore, Christopher; Jones, Stephanie

    2017-06-01

    Objective. Electroencephalography (EEG) offers a unique opportunity to study human neural activity non-invasively with millisecond resolution using minimal equipment in or outside of a lab setting. EEG can be combined with a number of techniques for closed-loop experiments, where external devices are driven by specific neural signals. However, reliable, commercially available EEG systems are expensive, often making them impractical for individual use and research development. Moreover, by design, a majority of these systems cannot be easily altered to the specification needed by the end user. We focused on mitigating these issues by implementing open-source tools to develop a new EEG platform to drive down research costs and promote collaboration and innovation. Approach. Here, we present methods to expand the open-source electrophysiology system, Open Ephys (www.openephys.org), to include human EEG recordings. We describe the equipment and protocol necessary to interface various EEG caps with the Open Ephys acquisition board, and detail methods for processing data. We present applications of Open Ephys  +  EEG as a research tool and discuss how this innovative EEG technology lays a framework for improved closed-loop paradigms and novel brain-computer interface experiments. Main results. The Open Ephys  +  EEG system can record reliable human EEG data, as well as human EMG data. A side-by-side comparison of eyes closed 8-14 Hz activity between the Open Ephys  +  EEG system and the Brainvision ActiCHamp EEG system showed similar average power and signal to noise. Significance. Open Ephys  +  EEG enables users to acquire high-quality human EEG data comparable to that of commercially available systems, while maintaining the price point and extensibility inherent to open-source systems.

  8. Detrended fluctuation analysis and Kolmogorov–Sinai entropy of electroencephalogram signals

    International Nuclear Information System (INIS)

    Lim, Jung Ho; Khang, Eun Joo; Lee, Tae Hyun; Kim, In Hye; Maeng, Seong Eun; Lee, Jae Woo

    2013-01-01

    We measured the electroencephalogram (EEG) of young students in the relaxed state and in the state of the mathematical activities. We applied the detrended fluctuation analysis and Kolmogorov–Sinai entropy (KSE) in the EEG signals. We found that the detrended fluctuation functions follow a power law with Hurst exponents larger than 1/2. The Hurst exponents enhanced at all EEG channels in the state of mathematical activities. The KSE in the relaxed state is larger than those in the state of the mathematical activities. These indicate that the entropy is enhanced in the disorder state of the brain.

  9. Monitoring alert and drowsy states by modeling EEG source nonstationarity

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

    2017-10-01

    Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to

  10. Robust power spectral estimation for EEG data.

    Science.gov (United States)

    Melman, Tamar; Victor, Jonathan D

    2016-08-01

    Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates. Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox. Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors. The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor. In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Circadian variation of EEG power spectra in NREM and REM sleep in humans: dissociation from body temperature

    Science.gov (United States)

    Dijk, D. J.

    1999-01-01

    In humans, EEG power spectra in REM and NREM sleep, as well as characteristics of sleep spindles such as their duration, amplitude, frequency and incidence, vary with circadian phase. Recently it has been hypothesized that circadian variations in EEG spectra in humans are caused by variations in brain or body temperature and may not represent phenomena relevant to sleep regulatory processes. To test this directly, a further analysis of EEG power spectra - collected in a forced desynchrony protocol in which sleep episodes were scheduled to a 28-h period while the rhythms of body temperature and plasma melatonin were oscillating at their near 24-h period - was carried out. EEG power spectra were computed for NREM and REM sleep occurring between 90-120 and 270-300 degrees of the circadian melatonin rhythm, i.e. just after the clearance of melatonin from plasma in the 'morning' and just after the 'evening' increase in melatonin secretion. Average body temperatures during scheduled sleep at these two circadian phases were identical (36.72 degrees C). Despite identical body temperatures, the power spectra in NREM sleep were very different at these two circadian phases. EEG activity in the low frequency spindle range was significantly and markedly enhanced after the evening increase in plasma melatonin as compared to the morning phase. For REM sleep, significant differences in power spectra during these two circadian phases, in particular in the alpha range, were also observed. The results confirm that EEG power spectra in NREM and REM sleep vary with circadian phase, suggesting that the direct contribution of temperature to the circadian variation in EEG power spectra is absent or only minor, and are at variance with the hypothesis that circadian variations in EEG power spectra are caused by variations in temperature.

  12. Human cortical activity related to unilateral movements. A high resolution EEG study.

    Science.gov (United States)

    Urbano, A; Babiloni, C; Onorati, P; Babiloni, F

    1996-12-20

    In the present study a modern high resolution electroencephalography (EEG) technique was used to investigate the dynamic functional topography of human cortical activity related to simple unilateral internally triggered finger movements. The sensorimotor area (M1-S1) contralateral to the movement as well as the supplementary motor area (SMA) and to a lesser extent the ipsilateral M1-S1 were active during the preparation and execution of these movements. These findings suggest that both hemispheres may cooperate in both planning and production of simple unilateral volitional acts.

  13. A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG

    OpenAIRE

    Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K.

    2014-01-01

    Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For pra...

  14. Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal.

    Science.gov (United States)

    Namazi, Hamidreza; Khosrowabadi, Reza; Hussaini, Jamal; Habibi, Shaghayegh; Farid, Ali Akhavan; Kulish, Vladimir V

    2016-08-30

    One of the major challenges in brain research is to relate the structural features of the auditory stimulus to structural features of Electroencephalogram (EEG) signal. Memory content is an important feature of EEG signal and accordingly the brain. On the other hand, the memory content can also be considered in case of stimulus. Beside all works done on analysis of the effect of stimuli on human EEG and brain memory, no work discussed about the stimulus memory and also the relationship that may exist between the memory content of stimulus and the memory content of EEG signal. For this purpose we consider the Hurst exponent as the measure of memory. This study reveals the plasticity of human EEG signals in relation to the auditory stimuli. For the first time we demonstrated that the memory content of an EEG signal shifts towards the memory content of the auditory stimulus used. The results of this analysis showed that an auditory stimulus with higher memory content causes a larger increment in the memory content of an EEG signal. For the verification of this result, we benefit from approximate entropy as indicator of time series randomness. The capability, observed in this research, can be further investigated in relation to human memory.

  15. Classification of EEG signals using a genetic-based machine learning classifier.

    Science.gov (United States)

    Skinner, B T; Nguyen, H T; Liu, D K

    2007-01-01

    This paper investigates the efficacy of the genetic-based learning classifier system XCS, for the classification of noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108bits). EEG signals from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and Fast Fourier Transform (FFT) methods were used to form feature vectors with which mental tasks can be discriminated. XCS achieved a maximum classification accuracy of 99.3% and a best average of 88.9%. The relative classification performance of XCS was then compared against four non-evolutionary classifier systems originating from different learning techniques. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

  16. Using EEG to Study Cognitive Development: Issues and Practices

    Science.gov (United States)

    Bell, Martha Ann; Cuevas, Kimberly

    2012-01-01

    Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain-behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development…

  17. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

    Ma, Zhanyu; Tan, Zheng-Hua; Prasad, Swati

    2012-01-01

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related...

  18. Assessment of anaesthetic depth by clustering analysis and autoregressive modelling of electroencephalograms

    DEFF Research Database (Denmark)

    Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K

    1991-01-01

    The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...

  19. Modulation of induced gamma band activity in the human EEG by attention and visual information processing.

    Science.gov (United States)

    Müller, M M; Gruber, T; Keil, A

    2000-12-01

    Here we present a series of four studies aimed to investigate the link between induced gamma band activity in the human EEG and visual information processing. We demonstrated and validated the modulation of spectral gamma band power by spatial selective visual attention. When subjects attended to a certain stimulus, spectral power was increased as compared to when the same stimulus was ignored. In addition, we showed a shift in spectral gamma band power increase to the contralateral hemisphere when subjects shifted their attention to one visual hemifield. The following study investigated induced gamma band activity and the perception of a Gestalt. Ambiguous rotating figures were used to operationalize the law of good figure (gute Gestalt). We found increased gamma band power at posterior electrode sites when subjects perceived an object. In the last experiment we demonstrated a differential hemispheric gamma band activation when subjects were confronted with emotional pictures. Results of the present experiments in combination with other studies presented in this volume are supportive for the notion that induced gamma band activity in the human EEG is closely related to visual information processing and attentional perceptual mechanisms.

  20. A novel approach for computer assisted EEG monitoring in the adult ICU

    NARCIS (Netherlands)

    Cloostermans, M.C.; de Vos, Cecilia Cecilia Clementine; van Putten, Michel Johannes Antonius Maria

    2011-01-01

    Objective The implementation of a computer assisted system for real-time classification of the electroencephalogram (EEG) in critically ill patients. Methods Eight quantitative features were extracted from the raw EEG and combined into a single classifier. The system was trained with 41 EEG

  1. [Control of intelligent car based on electroencephalogram and neurofeedback].

    Science.gov (United States)

    Li, Song; Xiong, Xin; Fu, Yunfa

    2018-02-01

    To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.

  2. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

    Vznik EEG aktivity v mozku, rozdělení EEG vln podle frekvence, způsob měření EEG, přístroje pro měření EEG. Dále popis biofeedback metody, její možnosti a návrh biofeedback her. Popis zpracování naměřených EEG signálů. EEG generation, brain rhythms, methods of recording EEG, EEG recorder. Description of biofeedback, potentialities of biofeedback, proposal of biofeedback games. Description of processing measured EEG signals. B

  3. Burst suppression in sleep in a routine outpatient EEG

    Directory of Open Access Journals (Sweden)

    Ammar Kheder

    2014-01-01

    Full Text Available Burst suppression (BS is an electroencephalogram (EEG pattern that is characterized by brief bursts of spikes, sharp waves, or slow waves of relatively high amplitude alternating with periods of relatively flat EEG or isoelectric periods. The pattern is usually associated with coma, severe encephalopathy of various etiologies, or general anesthesia. We describe an unusual case of anoxic brain injury in which a BS pattern was seen during behaviorally defined sleep during a routine outpatient EEG study.

  4. EEG-based characterization of flicker perception

    NARCIS (Netherlands)

    Lazo, M.; Tsoneva, T.; Garcia Molina, G.

    2013-01-01

    Steady-State Visual Evoked Potential (SSVEP) is an oscillatory electrical response appearing in the electroencephalogram (EEG) in response to flicker stimulation. The SSVEP manifests more prominently in electrodes located near the visual cortex and has oscillatory components at the stimulation

  5. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

    Science.gov (United States)

    Ying, Xiaoguo; Lin, Han; Hui, Guohua

    2015-01-01

    Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

  6. The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model

    NARCIS (Netherlands)

    Ponten, S.C.; Daffertshofer, A.; Hillebrand, A.; Stam, C.J.

    2010-01-01

    We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity

  7. Slowed EEG rhythmicity in patients with chronic pancreatitis: evidence of abnormal cerebral pain processing?

    DEFF Research Database (Denmark)

    Olesen, Søren Schou; Hansen, Tine Maria; Gravesen, Carina

    2011-01-01

    Intractable pain usually dominates the clinical presentation of chronic pancreatitis (CP). Slowing of electroencephalogram (EEG) rhythmicity has been associated with abnormal cortical pain processing in other chronic pain disorders. The aim of this study was to investigate the spectral distribution...

  8. Emergency electroencephalogram: Usefulness in the diagnosis of nonconvulsive status epilepticus by the on-call neurologist.

    Science.gov (United States)

    Máñez Miró, J U; Díaz de Terán, F J; Alonso Singer, P; Aguilar-Amat Prior, M J

    2018-03-01

    We aim to describe the use of emergency electroencephalogram (EmEEG) by the on-call neurologist when nonconvulsive status epilepticus (NCSE) is suspected, and in other indications, in a tertiary hospital. Observational retrospective cohort study of emergency EEG (EmEEG) recordings with 8-channel systems performed and analysed by the on-call neurologist in the emergency department and in-hospital wards between July 2013 and May 2015. Variables recorded were sex, age, symptoms, first diagnosis, previous seizure and cause, previous stroke, cancer, brain computed tomography, diagnosis after EEG, treatment, patient progress, routine control EEG (rEEG), and final diagnosis. We analysed frequency data, sensitivity, and specificity in the diagnosis of NCSE. The study included 135 EEG recordings performed in 129 patients; 51.4% were men and their median age was 69 years. In 112 cases (83%), doctors ruled out suspected NCSE because of altered level of consciousness in 42 (37.5%), behavioural abnormalities in 38 (33.9%), and aphasia in 32 (28.5%). The EmEEG diagnosis was NCSE in 37 patients (33%), and this was confirmed in 35 (94.6%) as the final diagnosis. In 3 other cases, NCSE was the diagnosis on discharge as confirmed by rEEG although the EmEEG missed this condition at first. EmEEG performed to rule out NCSE showed 92.1% sensitivity, 97.2% specificity, a positive predictive value of 94.6%, and a negative predictive value of 96%. Our experience finds that, in an appropriate clinical context, EmEEG performed by the on-call neurologist is a sensitive and specific tool for diagnosing NCSE. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

  9. Adenosine A{sub 1} receptors in human sleep regulation studied by electroencephalography (EEG) and positron emission tomography (PET)[Dissertation 17227

    Energy Technology Data Exchange (ETDEWEB)

    Geissler, E

    2007-07-01

    Sleep is an essential physiological process. However, the functions of sleep and the endogenous mechanisms involved in sleep regulation are only partially understood. Convergent lines of evidence support the hypothesis that the build-up of sleep propensity during wakefulness and its decline during sleep are associated with alterations in brain adenosine levels and adenosine receptor concentrations. The non-selective A{sub 1} and A{sub 2A} adenosine receptor antagonist caffeine stimulates alertness and is known to attenuate changes in the waking and sleep electroencephalogram (EEG) typically observed after prolonged waking. Several findings point to an important function of the adenosine A{sub 1} receptor (A{sub 1}AR) in the modulation of vigilance states. The A{sub 1}AR is densely expressed in brain regions involved in sleep regulation, and pharmacological manipulations affecting the A{sub 1}AR were shown to influence sleep propensity and sleep depth. However, an involvement of the A{sub 2A} adenosine receptor (A{sub 2A}AR) is also assumed. The distinct functions of the A{sub 1} and A{sub 2A} receptor subtypes in sleep-wake regulation and in mediating the effects of caffeine have not been identified so far. The selective adenosine A{sub 1} receptor antagonist, 8-cyclopentyl-3-(3-{sup 18}Ffluoropropyl)- 1-propylxanthine ({sup 18}F-CPFPX), offers the opportunity to get further insights into adenosinergic mechanisms by in vivo imaging of the A{sub 1}AR subtype with positron emission tomography (PET). The aim of this thesis was to elucidate the role of adenosine A{sub 1} receptors in human sleep regulation, combining {sup 18}F-CPFPX PET brain imaging and EEG recordings, the gold standard in sleep research. It was hypothesized that sleep deprivation would induce adenosine accumulation and/or changes in A{sub 1}AR density. Thus, the question was addressed whether these effects of prolonged wakefulness can be visualized by altered {sup 18}F-CPFPX binding. Moreover, it was

  10. Artificial bee colony algorithm for single-trial electroencephalogram analysis.

    Science.gov (United States)

    Hsu, Wei-Yen; Hu, Ya-Ping

    2015-04-01

    In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  11. Electroencephalogram measurement using polymer-based dry microneedle electrode

    Science.gov (United States)

    Arai, Miyako; Nishinaka, Yuya; Miki, Norihisa

    2015-06-01

    In this paper, we report a successful electroencephalogram (EEG) measurement using polymer-based dry microneedle electrodes. The electrodes consist of needle-shaped substrates of SU-8, a silver film, and a nanoporous parylene protective film. Differently from conventional wet electrodes, microneedle electrodes do not require skin preparation and a conductive gel. SU-8 is superior as a structural material to poly(dimethylsiloxane) (PDMS; Dow Corning Toray Sylgard 184) in terms of hardness, which was used in our previous work, and facilitates the penetration of needles through the stratum corneum. SU-8 microneedles can be successfully inserted into the skin without breaking and could maintain a sufficiently low skin-electrode contact impedance for EEG measurement. The electrodes successfully measured EEG from the frontal pole, and the quality of acquired signals was verified to be as high as those obtained using commercially available wet electrodes without any skin preparation or a conductive gel. The electrodes are readily applicable to record brain activities for a long period with little stress involved in skin preparation to the users.

  12. Periodic electroencephalogram discharges in a case of Lafora body disease: An unusual finding

    Directory of Open Access Journals (Sweden)

    Rajendra Singh Jain

    2016-01-01

    Full Text Available Lafora body disease (LBD is a form of progressive myoclonic epilepsy, characterized by seizures, myoclonic jerks, cognitive decline, ataxia, and intracellular polyglucosan inclusion bodies (Lafora bodies in the neurons, heart, skeletal muscle, liver, and sweat gland duct cells. Electroencephalogram (EEG findings in LBD may include multiple spikes and wave discharges, photosensitivity, multifocal epileptiform discharges, and progressive slowing in background activity. Periodicity in epileptiform discharges has not been frequently depicted in LBD. We herein report an unusual case of LBD who showed generalized periodic epileptiform discharges in EEG.

  13. Aetiology and prognosis of encephalopathic patterns on electroencephalogram in a general hospital.

    LENUS (Irish Health Repository)

    O'Sullivan, S S

    2012-02-03

    The purpose of this study was to investigate the frequency and clinical outcome of patients with encephalopathic electroencephalograms (EEGs) in a neurophysiology department based in a general hospital. We performed a retrospective review of all EEGs obtained during an 18-month period in a large tertiary referral hospital. The referral reasons for EEG, the diagnoses reached, and patient outcomes were reviewed according to EEG severity. One hundred and twenty-three patients with encephalopathic EEGs were reviewed. The most common referral reason found was for an assessment of a possible first-onset seizure. The most common diagnosis found was one of dementia or learning disability. Of patients who were followed-up for a median of 19 months, 20.7% had died. The mortality rate generally increased according to the severity of the encephalopathy on EEG. However, 21.4% of those patients with excessive theta activity only on EEG had died. This study highlights an increased mortality even in the apparently \\'milder\\' degrees of EEG abnormalities.

  14. A simple system for detection of EEG artifacts in polysomnographic recordings.

    Science.gov (United States)

    Durka, P J; Klekowicz, H; Blinowska, K J; Szelenberger, W; Niemcewicz, Sz

    2003-04-01

    We present an efficient parametric system for automatic detection of electroencephalogram (EEG) artifacts in polysomnographic recordings. For each of the selected types of artifacts, a relevant parameter was calculated for a given epoch. If any of these parameters exceeded a threshold, the epoch was marked as an artifact. Performance of the system, evaluated on 18 overnight polysomnographic recordings, revealed concordance with decisions of human experts close to the interexpert agreement and the repeatability of expert's decisions, assessed via a double-blind test. Complete software (Matlab source code) for the presented system is freely available from the Internet at http://brain.fuw.edu.pl/artifacts.

  15. Mapping human preictal and ictal haemodynamic networks using simultaneous intracranial EEG-fMRI

    Directory of Open Access Journals (Sweden)

    Umair J. Chaudhary

    2016-01-01

    In conclusion, icEEG-fMRI allowed us to reveal BOLD changes within and beyond the SOZ linked to very localised ictal fluctuations in beta and gamma activity measured in the amygdala and hippocampus. Furthermore, the BOLD changes within the SOZ structures were better captured by the quantitative models, highlighting the interest in considering seizure-related EEG fluctuations across the entire spectrum.

  16. Hypoglycemia-related electroencephalogram changes are independent of gender, age, duration of diabetes, and awareness status in type 1 diabetes

    DEFF Research Database (Denmark)

    Remvig, Line Sofie; Elsborg, Rasmus; Sejling, Anne-Sophie

    2012-01-01

    Introduction: Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm...... device. The aim of the present study was to explore the characteristics of the EEG differentiating normoglycemia and hypoglycemia and to elucidate potential group differences. Methods: We pooled data from experiments in T1DM where EEG was available during both normoglycemia and hypo-glycemia for each...

  17. Hypoglycemia-associated changes in the electroencephalogram in patients with type 1 diabetes and normal hypoglycemia awareness or unawareness

    DEFF Research Database (Denmark)

    Sejling, Anne-Sophie; Kjær, Troels W; Pedersen-Bjergaard, Ulrik

    2015-01-01

    Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes.Twenty-four patients...... and hypoglycemia symptom scores were recorded and the counterregulatory hormonal response was measured.Quantitative EEG analysis showed that the absolute amplitude of the theta band and alpha-theta band up to doubled during hypoglycemia with no difference between the two groups. In the recovery period the theta...

  18. EEG use in a tertiary referral centre.

    LENUS (Irish Health Repository)

    O'Toole, O

    2011-11-15

    The aim of this study was to retrospectively audit all electroencephalograms (EEGs) done over a 2-month period in 2009 by the Neurophysiology Department at Cork University Hospital. There were 316 EEGs performed in total, of which 176\\/316 (56%) were done within 24 hours of request. Out of 316 EEGs, 208 (66%) were considered \\'appropriate\\' by SIGN and NICE guidelines; 79\\/208 (38%) had abnormal EEGs and 28 of these abnormal EEGs had epileptiform features. There were 108\\/316 (34%) \\'inappropriate\\' requests for EEG; of these 15\\/108 (14%) were abnormal. Of the 67\\/316 (21%) patients who had EEGs requested based on a history of syncope\\/funny turns: none of these patients had epileptiform abnormalities on their EEGs. Our audit demonstrates that EEGs are inappropriately over-requested in our institution in particular for cases with reported \\'funny turns\\' and syncope. The yield from EEGs in this cohort of patients was low as would be expected.

  19. Epileptic seizure detection in EEG signal with GModPCA and support vector machine.

    Science.gov (United States)

    Jaiswal, Abeg Kumar; Banka, Haider

    2017-01-01

    Epilepsy is one of the most common neurological disorders caused by recurrent seizures. Electroencephalograms (EEGs) record neural activity and can detect epilepsy. Visual inspection of an EEG signal for epileptic seizure detection is a time-consuming process and may lead to human error; therefore, recently, a number of automated seizure detection frameworks were proposed to replace these traditional methods. Feature extraction and classification are two important steps in these procedures. Feature extraction focuses on finding the informative features that could be used for classification and correct decision-making. Therefore, proposing effective feature extraction techniques for seizure detection is of great significance. Principal Component Analysis (PCA) is a dimensionality reduction technique used in different fields of pattern recognition including EEG signal classification. Global modular PCA (GModPCA) is a variation of PCA. In this paper, an effective framework with GModPCA and Support Vector Machine (SVM) is presented for epileptic seizure detection in EEG signals. The feature extraction is performed with GModPCA, whereas SVM trained with radial basis function kernel performed the classification between seizure and nonseizure EEG signals. Seven different experimental cases were conducted on the benchmark epilepsy EEG dataset. The system performance was evaluated using 10-fold cross-validation. In addition, we prove analytically that GModPCA has less time and space complexities as compared to PCA. The experimental results show that EEG signals have strong inter-sub-pattern correlations. GModPCA and SVM have been able to achieve 100% accuracy for the classification between normal and epileptic signals. Along with this, seven different experimental cases were tested. The classification results of the proposed approach were better than were compared the results of some of the existing methods proposed in literature. It is also found that the time and space

  20. Human physiological benefits of viewing nature: EEG responses to exact and statistical fractal patterns.

    Science.gov (United States)

    Hagerhall, C M; Laike, T; Küller, M; Marcheschi, E; Boydston, C; Taylor, R P

    2015-01-01

    Psychological and physiological benefits of viewing nature have been extensively studied for some time. More recently it has been suggested that some of these positive effects can be explained by nature's fractal properties. Virtually all studies on human responses to fractals have used stimuli that represent the specific form of fractal geometry found in nature, i.e. statistical fractals, as opposed to fractal patterns which repeat exactly at different scales. This raises the question of whether human responses like preference and relaxation are being driven by fractal geometry in general or by the specific form of fractal geometry found in nature. In this study we consider both types of fractals (statistical and exact) and morph one type into the other. Based on the Koch curve, nine visual stimuli were produced in which curves of three different fractal dimensions evolve gradually from an exact to a statistical fractal. The patterns were shown for one minute each to thirty-five subjects while qEEG was continuously recorded. The results showed that the responses to statistical and exact fractals differ, and that the natural form of the fractal is important for inducing alpha responses, an indicator of a wakefully relaxed state and internalized attention.

  1. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

    Science.gov (United States)

    Kim, Kyungsoo; Lim, Sung-Ho; Lee, Jaeseok; Kang, Won-Seok; Moon, Cheil; Choi, Ji-Woong

    2016-01-01

    Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. PMID:27322267

  2. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

    Directory of Open Access Journals (Sweden)

    Kyungsoo Kim

    2016-06-01

    Full Text Available Electroencephalograms (EEGs measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE schemes based on a joint maximum likelihood (ML criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°.

  3. Rett syndrome: EEG presentation.

    Science.gov (United States)

    Robertson, R; Langill, L; Wong, P K; Ho, H H

    1988-11-01

    Rett syndrome, a degenerative neurological disorder of girls, has a classical presentation and typical EEG findings. The electroencephalograms (EEGs) of 7 girls whose records have been followed from the onset of symptoms to the age of 5 or more are presented. These findings are tabulated with the Clinical Staging System of Hagberg and Witt-Engerström (1986). The records show a progressive deterioration in background rhythms in waking and sleep. The abnormalities of the background activity may only become evident at 4-5 years of age or during stage 2--the Rapid Destructive Stage. The marked contrast between waking and sleep background may not occur until stage 3--the Pseudostationary Stage. In essence EEG changes appear to lag behind clinical symptomatology by 1-3 years. An unexpected, but frequent, abnormality was central spikes seen in 5 of 7 girls. They appeared to be age related and could be evoked by tactile stimulation in 2 patients. We hypothesize that the prominent 'hand washing' mannerism may be self-stimulating and related to the appearance of central spike discharges.

  4. Exercise benefits for the aging brain depend on the accompanying cognitive load: insights from sleep electroencephalogram.

    Science.gov (United States)

    Horne, Jim

    2013-11-01

    Although exercise clearly offsets aging effects on the body, its benefits for the aging brain are likely to depend on the extent that physical activity (especially locomotion) facilitates multisensory encounters, curiosity, and interactions with novel environments; this is especially true for exploratory activity, which occupies much of wakefulness for most mammals in the wild. Cognition is inseparable from physical activity, with both interlinked to promote neuroplasticity and more successful brain aging. In these respects and for humans, exercising in a static, featureless, artificially lit indoor setting contrasts with exploratory outdoor walking within a novel environment during daylight. However, little is known about the comparative benefits for the aging brain of longer-term daily regimens of this latter nature including the role of sleep, to the extent that sleep enhances neuroplasticity as shown in short-term laboratory studies. More discerning analyses of sleep electroencephalogram (EEG) slow-wave activity especially 0.5-2-Hz activity would provide greater insights into use-dependent recovery processes during longer-term tracking of these regimens and complement slower changing waking neuropsychologic and resting functional magnetic resonance imaging (fMRI) measures, including those of the brain's default mode network. Although the limited research only points to ephemeral small sleep EEG effects of pure exercise, more enduring effects seem apparent when physical activity incorporates cognitive challenges. In terms of "use it or lose it," curiosity-driven "getting out and about," encountering, interacting with, and enjoying novel situations may well provide the brain with its real exercise, further reflected in changes to the dynamics of sleep. Copyright © 2013 Elsevier B.V. All rights reserved.

  5. Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

    Science.gov (United States)

    Feltane, Amal; Boudreaux-Bartels, G. Faye; Besio, Walter

    2012-01-01

    Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection. PMID:23073989

  6. Long-Range Correlation in alpha-Wave Predominant EEG in Human

    Science.gov (United States)

    Sharif, Asif; Chyan Lin, Der; Kwan, Hon; Borette, D. S.

    2004-03-01

    The background noise in the alpha-predominant EEG taken from eyes-open and eyes-closed neurophysiological states is studied. Scale-free characteristic is found in both cases using the wavelet approach developed by Simonsen and Nes [1]. The numerical results further show the scaling exponent during eyes-closed is consistently lower than eyes-open. We conjecture the origin of this difference is related to the temporal reconfiguration of the neural network in the brain. To further investigate the scaling structure of the EEG background noise, we extended the second order statistics to higher order moments using the EEG increment process. We found that the background fluctuation in the alpha-predominant EEG is predominantly monofractal. Preliminary results are given to support this finding and its implication in brain functioning is discussed. [1] A.H. Simonsen and O.M. Nes, Physical Review E, 58, 2779¡V2748 (1998).

  7. Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements.

    Directory of Open Access Journals (Sweden)

    Fabrizio De Vico Fallani

    2010-12-01

    Full Text Available Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity--i.e., the tendency to form two separate subgraphs--than couples playing cooperative or tit-for-tat strategies. The decision to defect can be "read" in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

  8. Alternating Dynamics of Segregation and Integration in Human EEG Functional Networks During Working-memory Task.

    Science.gov (United States)

    Zippo, Antonio G; Della Rosa, Pasquale A; Castiglioni, Isabella; Biella, Gabriele E M

    2018-02-10

    Brain functional networks show high variability in short time windows but mechanisms governing these transient dynamics remain unknown. In this work, we studied the temporal evolution of functional brain networks involved in a working memory (WM) task while recording high-density electroencephalography (EEG) in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation faded after the prevailing the functional integration. Notably, wrong trials were associated with different or disrupted sequences of the segregation-integration profiles and measures of network centrality and modularity were able to identify crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a WM task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  9. What's that sound? Matches with auditory long-term memory induce gamma activity in human EEG.

    Science.gov (United States)

    Lenz, Daniel; Schadow, Jeanette; Thaerig, Stefanie; Busch, Niko A; Herrmann, Christoph S

    2007-04-01

    In recent years the cognitive functions of human gamma-band activity (30-100 Hz) advanced continuously into scientific focus. Not only bottom-up driven influences on 40 Hz activity have been observed, but also top-down processes seem to modulate responses in this frequency band. Among the various functions that have been related to gamma activity a pivotal role has been assigned to memory processes. Visual experiments suggested that gamma activity is involved in matching visual input to memory representations. Based on these findings we hypothesized that such memory related modulations of gamma activity exist in the auditory modality, as well. Thus, we chose environmental sounds for which subjects already had a long-term memory (LTM) representation and compared them to unknown, but physically similar sounds. 21 subjects had to classify sounds as 'recognized' or 'unrecognized', while EEG was recorded. Our data show significantly stronger activity in the induced gamma-band for recognized sounds in the time window between 300 and 500 ms after stimulus onset with a central topography. The results suggest that induced gamma-band activity reflects the matches between sounds and their representations in auditory LTM.

  10. Deep learning for EEG-Based preference classification

    Science.gov (United States)

    Teo, Jason; Hou, Chew Lin; Mountstephens, James

    2017-10-01

    Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

  11. Progress in EEG-Based Brain Robot Interaction Systems

    Directory of Open Access Journals (Sweden)

    Xiaoqian Mao

    2017-01-01

    Full Text Available The most popular noninvasive Brain Robot Interaction (BRI technology uses the electroencephalogram- (EEG- based Brain Computer Interface (BCI, to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques.

  12. A Fast EEG Forecasting Algorithm for Phase-Locked Transcranial Electrical Stimulation of the Human Brain

    Directory of Open Access Journals (Sweden)

    Farrokh Mansouri

    2017-07-01

    Full Text Available A growing body of research suggests that non-invasive electrical brain stimulation can more effectively modulate neural activity when phase-locked to the underlying brain rhythms. Transcranial alternating current stimulation (tACS can potentially stimulate the brain in-phase to its natural oscillations as recorded by electroencephalography (EEG, but matching these oscillations is a challenging problem due to the complex and time-varying nature of the EEG signals. Here we address this challenge by developing and testing a novel approach intended to deliver tACS phase-locked to the activity of the underlying brain region in real-time. This novel approach extracts phase and frequency from a segment of EEG, then forecasts the signal to control the stimulation. A careful tuning of the EEG segment length and prediction horizon is required and has been investigated here for different EEG frequency bands. The algorithm was tested on EEG data from 5 healthy volunteers. Algorithm performance was quantified in terms of phase-locking values across a variety of EEG frequency bands. Phase-locking performance was found to be consistent across individuals and recording locations. With current parameters, the algorithm performs best when tracking oscillations in the alpha band (8–13 Hz, with a phase-locking value of 0.77 ± 0.08. Performance was maximized when the frequency band of interest had a dominant frequency that was stable over time. The algorithm performs faster, and provides better phase-locked stimulation, compared to other recently published algorithms devised for this purpose. The algorithm is suitable for use in future studies of phase-locked tACS in preclinical and clinical applications.

  13. Music perception and imagery in EEG: Alpha band effects of task and stimulus

    NARCIS (Netherlands)

    Schaefer, R.S.; Vlek, R.J.; Desain, P.W.M.

    2011-01-01

    Previous work has shown that mental imagination of sound generally elicits an increase of alpha band activity (8-12Hz) in the electroencephalogram (EEG). In addition, alpha activity has been shown to be related to music processing. In the current study, EEG signatures were investigated for

  14. Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study.

    Directory of Open Access Journals (Sweden)

    Kei Omata

    Full Text Available The emergence of the occipital alpha rhythm on brain electroencephalogram (EEG is associated with brain activity in the cerebral neocortex and deep brain structures. To further understand the mechanisms of alpha rhythm power fluctuation, we performed simultaneous EEGs and functional magnetic resonance imaging recordings in human subjects during a resting state and explored the dynamic relationship between alpha power fluctuation and blood oxygenation level-dependent (BOLD signals of the brain. Based on the frequency characteristics of the alpha power time series (APTS during 20-minute EEG recordings, we divided the APTS into two components: fast fluctuation (0.04-0.167 Hz and slow fluctuation (0-0.04 Hz. Analysis of the correlation between the MRI signal and each component revealed that the slow fluctuation component of alpha power was positively correlated with BOLD signal changes in the brain stem and the medial part of the thalamus and anterior cingulate cortex, while the fast fluctuation component was correlated with the lateral part of the thalamus and the anterior cingulate cortex, but not the brain stem. In summary, these data suggest that different subcortical structures contribute to slow and fast modulations of alpha spectra on brain EEG.

  15. How do reference montage and electrodes setup affect the measured scalp EEG potentials?

    Science.gov (United States)

    Hu, Shiang; Lai, Yongxiu; Valdes-Sosa, Pedro A.; Bringas-Vega, Maria L.; Yao, Dezhong

    2018-04-01

    Objective. Human scalp electroencephalogram (EEG) is widely applied in cognitive neuroscience and clinical studies due to its non-invasiveness and ultra-high time resolution. However, the representativeness of the measured EEG potentials for the underneath neural activities is still a problem under debate. This study aims to investigate systematically how both reference montage and electrodes setup affect the accuracy of EEG potentials. Approach. First, the standard EEG potentials are generated by the forward calculation with a single dipole in the neural source space, for eleven channel numbers (10, 16, 21, 32, 64, 85, 96, 128, 129, 257, 335). Here, the reference is the ideal infinity implicitly determined by forward theory. Then, the standard EEG potentials are transformed to recordings with different references including five mono-polar references (Left earlobe, Fz, Pz, Oz, Cz), and three re-references (linked mastoids (LM), average reference (AR) and reference electrode standardization technique (REST)). Finally, the relative errors between the standard EEG potentials and the transformed ones are evaluated in terms of channel number, scalp regions, electrodes layout, dipole source position and orientation, as well as sensor noise and head model. Main results. Mono-polar reference recordings are usually of large distortions; thus, a re-reference after online mono-polar recording should be adopted in general to mitigate this effect. Among the three re-references, REST is generally superior to AR for all factors compared, and LM performs worst. REST is insensitive to head model perturbation. AR is subject to electrodes coverage and dipole orientation but no close relation with channel number. Significance. These results indicate that REST would be the first choice of re-reference and AR may be an alternative option for high level sensor noise case. Our findings may provide the helpful suggestions on how to obtain the EEG potentials as accurately as possible for

  16. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal

    OpenAIRE

    Hamidreza Namazi; Amin Akrami; Sina Nazeri; Vladimir V. Kulish

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally ...

  17. Burst-suppression pattern in the electroencephalogram of newborns and infants. Its clinical expression

    Directory of Open Access Journals (Sweden)

    Cervantes Blanco Jorge Mauricio

    2014-07-01

    Full Text Available Burst-suppression pattern in the electroencephalogram (EEG is associated with severe brain damage and has a bad prognosis in 85% of the cases. Objectives. To identify the prevalence of the EEG burst-suppression pattern (BSP in fullterm newborns and infants, determine its etiol- ogy, clinical features and course. Methods. A retrospective study was conducted. Between January 2008 and December 2012, 4,891 EEGs were reviewed. The EEGs of newborns and infants (< 3 months of age with BSP were selected. Results. 11 cases identified with burst suppression pattern. The overall prevalence of which was 3.5%; 8.1% among the newborns and 1.2% among infants. Seizures were the main reason for doing an EEG in the newborn period in 7 patients and after day 28 in three. The clinical manifestations were abnormal level of consciousness (n=8, hypotonia (n=2, and spasticity (n=6. The main causes were hypoxic ischemic injury, stroke and kernicterus. There were two cases of early infantile epileptic encephalopathy. Two patients died before the third month of age; 8 survived an average of 13 months. All had epilepsy, neurologic retardation and disability. Two patients had persistent EEG burst-suppression pattern; 1 and 3 months after the neonatal period respectively; 7 had focal spikes and an asymmetric pattern. Conclusions. Electroencephalographic burst-suppression pat- tern predicts a severe neurologic injury in fullterm newborns and infants.

  18. Electroencephalogram approximate entropy influenced by both age and sleep

    Directory of Open Access Journals (Sweden)

    Gerick M. H. Lee

    2013-12-01

    Full Text Available The use of information-based measures to assess changes in conscious state is an increasingly popular topic. Though recent results have seemed to justify the merits of such methods, little has been done to investigate the applicability of such measures to children. For our work, we used the approximate entropy (ApEn, a measure previously shown to correlate with changes in conscious state when applied to the electroencephalogram (EEG, and sought to confirm whether previously reported trends in adult ApEn values across wake and sleep were present in children. Besides validating the prior findings that ApEn decreases from wake to sleep (including wake, rapid eye movement [REM] sleep, and non-REM sleep in adults, we found that previously reported ApEn decreases across vigilance states in adults were also present in children (ApEn trends for both age groups: wake > REM sleep > non-REM sleep. When comparing ApEn values between age groups, adults had significantly larger ApEn values than children during wakefulness. After the application of an 8 Hz high-pass filter to the EEG signal, ApEn values were recalculated. The number of electrodes with significant vigilance state effects dropped from all 109 electrodes with the original 1 Hz filter to 1 electrode with the 8 Hz filter. The number of electrodes with significant age effects dropped from ten to four. Our results support the notion that ApEn can reliably distinguish between vigilance states, with low-frequency sleep-related oscillations implicated as the driver of changes between vigilance states. We suggest that the observed differences between adult and child ApEn values during wake may reflect differences in connectivity between age groups, a factor which may be important in the use of EEG to measure consciousness.

  19. Burst suppression in sleep in a routine outpatient EEG ?

    OpenAIRE

    Kheder, Ammar; Bianchi, Matt T.; Westover, M. Brandon

    2014-01-01

    Burst suppression (BS) is an electroencephalogram (EEG) pattern that is characterized by brief bursts of spikes, sharp waves, or slow waves of relatively high amplitude alternating with periods of relatively flat EEG or isoelectric periods. The pattern is usually associated with coma, severe encephalopathy of various etiologies, or general anesthesia. We describe an unusual case of anoxic brain injury in which a BS pattern was seen during behaviorally defined sleep during a routine outpatient...

  20. The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal

    Science.gov (United States)

    Namazi, Hamidreza; Kulish, Vladimir V.; Akrami, Amin

    2016-05-01

    One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders.

  1. Two channel EEG thought pattern classifier.

    Science.gov (United States)

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  2. Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis.

    Science.gov (United States)

    Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

    2016-01-01

    Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, and noise exposed which received Scoparia extract. The results showed no marked difference observed between control and control treated with Scoparia extract on EAM, Y-maze, novel object test, and EEG in both prefrontal and occipital region, however, noise stress exposed rats showed significant increase in the reference memory and working memory error in EAM and latency delay, triad errors in Y-maze, and prefrontal and occipital EEG frequency rate with the corresponding increase in plasma corticosterone and epinephrine, and significant reduction in the novelty test, and significant reduction in the novelty test, amplitude of prefrontal, occipital EEG, and acetylcholine. These noise stress induced changes in EAM, Y-maze, novel object test, and neurotransmitters were significantly prevented when treated with Scoparia extract and these changes may be due to the normalizing action of Scoparia extract on the brain, which altered due to noise stress. Noise stress exposure causes EEG, behavior, and neurotransmitter alteration in the frontoparietal and occipital regions mainly involved in planning and recognition memoryOnly the noise stress exposed animals showed the significant alteration in the EEG, behavior, and neurotransmittersHowever, these noise stress induced changes in EEG behavior and neurotransmitters were significantly prevented when treated with Scoparia extractThese changes may be due to the normalizing action of Scoparia dulcis (adoptogen) on the brain which altered by noise stress. Abbreviations used: EEG

  3. Analysis of EEG Related Saccadic Eye Movement

    Science.gov (United States)

    Funase, Arao; Kuno, Yoshiaki; Okuma, Shigeru; Yagi, Tohru

    Our final goal is to establish the model for saccadic eye movement that connects the saccade and the electroencephalogram(EEG). As the first step toward this goal, we recorded and analyzed the saccade-related EEG. In the study recorded in this paper, we tried detecting a certain EEG that is peculiar to the eye movement. In these experiments, each subject was instructed to point their eyes toward visual targets (LEDs) or the direction of the sound sources (buzzers). In the control cases, the EEG was recorded in the case of no eye movemens. As results, in the visual experiments, we found that the potential of EEG changed sharply on the occipital lobe just before eye movement. Furthermore, in the case of the auditory experiments, similar results were observed. In the case of the visual experiments and auditory experiments without eye movement, we could not observed the EEG changed sharply. Moreover, when the subject moved his/her eyes toward a right-side target, a change in EEG potential was found on the right occipital lobe. On the contrary, when the subject moved his/her eyes toward a left-side target, a sharp change in EEG potential was found on the left occipital lobe.

  4. Recognizing the degree of human attention using EEG signals from mobile sensors.

    Science.gov (United States)

    Liu, Ning-Han; Chiang, Cheng-Yu; Chu, Hsuan-Chin

    2013-08-09

    During the learning process, whether students remain attentive throughout instruction generally influences their learning efficacy. If teachers can instantly identify whether students are attentive they can be suitably reminded to remain focused, thereby improving their learning effects. Traditional teaching methods generally require that teachers observe students' expressions to determine whether they are attentively learning. However, this method is often inaccurate and increases the burden on teachers. With the development of electroencephalography (EEG) detection tools, mobile brainwave sensors have become mature and affordable equipment. Therefore, in this study, whether students are attentive or inattentive during instruction is determined by observing their EEG signals. Because distinguishing between attentiveness and inattentiveness is challenging, two scenarios were developed for this study to measure the subjects' EEG signals when attentive and inattentive. After collecting EEG data using mobile sensors, various common features were extracted from the raw data. A support vector machine (SVM) classifier was used to calculate and analyze these features to identify the combination of features that best indicates whether students are attentive. Based on the experiment results, the method proposed in this study provides a classification accuracy of up to 76.82%. The study results can be used as a reference for learning system designs in the future.

  5. Interictal functional connectivity of human epileptic networks assessed by intracerebral EEG and BOLD signal fluctuations.

    Directory of Open Access Journals (Sweden)

    Gaelle Bettus

    Full Text Available In this study, we aimed to demonstrate whether spontaneous fluctuations in the blood oxygen level dependent (BOLD signal derived from resting state functional magnetic resonance imaging (fMRI reflect spontaneous neuronal activity in pathological brain regions as well as in regions spared by epileptiform discharges. This is a crucial issue as coherent fluctuations of fMRI signals between remote brain areas are now widely used to define functional connectivity in physiology and in pathophysiology. We quantified functional connectivity using non-linear measures of cross-correlation between signals obtained from intracerebral EEG (iEEG and resting-state functional MRI (fMRI in 5 patients suffering from intractable temporal lobe epilepsy (TLE. Functional connectivity was quantified with both modalities in areas exhibiting different electrophysiological states (epileptic and non affected regions during the interictal period. Functional connectivity as measured from the iEEG signal was higher in regions affected by electrical epileptiform abnormalities relative to non-affected areas, whereas an opposite pattern was found for functional connectivity measured from the BOLD signal. Significant negative correlations were found between the functional connectivities of iEEG and BOLD signal when considering all pairs of signals (theta, alpha, beta and broadband and when considering pairs of signals in regions spared by epileptiform discharges (in broadband signal. This suggests differential effects of epileptic phenomena on electrophysiological and hemodynamic signals and/or an alteration of the neurovascular coupling secondary to pathological plasticity in TLE even in regions spared by epileptiform discharges. In addition, indices of directionality calculated from both modalities were consistent showing that the epileptogenic regions exert a significant influence onto the non epileptic areas during the interictal period. This study shows that functional

  6. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

    Science.gov (United States)

    Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

    2014-01-01

    Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks.

  7. Assessment of the depth of anesthesia based on symbolic dynamics of the EEG

    OpenAIRE

    Tupaika, Nadine; Vallverdú Ferrer, Montserrat; Jospin, Mathieu; Jensen, Erik Weber; Struys, Michel M. R. F.; Vereecke, Hugo E. M.; Voss, Andreas; Caminal Magrans, Pere

    2010-01-01

    Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the ...

  8. Prognostic accuracy of electroencephalograms in preterm infants

    DEFF Research Database (Denmark)

    Fogtmann, Emilie Pi; Plomgaard, Anne Mette; Greisen, Gorm

    2017-01-01

    CONTEXT: Brain injury is common in preterm infants, and predictors of neurodevelopmental outcome are relevant. OBJECTIVE: To assess the prognostic test accuracy of the background activity of the EEG recorded as amplitude-integrated EEG (aEEG) or conventional EEG early in life in preterm infants...... for predicting neurodevelopmental outcome. DATA SOURCES: The Cochrane Library, PubMed, Embase, and the Cumulative Index to Nursing and Allied Health Literature. STUDY SELECTION: We included observational studies that had obtained an aEEG or EEG within 7 days of life in preterm infants and reported...... neurodevelopmental outcomes 1 to 10 years later. DATA EXTRACTION: Two reviewers independently performed data extraction with regard to participants, prognostic testing, and outcomes. RESULTS: Thirteen observational studies with a total of 1181 infants were included. A metaanalysis was performed based on 3 studies...

  9. Acute toxicity and sleep-wake EEG analysis of Stachtarpheta ...

    African Journals Online (AJOL)

    The effect of systemic administration of TASC on sleep architecture in rats was also evaluated in Sprague-Dawley rats that were chronically implanted with electrodes for electroencephalogram (EEG) and electromyogram (EMG) recording. The acute toxicity test revealed no lethal effect with doses of SCCR (up to 2000 ...

  10. Continuous emotion detection using EEG signals and facial expressions

    NARCIS (Netherlands)

    Soleymani, Mohammad; Asghari-Esfeden, Sadjad; Pantic, Maja; Fu, Yun

    Emotions play an important role in how we select and consume multimedia. Recent advances on affect detection are focused on detecting emotions continuously. In this paper, for the first time, we continuously detect valence from electroencephalogram (EEG) signals and facial expressions in response to

  11. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

    Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...

  12. Automatic burst detection for the EEG of the preterm infant

    NARCIS (Netherlands)

    Jennekens, W.; Ruijs, L.S.; Lommen, Ch.M.L.; Niemarkt, H.J.; Pasman, J.W.; van Kranen-Mastenbroek, V.H.J.M.; Wijn, P.F.F.; van Pul, C.; Andriessen, P.

    2011-01-01

    To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological

  13. A systematic study of head tissue inhomogeneity and anisotropy on EEG forward problem computing

    International Nuclear Information System (INIS)

    Bashar, M.R.; Li, Y.; Wen, P.

    2010-01-01

    Full text: In this study, we propose a stochastic method to analyze the effects of inhomogeneous anisotropic tissue conductivity on electroencephalogram (EEG) in forward computation. We apply this method to an inhomogeneous and anisotropic spherical human head model. We apply stochastic finite element method based on Legendre polynomials, Karhunen-Loeve expansion and stochastic Galerkin methods. We apply Volume and Wang's constraints to restrict the anisotropic conductivities for both the white matter (WM) and the skull tissue compartments. The EEGs resulting from deterministic and stochastic FEMs are compared using statistical measurement techniques. Based on these comparisons, we find that EEGs generated by incorporating WM and skull inhomogeneous anisotropic tissue properties individually result in an average of 56.5 and 57.5% relative errors, respectively. Incorporating these tissue properties for both layers together generate 43.5% average relative error. Inhomogeneous scalp tissue causes 27% average relative error and a full inhomogeneous anisotropic model brings in an average of 45.5% relative error. The study results demonstrate that the effects of inhomogeneous anisotropic tissue conductivity are significant on EEG.

  14. Quantitative EEG analysis in minimally conscious state patients during postural changes.

    Science.gov (United States)

    Greco, A; Carboncini, M C; Virgillito, A; Lanata, A; Valenza, G; Scilingo, E P

    2013-01-01

    Mobilization and postural changes of patients with cognitive impairment are standard clinical practices useful for both psychic and physical rehabilitation process. During this process, several physiological signals, such as Electroen-cephalogram (EEG), Electrocardiogram (ECG), Photopletysmography (PPG), Respiration activity (RESP), Electrodermal activity (EDA), are monitored and processed. In this paper we investigated how quantitative EEG (qEEG) changes with postural modifications in minimally conscious state patients. This study is quite novel and no similar experimental data can be found in the current literature, therefore, although results are very encouraging, a quantitative analysis of the cortical area activated in such postural changes still needs to be deeply investigated. More specifically, this paper shows EEG power spectra and brain symmetry index modifications during a verticalization procedure, from 0 to 60 degrees, of three patients in Minimally Consciousness State (MCS) with focused region of impairment. Experimental results show a significant increase of the power in β band (12 - 30 Hz), commonly associated to human alertness process, thus suggesting that mobilization and postural changes can have beneficial effects in MCS patients.

  15. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal

    Science.gov (United States)

    Cherkassky, Vladimir; Lee, Jieun; Veber, Brandon; Patterson, Edward E.; Brinkmann, Benjamin H.; Worrell, Gregory A.

    2017-01-01

    Objective This paper describes a data-analytic modeling approach for prediction of epileptic seizures from intracranial electroencephalogram (iEEG) recording of brain activity. Even though it is widely accepted that statistical characteristics of iEEG signal change prior to seizures, robust seizure prediction remains a challenging problem due to subject-specific nature of data-analytic modeling. Methods Our work emphasizes understanding of clinical considerations important for iEEG-based seizure prediction, and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during pre-processing and post-processing are considered and investigated for their effect on seizure prediction accuracy. Results Our empirical results show that the proposed SVM-based seizure prediction system can achieve robust prediction of preictal and interictal iEEG segments from dogs with epilepsy. The sensitivity is about 90–100%, and the false-positive rate is about 0–0.3 times per day. The results also suggest good prediction is subject-specific (dog or human), in agreement with earlier studies. Conclusion Good prediction performance is possible only if the training data contain sufficiently many seizure episodes, i.e., at least 5–7 seizures. Significance The proposed system uses subject-specific modeling and unbalanced training data. This system also utilizes three different time scales during training and testing stages. PMID:27362758

  16. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  17. A Case of Habitual Neck Compression Induced Electroencephalogram Abnormalities: Differentiating from Epileptic Seizures Using a Tc-99m HMPAO SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Hongyoon; Seo, Minseok; Lee, Hoyoung; Kim, Youngsoo; Yun, Changho; Kim, Sangeun; Park, Sungho [Seoul National Univ. Bundang Hospital, Seongnam (Korea, Republic of)

    2014-06-15

    Self-induced hypoxia has been reported particularly in adolescents, and it can result in neurological injury. Here, we present a case of electroencephalogram (EEG) abnormalities induced by habitual neck compression differentiated from epileptic seizures by Tc-99m HMPAO SPECT. A 19-year-old male was admitted for evaluation of recurrent generalized tonic-clonic seizures. No interictal EEG abnormality was detected; however, abnormal slow delta waves were found immediately after habitual right neck compression. To differentiate EEG abnormalities due to a hemodynamic deficit induced by habitual neck compression from an epileptic seizure, Tc-99m HMPAO SPECT was performed immediately after right carotid artery compression. Abnormal delta waves were triggered, and cerebral hypoperfusion in the right internal carotid artery territory was detected on Tc-99m HMPAO SPECT. The slow delta wave detected on the EEG resulted from the cerebral hypoperfusion because of the habitual neck compression.

  18. Electroencephalogram in relation to brain glycogen level in irradiated rats treated with vitamin E as a radioprotective compound

    International Nuclear Information System (INIS)

    Mahdy, A.M.

    1992-01-01

    Whole body gamma irradiation of untreated rats at the dose of 7 Gy induced severe abnormalities in the brain electrical activity, electroencephalogram (EEG), patterns of both frontal and occipital cortical areas. The visual analysis of the frontal EEG records showed a significant shift of frequencies towards faster and higher voltage activity along the experiment period (first , third, seventh and tenth days post irradiation). However, an opposite picture was prominent on the occipital EEG records after irradiation. On the other hand,the level of brain glycogen, which is considered as an important energy source for brain functions, significantly increased at all intervals of post irradiation. The treatment of rats with intraperitoneal injection of vitamin E pre-irradiation succeeded in diminishing the deleterious abnormalities in the EEG records in both frontal and occipital areas as well as the changes induced in the level of brain glycogen after whole body gamma irradiation.4 fig

  19. A Case of Habitual Neck Compression Induced Electroencephalogram Abnormalities: Differentiating from Epileptic Seizures Using a Tc-99m HMPAO SPECT

    International Nuclear Information System (INIS)

    Choi, Hongyoon; Seo, Minseok; Lee, Hoyoung; Kim, Youngsoo; Yun, Changho; Kim, Sangeun; Park, Sungho

    2014-01-01

    Self-induced hypoxia has been reported particularly in adolescents, and it can result in neurological injury. Here, we present a case of electroencephalogram (EEG) abnormalities induced by habitual neck compression differentiated from epileptic seizures by Tc-99m HMPAO SPECT. A 19-year-old male was admitted for evaluation of recurrent generalized tonic-clonic seizures. No interictal EEG abnormality was detected; however, abnormal slow delta waves were found immediately after habitual right neck compression. To differentiate EEG abnormalities due to a hemodynamic deficit induced by habitual neck compression from an epileptic seizure, Tc-99m HMPAO SPECT was performed immediately after right carotid artery compression. Abnormal delta waves were triggered, and cerebral hypoperfusion in the right internal carotid artery territory was detected on Tc-99m HMPAO SPECT. The slow delta wave detected on the EEG resulted from the cerebral hypoperfusion because of the habitual neck compression

  20. Electroencephalogram abnormalities in full term infants with history of severe asphyxia

    Directory of Open Access Journals (Sweden)

    Susanti Halim

    2016-11-01

    Full Text Available Background An electroencephalogram (EEG is an electroimaging tool used to determine developmental and electrical problems in the brain. A history of severe asphyxia is a risk factor for these brain problems in infants. Objective To evaluate the prevalence of abnormal EEGs in full term neonates and to assess for an association with severe asphyxia, hypoxic ischemic encephalopathy (HIE, and spontaneous delivery. Methods This cross-sectional study was conducted at the Pediatric Outpatient Department of Sanglah Hospital, Denpasar, from November 2013 to January 2014. Subjects were fullterm infants aged 1 month who were delivered and/or hospitalized at Sanglah Hospital. All subjects underwent EEG. The EEGs were interpreted by a pediatric neurology consultant, twice, with a week interval between readings. Clinical data were obtained from medical records. Association between abnormal ECG and severe asphyxia were analyzed by Chi-square and multivariable logistic analyses. Results Of 55 subjects, 27 had a history of severe asphyxia and 28 were vigorous babies. Forty percent (22/55 of subjects had abnormal EEG findings, 19/22 of these subjects having history of severe asphyxia, 15/22 had history of hypoxic-ischemic encephalopathy (HIE, and 20/22 were delievered vaginally. There were strong correlations between the prevalence of abnormal EEG and history of severe asphyxia, HIE, and spontaneous delivery. Conclusion Prevalence of abnormal EEG among full-term neonates referred to neurology/growth development clinic is around 40%, with most of them having a history of severe asphyxia. Abnormal EEG is significantly associated to severe asphyxia, HIE, and spontaneous delivery.

  1. Alteration in Memory and Electroencephalogram Waves with Sub-acute Noise Stress in Albino Rats and Safeguarded by Scoparia dulcis

    OpenAIRE

    Loganathan, Sundareswaran; Rathinasamy, Sheeladevi

    2016-01-01

    Background: Noise stress has different effects on memory and novelty and the link between them with an electroencephalogram (EEG) has not yet been reported. Objective: To find the effect of sub-acute noise stress on the memory and novelty along with EEG and neurotransmitter changes. Materials and Methods: Eight-arm maze (EAM) and Y-maze to analyze the memory and novelty by novel object test. Four groups of rats were used: Control, control treated with Scoparia dulcis extract, noise exposed, a...

  2. Aesthetic preference recognition of 3D shapes using EEG.

    Science.gov (United States)

    Chew, Lin Hou; Teo, Jason; Mountstephens, James

    2016-04-01

    Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.

  3. LORETA EEG phase reset of the default mode network.

    Science.gov (United States)

    Thatcher, Robert W; North, Duane M; Biver, Carl J

    2014-01-01

    The purpose of this study was to explore phase reset of 3-dimensional current sources in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodmann areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st and 2nd derivatives of the time series of phase differences. Phase shift duration exhibited three discrete modes at approximately: (1) 25 ms, (2) 50 ms, and (3) 65 ms. Phase lock duration present primarily at: (1) 300-350 ms and (2) 350-450 ms. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a "shutter" that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

  4. Power of theta waves in the EEG of human subjects increases during recall of haptic information.

    Science.gov (United States)

    Grunwald, M; Weiss, T; Krause, W; Beyer, L; Rost, R; Gutberlet, I; Gertz, H J

    1999-02-05

    Several studies have reported a functional relationship between spectral power within the theta-band of the EEG (theta-power) and memory load while processing visual or semantic information. We investigated theta power during the processing of different complex haptic stimuli using a delayed recall design. The haptic explorations consisted of palpating the structure of twelve sunken reliefs with closed eyes. Subjects had to reproduce each relief by drawing it 10 s after the end of the exploration. The relationship between mean theta power and mean exploration time was analysed using a regression model. A linear relationship was found between the exploration time and theta power over fronto-central regions (Fp1, Fp2, F3, F7, F8, Fz, C3) directly before the recall of the relief. This result is interpreted in favour of the hypothesis that fronto-central theta power of the EEG correlates with the load of working memory independent of stimulus modality.

  5. Enhancing early consolidation of human episodic memory by theta EEG neurofeedback.

    Science.gov (United States)

    Rozengurt, Roman; Shtoots, Limor; Sheriff, Aviv; Sadka, Ofir; Levy, Daniel A

    2017-11-01

    Consolidation of newly formed memories is readily disrupted, but can it be enhanced? Given the prominent role of hippocampal theta oscillations in memory formation and retrieval, we hypothesized that upregulating theta power during early stages of consolidation might benefit memory stability and persistence. We used EEG neurofeedback to enable participants to selectively increase theta power in their EEG spectra following episodic memory encoding, while other participants engaged in low beta-focused neurofeedback or passively viewed a neutral nature movie. Free recall assessments immediately following the interventions, 24h later and 7d later all indicated benefit to memory of theta neurofeedback, relative to low beta neurofeedback or passive movie-viewing control conditions. The degree of benefit to memory was correlated with the extent of theta power modulation, but not with other spectral changes. Theta enhancement may provide optimal conditions for stabilization of new hippocampus-dependent memories. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Detection of focal epileptic activity using combined simultaneous electroencephalogram-functional MRI

    International Nuclear Information System (INIS)

    Zhang Zhiqiang; Lu Guangming; Tian Lei; Sun Kanjian; Tan Qifu; Zhu Jianguo; Nie Cong; Hao Shaowei; Jiang Li; Liu Yijun

    2007-01-01

    Objective: To observe the brain activation of interictal epiletiform discharges (IEDs) and to localize the epileptogenic foci of epilepsy. Methods: The electroencephalogram (EEG) and functional MRI data of 12 focal epileptic patients were acquired using a combination of EEG and functional MRI simultaneously. The IEDs onset time detected with EEG were set as the time parameters in an event- related paradigm of functional MRI analysis. The spatial and temporal characters of IEDs activation were analyzed in detail. In order to confirm the consistency of this method, all patients were scanned repeatedly and the results were correlated with clinical evaluation. Results: Of the 12 patients, valid data from EEG- fMRI were obtained from 10 patients in a total of 18 sessions. Compared with the structural foci, the epileptic foci localization results of eleven sessions were good, five sessions were fairly good, and two sessions were poor. The results obtained from six patients in two separate sessions were concordant, respectively. Moreover, thalamic activation was detected in ten sessions, cerebellar activation was detected in all sessions, and the deactivation was found in the default mode loci in nine sessions. Conclusion: The method of performing EEG and fMRI simultaneously can potentially be a useful tool in epilepsy research. (authors)

  7. Long-range correlations of electroencephalogram in rats irradiated by millimeter wave

    International Nuclear Information System (INIS)

    Xie Taorong; Pei Jian; Li Fen; Zhang Jie; Qi Hongxing; Chen Shude; Qiao Dengjiang

    2011-01-01

    A quantitative study was conducted on stress reaction in rat induced by 35 GHz millimeter wave. Long-range correlations analysis of the rat electroencephalogram(EEG) was investigated. The scaling exponents α 1 and α 2 were calculated by de-trended fluctuation analysis (DFA) method. The exponent α 1 shows that the high frequency EEG component is characterized by Brownian noise before irradiated by 35 GHz millimeter wave while it has long-range correlations during irradiation. The exponent α 2 shows that the low frequency EEG component has long-range correlations before irradiation while it is characterized by Brownian noise during irradiation. Introducing stress parameter k(k=α 2 /α 1 ), the average change rate of k was used to evaluate the intensity of stress in rat evoked by 35 GHz millimeter wave. The k increases 49.9%±13.6% during irradiation, which indicates that the high frequency EEG component becomes more ordered and the low frequency EEG component becomes more disordered, showing the acute stress in rat induced by 35 GHz millimeter wave. (authors)

  8. Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

    Science.gov (United States)

    Mahmoudi, Babak; Erfanian, Abbas

    2006-11-01

    Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

  9. Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans.

    Directory of Open Access Journals (Sweden)

    Lisa Marshall

    Full Text Available Previously the application of a weak electric anodal current oscillating with a frequency of the sleep slow oscillation (∼0.75 Hz during non-rapid eye movement sleep (NonREM sleep boosted endogenous slow oscillation activity and enhanced sleep-associated memory consolidation. The slow oscillations occurring during NonREM sleep and theta oscillations present during REM sleep have been considered of critical relevance for memory formation. Here transcranial direct current stimulation (tDCS oscillating at 5 Hz, i.e., within the theta frequency range (theta-tDCS is applied during NonREM and REM sleep. Theta-tDCS during NonREM sleep produced a global decrease in slow oscillatory activity conjoint with a local reduction of frontal slow EEG spindle power (8-12 Hz and a decrement in consolidation of declarative memory, underlining the relevance of these cortical oscillations for sleep-dependent memory consolidation. In contrast, during REM sleep theta-tDCS appears to increase global gamma (25-45 Hz activity, indicating a clear brain state-dependency of theta-tDCS. More generally, results demonstrate the suitability of oscillating-tDCS as a tool to analyze functions of endogenous EEG rhythms and underlying endogenous electric fields as well as the interactions between EEG rhythms of different frequencies.

  10. Quantitative topographic differentiation of the neonatal EEG.

    Science.gov (United States)

    Paul, Karel; Krajca, Vladimír; Roth, Zdenek; Melichar, Jan; Petránek, Svojmil

    2006-09-01

    To test the discriminatory topographic potential of a new method of the automatic EEG analysis in neonates. A quantitative description of the neonatal EEG can contribute to the objective assessment of the functional state of the brain, and may improve the precision of diagnosing cerebral dysfunctions manifested by 'disorganization', 'dysrhythmia' or 'dysmaturity'. 21 healthy, full-term newborns were examined polygraphically during sleep (EEG-8 referential derivations, respiration, ECG, EOG, EMG). From each EEG record, two 5-min samples (one from the middle of quiet sleep, the other from the middle of active sleep) were subject to subsequent automatic analysis and were described by 13 variables: spectral features and features describing shape and variability of the signal. The data from individual infants were averaged and the number of variables was reduced by factor analysis. All factors identified by factor analysis were statistically significantly influenced by the location of derivation. A large number of statistically significant differences were also established when comparing the effects of individual derivations on each of the 13 measured variables. Both spectral features and features describing shape and variability of the signal are largely accountable for the topographic differentiation of the neonatal EEG. The presented method of the automatic EEG analysis is capable to assess the topographic characteristics of the neonatal EEG, and it is adequately sensitive and describes the neonatal electroencephalogram with sufficient precision. The discriminatory capability of the used method represents a promise for their application in the clinical practice.

  11. ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition.

    Science.gov (United States)

    Zhang, Jianhai; Chen, Ming; Zhao, Shaokai; Hu, Sanqing; Shi, Zhiguo; Cao, Yu

    2016-09-22

    Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels' weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the

  12. ReliefF-Based EEG Sensor Selection Methods for Emotion Recognition

    Directory of Open Access Journals (Sweden)

    Jianhai Zhang

    2016-09-01

    Full Text Available Electroencephalogram (EEG signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP. Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation. Furthermore, support vector machine (SVM was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels’ weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels. In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a

  13. EEG Artifact Removal Using a Wavelet Neural Network

    Science.gov (United States)

    Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom

    2011-01-01

    !n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

  14. Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model.

    Science.gov (United States)

    Babiloni, Claudio; Babiloni, Fabio; Carducci, Filippo; Cappa, Stefano; Cincotti, Febo; Del Percio, Claudio; Miniussi, Carlo; Moretti, Davide Vito; Pasqualetti, Patrizio; Rossi, Simone; Sosta, Katiuscia; Rossini, Paolo Maria

    2004-04-01

    Many recent neuroimaging studies of episodic memory have indicated an asymmetry in prefrontal involvement, with the left prefrontal cortex more involved than the right in encoding, the right more than the left in retrieval (hemispheric encoding and retrieval asymmetry, or HERA model). In this electroencephalographic (EEG) high-resolution study, we studied brain rhythmicity during a visual episodic memory (recognition) task. The theta (4-6 Hz), alpha (6-12 Hz) and gamma (28-48 Hz) oscillations were investigated during a visuospatial long-term episodic memory task including an encoding (ENC) and retrieval (RET) phases. During the ENC phase, 25 figures representing interiors of buildings ("indoor") were randomly intermingled with 25 figures representing landscapes ("landscapes"). Subject's response was given at left ("indoor") or right ("landscapes") mouse button. During the RET phase (1 h later), 25 figures representing previously presented "indoor" pictures ("tests") were randomly intermingled with 25 figures representing novel "indoor" ("distractors"). Again, a mouse response was required. Theta and alpha EEG results showed no change of frontal rhythmicity. In contrast, the HERA prediction of asymmetry was fitted only by EEG gamma responses, but only in the posterior parietal areas. The ENC phase was associated with gamma EEG oscillations over left parietal cortex. Afterward, the RET phase was associated with gamma EEG oscillations predominantly over right parietal cortex. The predicted HERA asymmetry was thus observed in an unexpected location. This discrepancy may be due to the differential sensitivity of neuroimaging methods to selected components of cognitive processing. The strict relation between gamma response and perception suggests that retrieval processes of long-term memory deeply impinged upon sensory representation of the stored material.

  15. Effects of A 60 Hz Magnetic Field of Up to 50 milliTesla on Human Tremor and EEG: A Pilot Study.

    Science.gov (United States)

    Davarpanah Jazi, Shirin; Modolo, Julien; Baker, Cadence; Villard, Sebastien; Legros, Alexandre

    2017-11-24

    Humans are surrounded by sources of daily exposure to power-frequency (60 Hz in North America) magnetic fields (MFs). Such time-varying MFs induce electric fields and currents in living structures which possibly lead to biological effects. The present pilot study examined possible extremely low frequency (ELF) MF effects on human neuromotor control in general, and physiological postural tremor and electroencephalography (EEG) in particular. Since the EEG cortical mu-rhythm (8-12 Hz) from the primary motor cortex and physiological tremor are related, it was hypothesized that a 60 Hz MF exposure focused on this cortical region could acutely modulate human physiological tremor. Ten healthy volunteers (age: 23.8 ± 4 SD) were fitted with a MRI-compatible EEG cap while exposed to 11 MF conditions (60 Hz, 0 to 50 mT rms , 5 mT rms increments). Simultaneously, physiological tremor (recorded from the contralateral index finger) and EEG (from associated motor and somatosensory brain regions) were measured. Results showed no significant main effect of MF exposure conditions on any of the analyzed physiological tremor characteristics. In terms of EEG, no significant effects of the MF were observed for C1, C3, C5 and CP1 electrodes. However, a significant main effect was found for CP3 and CP5 electrodes, both suggesting a decreased mu-rhythm spectral power with increasing MF flux density. This is however not confirmed by Bonferroni corrected pairwise comparisons. Considering both EEG and tremor findings, no effect of the MF exposure on human motor control was observed. However, MF exposure had a subtle effect on the mu-rhythm amplitude in the brain region involved in tactile perception. Current findings are to be considered with caution due to the small size of this pilot work, but they provide preliminary insights to international agencies establishing guidelines regarding electromagnetic field exposure with new experimental data acquired in humans exposed to high m

  16. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI.

    Science.gov (United States)

    Arichi, Tomoki; Whitehead, Kimberley; Barone, Giovanni; Pressler, Ronit; Padormo, Francesco; Edwards, A David; Fabrizi, Lorenzo

    2017-09-12

    Electroencephalographic recordings from the developing human brain are characterized by spontaneous neuronal bursts, the most common of which is the delta brush. Although similar events in animal models are known to occur in areas of immature cortex and drive their development, their origin in humans has not yet been identified. Here, we use simultaneous EEG-fMRI to localise the source of delta brush events in 10 preterm infants aged 32-36 postmenstrual weeks. The most frequent patterns were left and right posterior-temporal delta brushes which were associated in the left hemisphere with ipsilateral BOLD activation in the insula only; and in the right hemisphere in both the insular and temporal cortices. This direct measure of neural and hemodynamic activity shows that the insula, one of the most densely connected hubs in the developing cortex, is a major source of the transient bursting events that are critical for brain maturation.

  17. Detection of movement intention using EEG in a human-robot interaction environment

    Directory of Open Access Journals (Sweden)

    Ernesto Pablo Lana

    Full Text Available Introduction : This paper presents a detection method for upper limb movement intention as part of a brain-machine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm’s flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.

  18. Linear and non-linear interdependence of EEG and HRV frequency bands in human sleep.

    Science.gov (United States)

    Chaparro-Vargas, Ramiro; Dissanayaka, P Chamila; Patti, Chanakya Reddy; Schilling, Claudia; Schredl, Michael; Cvetkovic, Dean

    2014-01-01

    The characterisation of functional interdependencies of the autonomic nervous system (ANS) stands an evergrowing interest to unveil electroencephalographic (EEG) and Heart Rate Variability (HRV) interactions. This paper presents a biosignal processing approach as a supportive computational resource in the estimation of sleep dynamics. The application of linear, non-linear methods and statistical tests upon 10 overnight polysomnographic (PSG) recordings, allowed the computation of wavelet coherence and phase locking values, in order to identify discerning features amongst the clinical healthy subjects. Our findings showed that neuronal oscillations θ, α and σ interact with cardiac power bands at mid-to-high rank of coherence and phase locking, particularly during NREM sleep stages.

  19. EEG-guided meditation: A personalized approach.

    Science.gov (United States)

    Fingelkurts, Andrew A; Fingelkurts, Alexander A; Kallio-Tamminen, Tarja

    2015-12-01

    The therapeutic potential of meditation for physical and mental well-being is well documented, however the possibility of adverse effects warrants further discussion of the suitability of any particular meditation practice for every given participant. This concern highlights the need for a personalized approach in the meditation practice adjusted for a concrete individual. This can be done by using an objective screening procedure that detects the weak and strong cognitive skills in brain function, thus helping design a tailored meditation training protocol. Quantitative electroencephalogram (qEEG) is a suitable tool that allows identification of individual neurophysiological types. Using qEEG screening can aid developing a meditation training program that maximizes results and minimizes risk of potential negative effects. This brief theoretical-conceptual review provides a discussion of the problem and presents some illustrative results on the usage of qEEG screening for the guidance of mediation personalization. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. Electroencephalogram-based methodology for determining unconsciousness during depopulation.

    Science.gov (United States)

    Benson, E R; Alphin, R L; Rankin, M K; Caputo, M P; Johnson, A L

    2012-12-01

    When an avian influenza or virulent Newcastle disease outbreak occurs within commercial poultry, key steps involved in managing a fast-moving poultry disease can include: education; biosecurity; diagnostics and surveillance; quarantine; elimination of infected poultry through depopulation or culling, disposal, and disinfection; and decreasing host susceptibility. Available mass emergency depopulation procedures include whole-house gassing, partial-house gassing, containerized gassing, and water-based foam. To evaluate potential depopulation methods, it is often necessary to determine the time to the loss of consciousness (LOC) in poultry. Many current approaches to evaluating LOC are qualitative and require visual observation of the birds. This study outlines an electroencephalogram (EEG) frequency domain-based approach for determining the point at which a bird loses consciousness. In this study, commercial broilers were used to develop the methodology, and the methodology was validated with layer hens. In total, 42 data sets from 13 broilers aged 5-10 wk and 12 data sets from four spent hens (age greater than 1 yr) were collected and analyzed. A wireless EEG transmitter was surgically implanted, and each bird was monitored during individual treatment with isoflurane anesthesia. EEG data were evaluated using a frequency-based approach. The alpha/delta (A/D, alpha: 8-12 Hz, delta: 0.5-4 Hz) ratio and loss of posture (LOP) were used to determine the point at which the birds became unconscious. Unconsciousness, regardless of the method of induction, causes suppression in alpha and a rise in the delta frequency component, and this change is used to determine unconsciousness. There was no statistically significant difference between time to unconsciousness as measured by A/D ratio or LOP, and the A/D values were correlated at the times of unconsciousness. The correlation between LOP and A/D ratio indicates that the methodology is appropriate for determining

  1. Sleep EEG of Microcephaly in Zika Outbreak.

    Science.gov (United States)

    Kanda, Paulo Afonso Medeiros; Aguiar, Aline de Almeida Xavier; Miranda, Jose Lucivan; Falcao, Alexandre Loverde; Andrade, Claudia Suenia; Reis, Luigi Neves Dos Santos; Almeida, Ellen White R Bacelar; Bello, Yanes Brum; Monfredinho, Arthur; Kanda, Rafael Guimaraes

    2018-01-01

    Microcephaly (MC), previously considered rare, is now a health emergency of international concern because of the devastating Zika virus pandemic outbreak of 2015. The authors describe the electroencephalogram (EEG) findings in sleep EEG of epileptic children who were born with microcephaly in areas of Brazil with active Zika virus transmission between 2014 and 2017. The authors reviewed EEGs from 23 children. Nine were females (39.2%), and the age distribution varied from 4 to 48 months. MC was associated with mother positive serology to toxoplasmosis (toxo), rubella (rub), herpes, and dengue (1 case); toxo (1 case); chikungunya virus (CHIKV) (1 case); syphilis (1 case); and Zika virus (ZIKV) (10 cases). In addition, 1 case was associated with perinatal hypoxia and causes of 9 cases remain unknown. The main background EEG abnormality was diffuse slowing (10 cases), followed by classic (3 cases) and modified (5 cases) hypsarrhythmia. A distinct EEG pattern was seen in ZIKV (5 cases), toxo (2 cases), and undetermined cause (1 case). It was characterized by runs of frontocentrotemporal 4.5-13 Hz activity (7 cases) or diffuse and bilateral runs of 18-24 Hz (1 case). In ZIKV, this rhythmic activity was associated with hypsarrhythmia or slow background. Further studies are necessary to determine if this association is suggestive of ZIKV infection. The authors believe that EEG should be included in the investigation of all newly diagnosed congenital MC, especially those occurring in areas of autochthonous transmission of ZIKV.

  2. Brain damage and addictive behavior: a neuropsychological and electroencephalogram investigation with pathologic gamblers.

    Science.gov (United States)

    Regard, Marianne; Knoch, Daria; Gütling, Eva; Landis, Theodor

    2003-03-01

    Gambling is a form of nonsubstance addiction classified as an impulse control disorder. Pathologic gamblers are considered healthy with respect to their cognitive status. Lesions of the frontolimbic systems, mostly of the right hemisphere, are associated with addictive behavior. Because gamblers are not regarded as "brain-lesioned" and gambling is nontoxic, gambling is a model to test whether addicted "healthy" people are relatively impaired in frontolimbic neuropsychological functions. Twenty-one nonsubstance dependent gamblers and nineteen healthy subjects underwent a behavioral neurologic interview centered on incidence, origin, and symptoms of possible brain damage, a neuropsychological examination, and an electroencephalogram. Seventeen gamblers (81%) had a positive medical history for brain damage (mainly traumatic head injury, pre- or perinatal complications). The gamblers, compared with the controls, were significantly more impaired in concentration, memory, and executive functions, and evidenced a higher prevalence of non-right-handedness (43%) and, non-left-hemisphere language dominance (52%). Electroencephalogram (EEG) revealed dysfunctional activity in 65% of the gamblers, compared with 26% of controls. This study shows that the "healthy" gamblers are indeed brain-damaged. Compared with a matched control population, pathologic gamblers evidenced more brain injuries, more fronto-temporo-limbic neuropsychological dysfunctions and more EEG abnormalities. The authors thus conjecture that addictive gambling may be a consequence of brain damage, especially of the frontolimbic systems, a finding that may well have medicolegal consequences.

  3. An exploratory data analysis of electroencephalograms using the functional boxplots approach

    KAUST Repository

    Ngo, Duy

    2015-08-19

    Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.

  4. An exploratory data analysis of electroencephalograms using the functional boxplots approach

    Science.gov (United States)

    Ngo, Duy; Sun, Ying; Genton, Marc G.; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando

    2015-01-01

    Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam. PMID:26347598

  5. An exploratory data analysis of electroencephalograms using the functional boxplots approach

    KAUST Repository

    Ngo, Duy; Sun, Ying; Genton, Marc G.; Wu, Jennifer; Srinivasan, Ramesh; Cramer, Steven C.; Ombao, Hernando

    2015-01-01

    Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.

  6. Topographic distribution of EEG alpha attractor correlation dimension values in wake and drowsy states in humans.

    Science.gov (United States)

    Kalauzi, Aleksandar; Vuckovic, Aleksandra; Bojić, Tijana

    2015-03-01

    Organization of resting state cortical networks is of fundamental importance for the phenomenon of awareness, which is altered in the first part of hypnagogic period (Hori stages 1-4). Our aim was to investigate the change in brain topography pattern of EEG alpha attractor correlation dimension (CD) in the period of transition from Hori stage 1 to 4. EEG of ten healthy adult individuals was recorded in the wake and drowsy states, using a 14 channel average reference montage, from which 91 bipolar channels were derived and filtered in the wider alpha (6-14 Hz) range. Sixty 1s long epochs of each state and individual were subjected to CD calculation according to the Grassberger-Procaccia method. For such a collection of signals, two embedding dimensions, d={5, 10}, and 22 time delays τ=2-23 samples were explored. Optimal values were d=10 and τ=18, where both saturation and second zero crossing of the autocorrelation function occurred. Bipolar channel CD underwent a significant decrease during the transition and showed a positive linear correlation with electrode distance, stronger in the wake individuals. Topographic distribution of bipolar channels with above median CD changed from longitudinal anterior-posterior pattern (awake) to a more diagonal pattern, with localization in posterior regions (drowsiness). Our data are in line with the literature reporting functional segregation of neuronal assemblies in anterior and posterior regions during this transition. Our results should contribute to understanding of complex reorganization of the cortical part of alpha generators during the wake/drowsy transition. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

    Directory of Open Access Journals (Sweden)

    George J. A. Jiang

    2015-01-01

    Full Text Available Electroencephalogram (EEG signals, as it can express the human brain’s activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA. Bispectral (BIS index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD method and analyzed using sample entropy (SampEn analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN model through using expert assessment of consciousness level (EACL which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.

  8. Narrow band quantitative and multivariate electroencephalogram analysis of peri-adolescent period.

    Science.gov (United States)

    Martinez, E I Rodríguez; Barriga-Paulino, C I; Zapata, M I; Chinchilla, C; López-Jiménez, A M; Gómez, C M

    2012-08-24

    The peri-adolescent period is a crucial developmental moment of transition from childhood to emergent adulthood. The present report analyses the differences in Power Spectrum (PS) of the Electroencephalogram (EEG) between late childhood (24 children between 8 and 13 years old) and young adulthood (24 young adults between 18 and 23 years old). The narrow band analysis of the Electroencephalogram was computed in the frequency range of 0-20 Hz. The analysis of mean and variance suggested that six frequency ranges presented a different rate of maturation at these ages, namely: low delta, delta-theta, low alpha, high alpha, low beta and high beta. For most of these bands the maturation seems to occur later in anterior sites than posterior sites. Correlational analysis showed a lower pattern of correlation between different frequencies in children than in young adults, suggesting a certain asynchrony in the maturation of different rhythms. The topographical analysis revealed similar topographies of the different rhythms in children and young adults. Principal Component Analysis (PCA) demonstrated the same internal structure for the Electroencephalogram of both age groups. Principal Component Analysis allowed to separate four subcomponents in the alpha range. All these subcomponents peaked at a lower frequency in children than in young adults. The present approaches complement and solve some of the incertitudes when the classical brain broad rhythm analysis is applied. Children have a higher absolute power than young adults for frequency ranges between 0-20 Hz, the correlation of Power Spectrum (PS) with age and the variance age comparison showed that there are six ranges of frequencies that can distinguish the level of EEG maturation in children and adults. The establishment of maturational order of different frequencies and its possible maturational interdependence would require a complete series including all the different ages.

  9. Stroop color-word interference and electroencephalogram activation: evidence for age-related decline of the anterior attention system.

    Science.gov (United States)

    West, R; Bell, M A

    1997-07-01

    Groups of healthy, community-dwelling younger and older adults performed a Stroop task in which color and word could be congruent or incongruent and spatially integrated or separated. During the task, continuous electroencephalogram (EEG) was recorded from frontal, parietal, and occipital regions. The magnitude of the Stroop interference effect and task-related EEG activation was greater for older than younger adults when stimuli were integrated. This effect was significant over medial and lateral frontal and parietal, but not occipital, regions. In comparison, interference and EEG activation did not differ for younger and older adults when stimuli were separated. These findings support the hypothesis that the anterior attention system is more sensitive to the effects of increasing age than the posterior attention system.

  10. Study of EEG during Sternberg Tasks with Different Direction of Arrangement for Letters

    Science.gov (United States)

    Kamihoriuchi, Kenji; Nuruki, Atsuo; Matae, Tadashi; Kurono, Asutsugu; Yunokuchi, Kazutomo

    In previous study, we recorded electroencephalogram (EEG) of patients with dementia and healthy subjects during Sternberg task. But, only one presentation method of Sternberg task was considered in previous study. Therefore, we examined whether the EEG was different in two different presentation methods wrote letters horizontally and wrote letters vertically in this study. We recorded EEG of six healthy subjects during Sternberg task using two different presentation methods. The result was not different in EEG topography of all subjects. In all subjects, correct rate increased in case of vertically arranged letters.

  11. Quantitative EEG in assessment of anaesthetic depth: comparative study of methodology

    DEFF Research Database (Denmark)

    Thomsen, C. E.; Prior, P. F.

    1996-01-01

    ) and (4) a depth of anaesthesia monitor based on EEG pattern recognition (ADAM). Dose-response curves are presented for stepwise increases in stable end-tidal concentrations of each agent. Results indicated considerable inter-patient variability and the limitations of single EEG measures, particularly......Methodology for assessment of depth of anaesthesia based on analysis of the electroencephalogram (EEG) is controversial. Techniques range from display of single measures, for example median value of the frequency spectrum, to dedicated pattern recognition systems based on measures of several EEG...

  12. Changes in decibel scale wavelength properties of EEG with alertness levels while performing sustained attention tasks.

    Science.gov (United States)

    Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

    2009-01-01

    Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.

  13. Design of EEG Signal Acquisition System Using Arduino MEGA1280 and EEGAnalyzer

    Directory of Open Access Journals (Sweden)

    Saptono Debyo

    2016-01-01

    Full Text Available This study integrates the hardware circuit design and software development to achieve a 16 channels Electroencephalogram (EEG system for Brain Computer Interface (BCI applications. Signals obtained should be strong enough amplitude that is usually expressed in units of millivolts and reasonably clean of noise that appears when the data acquisition process. The process of data acquisition consists of two stages are the acquisition of the original EEG signal can be done by the active electrode with an instrumentation amplifier or a preamplifier and processing the signal to get better signals with improved signal quality by removing noise using filters with IC OPAMP. The design of a preamplifier with high common-mode rejection ratio and high signal-to-noise ratio is very important. Moreover, the friction between the electrode pads and the skin as well as the design of dual power supply. Designs used single-power AC-coupled circuit, which effectively reduces the DC bias and improves the error caused by the effects of part errors. At the same time, the digital way is applied to design the adjustable amplification and filter function, which can design for different EEG frequency bands. The next step, those EEG signals received by the microcontroller through a port Analog to Digital Converter (ADC that integrated and converted into digital signals and stored in the RAM of microcontroller which simultaneously at 16 ports in accordance with the minimal number of points of data collection on the human scalp. Implementation results have shown the series of acquisitions to work properly so that it can be displayed EEG signals via software EEGAnalyzer.

  14. Sedation with dexmedetomidine for conducting electroencephalogram in a patient with Angelman syndrome: a case report

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    Magda Lourenço Fernandes

    2016-03-01

    Full Text Available Introduction: Angelman syndrome is characterized by severe mental retardation and speech and seizure disorders. This rare genetic condition is associated with changes in GABAA receptor. Patients with Angelman syndrome need to be sedated during an electroencephalogram ordered for diagnostic purposes or evolutionary control. Dexmedetomidine, whose action is independent of GABA receptor, promotes a sleep similar to physiological sleep and can facilitate the performing of this examination in patients with Angelman syndrome. Case report: Female patient, 14 years old, with Angelman syndrome; electroencephalogram done under sedation with dexmedetomidine. The procedure was uneventful and bradycardia or respiratory depression was not recorded. The examination was successfully interpreted and epileptiform activity was not observed. Conclusion: Dexmedetomidine promoted satisfactory sedation, was well tolerated and enabled the interpretation of the electroencephalogram in a patient with Angelman syndrome and seizure disorder. Resumo: Introdução: A síndrome de Angelman (SA é caracterizada por retardo mental grave, distúrbio da fala e desordem convulsiva. Essa condição genética rara está associada a alterções do receptor GABAA. Pacientes portadores de SA necessitam ser sedados durante a feitura de eletroencefalograma (EEG, indicado para fins diagnósticos ou controle evolutivo. A dexmedetomidina, cuja ação independe do receptor GABA, promove sono semelhante ao fisiológico e pode viabilizar a feitura desse exame em pacientes com SA. Relato de caso: Paciente feminina, 14 anos, portadora de SA, fez EEG sob sedação com dexmedetomidina. O procedimento transcorreu sem intercorrências e não foi registrada bradicardia ou depressão respiratória. O exame foi interpretado com sucesso e atividade epileptiforme não foi observada. Conclusão: A dexmedetomidina promoveu sedação satisfatória, foi bem tolerada e possibilitou a interpretação do EEG em

  15. Human exposure to power frequency magnetic fields up to 7.6 mT: An integrated EEG/fMRI study.

    Science.gov (United States)

    Modolo, Julien; Thomas, Alex W; Legros, Alexandre

    2017-09-01

    We assessed the effects of power-line frequency (60 Hz in North America) magnetic fields (MF) in humans using simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Twenty-five participants were enrolled in a pseudo-double-blind experiment involving "real" or "sham" exposure to sinusoidal 60 Hz MF exposures delivered using the gradient coil of an MRI scanner following two conditions: (i) 10 s exposures at 3 mT (10 repetitions); (ii) 2 s exposures at 7.6 mT (100 repetitions). Occipital EEG spectral power was computed in the alpha range (8-12 Hz, reportedly the most sensitive to MF exposure in the literature) with/without exposure. Brain functional activation was studied using fMRI blood oxygen level-dependent (BOLD, inversely correlated with EEG alpha power) maps. No significant effects were detected on occipital EEG alpha power during or post-exposure for any exposure condition. Consistent with EEG results, no effects were observed on fMRI BOLD maps in any brain region. Our results suggest that acute exposure (2-10 s) to 60 Hz MF from 3 to 7.6 mT (30,000 to 76,000 times higher than average public exposure levels for 60 Hz MF) does not induce detectable changes in EEG or BOLD signals. Combined with previous findings in which effects were observed on the BOLD signal after 1 h exposure to 3 mT, 60 Hz MF, this suggests that MF exposure in the low mT range (<10 mT) might require prolonged durations of exposure to induce detectable effects. Bioelectromagnetics. 38:425-435, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  16. Study of heart-brain interactions through EEG, ECG, and emotions

    Science.gov (United States)

    Ramasamy, Mouli; Varadan, Vijay K.

    2017-04-01

    Neurocardiology is the exploration of neurophysiological, neurological and neuroanatomical facets of neuroscience's influence in cardiology. The paraphernalia of emotions on the heart and brain are premeditated because of the interaction between the central and peripheral nervous system. This is an investigative attempt to study emotion based neurocardiology and the factors that influence this phenomenon. The factors include: interaction between sleep EEG (electroencephalogram) and ECG (electrocardiogram), relationship between emotion and music, psychophysiological coherence between the heart and brain, emotion recognition techniques, and biofeedback mechanisms. Emotions contribute vitally to the mundane life and are quintessential to a numerous biological and everyday-functional modality of a human being. Emotions are best represented through EEG signals, and to a certain extent, can be observed through ECG and body temperature. Confluence of medical and engineering science has enabled the monitoring and discrimination of emotions influenced by happiness, anxiety, distress, excitement and several other factors that influence the thinking patterns and the electrical activity of the brain. Similarly, HRV (Heart Rate Variability) widely investigated for its provision and discerning characteristics towards EEG and the perception in neurocardiology.

  17. P2-15: EEG Analysis on Story Change in TV Drama

    Directory of Open Access Journals (Sweden)

    Chung-Yeon Lee

    2012-10-01

    Full Text Available The human brain naturally recognizes a change of environment or atmosphere without great effort, and this is essential for interactive communication in social life and a specific reaction in an emergency situation. Most studies have investigated change detection of the brain with conditional experimental paradigms rather than the performance of everyday tasks. However, naturally occurring sensory stimuli are multimodal and dynamic. In an effort to study the relationship between users' induced physiological responses and changes of environment and atmosphere under more naturalistic and ecological conditions, we performed a basic experiment using audio-visual movies and electroencephalogram (EEG measurement. 8 healthy subjects were asked to watch a television sitcom without any responses, and their EEG signals were recorded simultaneously with 126 electrodes mounted in an elastic electrode cap. Time-frequency analysis of EEG revealed distinctive neural oscillations at the point of story change in the movie. This result could be used for applications in brain-computer interfaces, and provides a reference to cognitive impairment studies such as Attention Deficit Disorder (ADD or Attention-Deficit/Hyperactivity Disorder (ADHD.

  18. EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

    Science.gov (United States)

    Kortelainen, Jukka; Seppänen, Tapio

    2013-01-01

    Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

  19. Electroencephalogram Power Density and Slow Wave Sleep as a Function of Prior Waking and Circadian Phase

    NARCIS (Netherlands)

    Dijk, Derk-Jan; Brunner, Daniel P.; Beersma, Domien G.M.; Borbély, Alexander A.

    1990-01-01

    Human sleep electroencephalograms, recorded in four experiments, were subjected to spectral analysis. Waking prior to sleep varied from 12 to 36 h and sleep was initiated at different circadian phases. Power density of delta and theta frequencies in rapid-eye-movement (REM) sleep and non-REM (NREM)

  20. Alzheimer's disease: relationship between cognitive aspects and power and coherence EEG measures

    Directory of Open Access Journals (Sweden)

    Lineu C. Fonseca

    2011-12-01

    Full Text Available OBJECTIVE: To evaluate the relationship between specific cognitive aspects and quantitative EEG measures, in patients with mild or moderate Alzheimer's disease (AD. METHOD: Thirty-eight AD patients and 31 controls were assessed by CERAD neuropsychological battery (Consortium to Establish a Registry for AD and the electroencephalogram (EEG. The absolute power and coherences EEG measures were calculated at rest. The correlations between the cognitive variables and the EEG were evaluated. RESULTS: In the AD group there were significant correlations between different coherence EEG measures and Mini-Mental State Examination, verbal fluency, modified Boston naming, word list memory with repetition, word list recall and recognition, and constructional praxis (p<0.01. These correlations were all negative for the delta and theta bands and positive for alpha and beta. There were no correlations between cognitive aspects and absolute EEG power. CONCLUSION: The coherence EEG measures reflect different forms in the relationship between regions related to various cognitive dysfunctions.

  1. [Specific language impairment and electroencephalogram: which recommendations in clinical practice? A cohort of 24 children].

    Science.gov (United States)

    Billard, C; Hassairi, I; Delteil, F

    2010-04-01

    Electroencephalographic recording (electroencephalogram [EEG]) is frequent in specific language impairment (SLI), whereas the relations between epileptiform activity (EA) and language disorders remain uncertain and the therapeutic approach undetermined. The aim of this prospective study was to clarify EEG indications and interpretation in SLI. We present a prospective study of cognitive (speech-language measures, psychological assessments) and electroencephalographic data on 24 children (20 males, 4 females; mean age: 4 years 5 months; range: 3 years to 4 years 8 months) with a diagnosis of SLI, defined as a pathologic score on at least 2 speech-language measures and IQ performance of at least 80 points, within epileptic seizures. All participants had an EEG after partial deprivation of sleep at night. When nonsporadic EA was found, 24-h EEG was performed. Antiepileptic treatment was prescribed depending on the frequency of discharges and the SLI profile. The follow-up lasted 2 years. All patients reached stage II sleep during their EEG. Seven children had abnormal electroencephalography results, including 5 children with EA. Two patients with mixed SLI prevailing on expression presented a left centrotemporal spike focus on EEG becoming subcontinuous during sleep. In the first case, the language progressed without antiepileptic treatment. The 2nd case was treated with ethosuximide; the EEG normalized on subsequent recordings, but the language disorder remained severe. The lexical and syntactic understanding and syntactic production scores were not different for children presented EA (5 cases) or without (19 cases) (Wilcoxon's test). Finally, the progression of the various linguistic skills was similar whether or not the children had EA (pactivity is more frequent in SLI than in normal children. It can be seen in all types of SLI but preferentially in the mixed forms. The longitudinal systematic evaluation of all the children with or without EA has never been

  2. Correlations between personality traits and specific groups of alpha waves in the human EEG

    Directory of Open Access Journals (Sweden)

    Tomas Johannisson

    2016-07-01

    Full Text Available Background. Different individuals have alpha waves with different wavelengths. The distribution of the wavelengths is assumed to be bell-shaped and smooth. Although this view is generally accepted, it is still just an assumption and has never been critically tested. When exploring the relationship between alpha waves and personality traits, it makes a huge difference if the distribution of the alpha waves is smooth or if specific groups of alpha waves can be demonstrated. Previous studies have not considered the possibility that specific groups of alpha waves may exist. Methods. Computerized EEGs have become standard, but wavelength measurements are problematic when based on averaging procedures using the Fourier transformation because such procedures cause a large systematic error. If the actual wavelength is of interest, it is necessary to go back to basic physiology and use raw EEG signals. In the present study, measurements were made directly from sequences of alpha waves where every wave could be identified. Personality dimensions were measured using an inventory derived from the International Personality Item Pool. Results. Recordings from 200 healthy individuals revealed that there are three main groups of alpha waves. These groups had frequencies around 8, 10, and 12 waves per second. The middle group had a bimodal distribution, and a subdivision gave a total of four alpha groups. In the center of each group, the degree of extraversion was high and the degree of neuroticism was low. Many small differences in personality traits were found when the centers were compared with one another. This gave four personality profiles that resemble the four classical temperaments. When people in the surrounding zones were compared with those in the centers, relatively large differences in personality traits were found. Conclusions. Specific groups of alpha waves exist, and these groups have to be taken into account when correlations are made to

  3. EEG patterns in theta and gamma frequency range and their probable relation to human voluntary movement organization.

    Science.gov (United States)

    Popivanov, D; Mineva, A; Krekule, I

    1999-05-21

    In experiments with EEG accompanying continuous slow goal-directed voluntary movements we found abrupt short-term transients (STs) of the coefficients of EEG time-varying autoregressive (TVAR) model. The onset of STs indicated (i) a positive EEG wave related to an increase of 3-7 Hz oscillations in time period before the movement start, (ii) synchronization of 35-40 Hz prior to movement start and during the movement when the target is nearly reached. Both these phenomena are expressed predominantly over supplementary motor area, premotor and parietal cortices. These patterns were detected after averaging of EEG segments synchronized to the abrupt changes of the TVAR coefficients computed in the time course of EEG single records. The results are discussed regarding the cognitive aspect of organization of goal-directed movements.

  4. Detrended cross-correlation analysis of electroencephalogram

    International Nuclear Information System (INIS)

    Wang Jun; Zhao Da-Qing

    2012-01-01

    In the paper we use detrended cross-correlation analysis (DCCA) to study the electroencephalograms of healthy young subjects and healthy old subjects. It is found that the cross-correlation between different leads of a healthy young subject is larger than that of a healthy old subject. It was shown that the cross-correlation relationship decreases with the aging process and the phenomenon can help to diagnose whether the subject's brain function is healthy or not. (interdisciplinary physics and related areas of science and technology)

  5. Use of Electroencephalography (EEG) to Assess CNS Changes Produced by Pesticides with different Modes of Action: Effects of Permethrin, Deltamethrin, Fipronil, Imidacloprid, Carbaryl, and Triadimefon

    Science.gov (United States)

    The electroencephalogram (EEG) is an apical measure, capable of detecting changes in brain neuronal activity produced by internal or external stimuli. We assessed whether pesticides with different modes of action produced different changes in the EEG of adult male Long-Evans rats...

  6. Electroencephalographic precursors of spike-wave discharges in a genetic rat model of absence epilepsy: Power spectrum and coherence EEG analyses

    NARCIS (Netherlands)

    Sitnikova, E.Y.; Luijtelaar, E.L.J.M. van

    2009-01-01

    Periods in the electroencephalogram (EEG) that immediately precede the onset of spontaneous spike-wave discharges (SWD) were examined in WAG/Rij rat model of absence epilepsy. Precursors of SWD (preSWD) were classified based on the distribution of EEG power in delta-theta-alpha frequency bands as

  7. New perspectives in EEG/MEG brain mapping and PET/fMRI neuroimaging of human pain.

    Science.gov (United States)

    Chen, A C

    2001-10-01

    With the maturation of EEG/MEG brain mapping and PET/fMRI neuroimaging in the 1990s, greater understanding of pain processing in the brain now elucidates and may even challenge the classical theory of pain mechanisms. This review scans across the cultural diversity of pain expression and modulation in man. It outlines the difficulties in defining and studying human pain. It then focuses on methods of studying the brain in experimental and clinical pain, the cohesive results of brain mapping and neuroimaging of noxious perception, the implication of pain research in understanding human consciousness and the relevance to clinical care as well as to the basic science of human psychophysiology. Non-invasive brain studies in man start to unveil the age-old puzzles of pain-illusion, hypnosis and placebo in pain modulation. The neurophysiological and neurohemodynamic brain measures of experimental pain can now largely satisfy the psychophysiologist's dream, unimaginable only a few years ago, of modelling the body-brain, brain-mind, mind-matter duality in an inter-linking 3-P triad: physics (stimulus energy); physiology (brain activities); and psyche (perception). For neuropsychophysiology greater challenges lie ahead: (a) how to integrate a cohesive theory of human pain in the brain; (b) what levels of analyses are necessary and sufficient; (c) what constitutes the structural organisation of the pain matrix; (d) what are the modes of processing among and across the sites of these structures; and (e) how can neural computation of these processes in the brain be carried out? We may envision that modular identification and delineation of the arousal-attention, emotion-motivation and perception-cognition neural networks of pain processing in the brain will also lead to deeper understanding of the human mind. Two foreseeable impacts on clinical sciences and basic theories from brain mapping/neuroimaging are the plausible central origin in persistent pain and integration of

  8. Joint Time-Frequency-Space Classification of EEG in a Brain-Computer Interface Application

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

    Full Text Available Brain-computer interface is a growing field of interest in human-computer interaction with diverse applications ranging from medicine to entertainment. In this paper, we present a system which allows for classification of mental tasks based on a joint time-frequency-space decorrelation, in which mental tasks are measured via electroencephalogram (EEG signals. The efficiency of this approach was evaluated by means of real-time experimentations on two subjects performing three different mental tasks. To do so, a number of protocols for visualization, as well as training with and without feedback, were also developed. Obtained results show that it is possible to obtain good classification of simple mental tasks, in view of command and control, after a relatively small amount of training, with accuracies around 80%, and in real time.

  9. Highly Efficient Compression Algorithms for Multichannel EEG.

    Science.gov (United States)

    Shaw, Laxmi; Rahman, Daleef; Routray, Aurobinda

    2018-05-01

    The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

  10. How does a surgeon’s brain buzz? An EEG coherence study on the interaction between humans and robot

    Science.gov (United States)

    2013-01-01

    Introduction In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery. Experimental procedures We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3. Results We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p with different approaches. To the best of our knowledge, this is the first study that tried to assess semi-quantitative differences during the interaction between normal human brain and robotic devices. PMID:23607324

  11. Differences between state entropy and bispectral index during analysis of identical electroencephalogram signals: a comparison with two randomised anaesthetic techniques.

    Science.gov (United States)

    Pilge, Stefanie; Kreuzer, Matthias; Karatchiviev, Veliko; Kochs, Eberhard F; Malcharek, Michael; Schneider, Gerhard

    2015-05-01

    It is claimed that bispectral index (BIS) and state entropy reflect an identical clinical spectrum, the hypnotic component of anaesthesia. So far, it is not known to what extent different devices display similar index values while processing identical electroencephalogram (EEG) signals. To compare BIS and state entropy during analysis of identical EEG data. Inspection of raw EEG input to detect potential causes of erroneous index calculation. Offline re-analysis of EEG data from a randomised, single-centre controlled trial using the Entropy Module and an Aspect A-2000 monitor. Klinikum rechts der Isar, Technische Universität München, Munich. Forty adult patients undergoing elective surgery under general anaesthesia. Blocked randomisation of 20 patients per anaesthetic group (sevoflurane/remifentanil or propofol/remifentanil). Isolated forearm technique for differentiation between consciousness and unconsciousness. Prediction probability (PK) of state entropy to discriminate consciousness from unconsciousness. Correlation and agreement between state entropy and BIS from deep to light hypnosis. Analysis of raw EEG compared with index values that are in conflict with clinical examination, with frequency measures (frequency bands/Spectral Edge Frequency 95) and visual inspection for physiological EEG patterns (e.g. beta or delta arousal), pathophysiological features such as high-frequency signals (electromyogram/high-frequency EEG or eye fluttering/saccades), different types of electro-oculogram or epileptiform EEG and technical artefacts. PK of state entropy was 0.80 and of BIS 0.84; correlation coefficient of state entropy with BIS 0.78. Nine percent BIS and 14% state entropy values disagreed with clinical examination. Highest incidence of disagreement occurred after state transitions, in particular for state entropy after loss of consciousness during sevoflurane anaesthesia. EEG sequences which led to false 'conscious' index values often showed high

  12. Analysis of cross-correlations in electroencephalogram signals as an approach to proactive diagnosis of schizophrenia

    Science.gov (United States)

    Timashev, Serge F.; Panischev, Oleg Yu.; Polyakov, Yuriy S.; Demin, Sergey A.; Kaplan, Alexander Ya.

    2012-02-01

    We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchronization, a phenomenon representing specific correlations between the characteristic frequencies and phases of excitations in the brain. We introduce quantitative measures of frequency-phase synchronization and systematize the values of FNS parameters for the EEG data. The comparison of our results with the medical diagnoses for 84 subjects performed at NCPH makes it possible to group the EEG signals into 4 categories corresponding to different risk levels of subjects' susceptibility to schizophrenia. We suggest that the introduced quantitative characteristics and classification of cross-correlations may be used for the diagnosis of schizophrenia at the early stages of its development.

  13. More attention, less deficit:wearable eeg-based serious game for focus improvement

    OpenAIRE

    Alchalabi, Alaa Eddin; Eddin, Amer Nour; Shirmohammadi, Shervin

    2017-01-01

    Attention Deficit Hyperactivity Disorder (ADHD) and Attention Deficit Disorders (ADD) are two of the most spread mental disorders characterized by the lack of attention and focus. One way to measure focus is through Electroencephalogram (EEG) signals that can be read using the new wireless EEG reading devices often used by Brain-computer Interface (BCI) researchers. In parallel, serious games have been recently providing opportunities for the rehabilitation of various cognitive and emotional ...

  14. Spectral electroencephalogram in liver cirrhosis with minimal hepatic encephalopathy before and after lactulose therapy.

    Science.gov (United States)

    Singh, Jatinderpal; Sharma, Barjesh Chander; Maharshi, Sudhir; Puri, Vinod; Srivastava, Siddharth

    2016-06-01

    Minimal hepatic encephalopathy (MHE) represents the mildest form of hepatic encephalopathy. Spectral electroencephalogram (sEEG) analysis improves the recognition of MHE by decreasing inter-operator variability and providing quantitative parameters of brain dysfunction. We compared sEEG in patients with cirrhosis with and without MHE and the effects of lactulose on sEEG in patients with MHE. One hundred patients with cirrhosis (50 with and 50 without MHE) were enrolled. Diagnosis of MHE was based on psychometric hepatic encephalopathy score (PHES) of ≤ -5. Critical flicker frequency, model of end-stage liver disease score, and sEEG were performed at baseline in all patients. The spectral variables considered were the mean dominant frequency (MDF) and relative power in beta, alpha, theta, and delta bands. Patients with MHE were given 3 months of lactulose, and all parameters were repeated. Spectral electroencephalogram analysis showed lower MDF (7.8 ± 1.7 vs 8.7 ± 1.3 Hz, P < 0.05) and higher theta relative power (34.29 ± 4.8 vs 24 ± 6.7%, P = 001) while lower alpha relative power (28.6 ± 4.0 vs 33.5 ± 5.3%, P = .001) in patients with MHE than in patients without MHE. With theta relative power, sensitivity 96%, specificity 84%, and accuracy of 90% were obtained for diagnosis of MHE. After lactulose treatment, MHE improved in 21 patients, and significant changes were seen in MDF (7.8 ± 0.5 vs 8.5 ± 0.6), theta (34.2 ± 4.8 vs 23.3 ± 4.1%), alpha (28.6 ± 4.0 vs 35.5 ± 4.5%), and delta relative power (13.7 ± 3.5 vs 17.0 ± 3.3%) after treatment (P ≤ 0.05). Spectral EEG is a useful objective and quantitative tool for diagnosis and to assess the response to treatment in patients with cirrhosis with MHE. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

  15. SVM detection of epileptiform activity in routine EEG.

    LENUS (Irish Health Repository)

    Kelleher, Daniel

    2010-01-01

    Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect of changing a range of input values to the detection system on its ability to distinguish between normal and abnormal EEG activity. It is shown that the length of analysis window in the range of 0.5s to 1s are well suited to the task. Additionally, it is reported that patient specific systems should be used where possible due to their better performance.

  16. EEG feature selection method based on decision tree.

    Science.gov (United States)

    Duan, Lijuan; Ge, Hui; Ma, Wei; Miao, Jun

    2015-01-01

    This paper aims to solve automated feature selection problem in brain computer interface (BCI). In order to automate feature selection process, we proposed a novel EEG feature selection method based on decision tree (DT). During the electroencephalogram (EEG) signal processing, a feature extraction method based on principle component analysis (PCA) was used, and the selection process based on decision tree was performed by searching the feature space and automatically selecting optimal features. Considering that EEG signals are a series of non-linear signals, a generalized linear classifier named support vector machine (SVM) was chosen. In order to test the validity of the proposed method, we applied the EEG feature selection method based on decision tree to BCI Competition II datasets Ia, and the experiment showed encouraging results.

  17. Diagnostic Role of ECG Recording Simultaneously With EEG Testing.

    Science.gov (United States)

    Kendirli, Mustafa Tansel; Aparci, Mustafa; Kendirli, Nurten; Tekeli, Hakan; Karaoglan, Mustafa; Senol, Mehmet Guney; Togrol, Erdem

    2015-07-01

    Arrhythmia is not uncommon in the etiology of syncope which mimics epilepsy. Data about the epilepsy induced vagal tonus abnormalities have being increasingly reported. So we aimed to evaluate what a neurologist may gain by a simultaneous electrocardiogram (ECG) and electroencephalogram (EEG) recording in the patients who underwent EEG testing due to prediagnosis of epilepsy. We retrospectively evaluated and detected ECG abnormalities in 68 (18%) of 376 patients who underwent EEG testing. A minimum of 20 of minutes artifact-free recording were required for each patient. Standard 1-channel ECG was simultaneously recorded in conjunction with the EEG. In all, 28% of females and 14% of males had ECG abnormalities. Females (mean age 49 years, range 18-88 years) were older compared with the male group (mean age 28 years, range 16-83 years). Atrial fibrillation was more frequent in female group whereas bradycardia and respiratory sinus arrhythmia was higher in male group. One case had been detected a critical asystole indicating sick sinus syndrome in the female group and treated with a pacemaker implantation in the following period. Simultaneous ECG recording in conjunction with EEG testing is a clinical prerequisite to detect and to clarify the coexisting ECG and EEG abnormalities and their clinical relevance. Potentially rare lethal causes of syncope that mimic seizure or those that could cause resistance to antiepileptic therapy could effectively be distinguished by detecting ECG abnormalities coinciding with the signs and abnormalities during EEG recording. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  18. The effect of CPAP treatment on EEG of OSAS patients.

    Science.gov (United States)

    Zhang, Cheng; Lv, Jun; Zhou, Junhong; Su, Li; Feng, Liping; Ma, Jing; Wang, Guangfa; Zhang, Jue

    2015-12-01

    Continuous positive airway pressure (CPAP) is currently the most effective treatment method for obstructive sleep apnea syndrome (OSAS). The purpose of this study was to compare the sleep electroencephalogram (EEG) changes before and after the application of CPAP to OSAS patients. A retrospective study was conducted and 45 sequential patients who received both polysomnography (PSG) and CPAP titration were included. The raw data of sleep EEG were extracted and analyzed by engineers using two main factors: fractal dimension (FD) and the zero-crossing rate of detrended FD (zDFD). FD was an effective indicator reflecting the EEG complexity and zDFD was useful to reflect the variability of the EEG complexity. The FD and zDFD indexes of sleep EEG of 45 OSAS patients before and after CPAP titration were analyzed. The age of 45 OSAS patients was 52.7 ± 5.6 years old and the patients include 12 females and 33 males. After CPAP treatment, FD of EEG in non-rapid eye movement (NREM) sleep decreased significantly (P CPAP therapy (P CPAP therapy had a significant influence on sleep EEG in patients with OSAHS, which lead to a more stable EEG pattern. This may be one of the mechanisms that CPAP could improve sleep quality and brain function of OSAS patients.

  19. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  20. Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

    Science.gov (United States)

    Omurtag, Ahmet; Aghajani, Haleh; Onur Keles, Hasan

    2017-12-01

    Objective. Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system’s ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS’s decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

  1. Adaptation in human somatosensory cortex as a model of sensory memory construction: a study using high-density EEG.

    Science.gov (United States)

    Bradley, Claire; Joyce, Niamh; Garcia-Larrea, Luis

    2016-01-01

    Adaptation in sensory cortices has been seen as a mechanism allowing the creation of transient memory representations. Here we tested the adapting properties of early responses in human somatosensory areas SI and SII by analysing somatosensory-evoked potentials over the very first repetitions of a stimulus. SI and SII generators were identified by well-defined scalp potentials and source localisation from high-density 128-channel EEG. Earliest responses (~20 ms) from area 3b in the depth of the post-central gyrus did not show significant adaptation to stimuli repeated at 300 ms intervals. In contrast, responses around 45 ms from the crown of the gyrus (areas 1 and 2) rapidly lessened to a plateau and abated at the 20th stimulation, and activities from SII in the parietal operculum at ~100 ms displayed strong adaptation with a steady amplitude decrease from the first repetition. Although responses in both SI (1-2) and SII areas showed adapting properties and hence sensory memory capacities, evidence of sensory mismatch detection has been demonstrated only for responses reflecting SII activation. This may index the passage from an early form of sensory storage in SI to more operational memory codes in SII, allowing the prediction of forthcoming input and the triggering of a specific signal when such input differs from the previous sequence. This is consistent with a model whereby the length of temporal receptive windows increases with progression in the cortical hierarchy, in parallel with the complexity and abstraction of neural representations.

  2. How does a surgeon's brain buzz? An EEG coherence study on the interaction between humans and robot.

    Science.gov (United States)

    Bocci, Tommaso; Moretto, Carlo; Tognazzi, Silvia; Briscese, Lucia; Naraci, Megi; Leocani, Letizia; Mosca, Franco; Ferrari, Mauro; Sartucci, Ferdinando

    2013-04-22

    In humans, both primary and non-primary motor areas are involved in the control of voluntary movements. However, the dynamics of functional coupling among different motor areas have not been fully clarified yet. There is to date no research looking to the functional dynamics in the brain of surgeons working in laparoscopy compared with those trained and working in robotic surgery. We enrolled 16 right-handed trained surgeons and assessed changes in intra- and inter-hemispheric EEG coherence with a 32-channels device during the same motor task with either a robotic or a laparoscopic approach. Estimates of auto and coherence spectra were calculated by a fast Fourier transform algorithm implemented on Matlab 5.3. We found increase of coherence in surgeons performing laparoscopy, especially in theta and lower alpha activity, in all experimental conditions (M1 vs. SMA, S1 vs. SMA, S1 vs. pre-SMA and M1 vs. S1; p right vs. left M1, right vs. left S1, right pre-SMA vs. left M1, left pre-SMA vs. right M1; p brain and robotic devices.

  3. Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach.

    Science.gov (United States)

    Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L

    2017-10-01

    The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (pEEG patterns such as generalized periodic discharges (pEEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. A Comparison between the Rhesus Monkey and the Human on the Effect of Atropine on the Electroencephalogram. Volume 2. Preliminary Statistical Analysis of Spectral EEG Waveforms in Rhesus Monkeys Exposed to Atropine.

    Science.gov (United States)

    1994-11-01

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  5. No effects of a single 3G UMTS mobile phone exposure on spontaneous EEG activity, ERP correlates, and automatic deviance detection.

    Science.gov (United States)

    Trunk, Attila; Stefanics, Gábor; Zentai, Norbert; Kovács-Bálint, Zsófia; Thuróczy, György; Hernádi, István

    2013-01-01

    Potential effects of a 30 min exposure to third generation (3G) Universal Mobile Telecommunications System (UMTS) mobile phone-like electromagnetic fields (EMFs) were investigated on human brain electrical activity in two experiments. In the first experiment, spontaneous electroencephalography (sEEG) was analyzed (n = 17); in the second experiment, auditory event-related potentials (ERPs) and automatic deviance detection processes reflected by mismatch negativity (MMN) were investigated in a passive oddball paradigm (n = 26). Both sEEG and ERP experiments followed a double-blind protocol where subjects were exposed to either genuine or sham irradiation in two separate sessions. In both experiments, electroencephalograms (EEG) were recorded at midline electrode sites before and after exposure while subjects were watching a silent documentary. Spectral power of sEEG data was analyzed in the delta, theta, alpha, and beta frequency bands. In the ERP experiment, subjects were presented with a random series of standard (90%) and frequency-deviant (10%) tones in a passive binaural oddball paradigm. The amplitude and latency of the P50, N100, P200, MMN, and P3a components were analyzed. We found no measurable effects of a 30 min 3G mobile phone irradiation on the EEG spectral power in any frequency band studied. Also, we found no significant effects of EMF irradiation on the amplitude and latency of any of the ERP components. In summary, the present results do not support the notion that a 30 min unilateral 3G EMF exposure interferes with human sEEG activity, auditory evoked potentials or automatic deviance detection indexed by MMN. Copyright © 2012 Wiley Periodicals, Inc.

  6. Transcranial electrical stimulation accelerates human sleep homeostasis.

    Directory of Open Access Journals (Sweden)

    Davide Reato

    Full Text Available The sleeping brain exhibits characteristic slow-wave activity which decays over the course of the night. This decay is thought to result from homeostatic synaptic downscaling. Transcranial electrical stimulation can entrain slow-wave oscillations (SWO in the human electro-encephalogram (EEG. A computational model of the underlying mechanism predicts that firing rates are predominantly increased during stimulation. Assuming that synaptic homeostasis is driven by average firing rates, we expected an acceleration of synaptic downscaling during stimulation, which is compensated by a reduced drive after stimulation. We show that 25 minutes of transcranial electrical stimulation, as predicted, reduced the decay of SWO in the remainder of the night. Anatomically accurate simulations of the field intensities on human cortex precisely matched the effect size in different EEG electrodes. Together these results suggest a mechanistic link between electrical stimulation and accelerated synaptic homeostasis in human sleep.

  7. The effect of a prospected reward on semantic processing : An N400 EEG study

    NARCIS (Netherlands)

    van der Ven, Sanne H. G.; van Touw, Sven A. C.; van Hoogmoed, Anne H.; Janssen, Eva M.; Leseman, Paul P. M.

    2016-01-01

    Promised rewards are often used in education to stimulate learning behaviour. The present study tested whether a reward prospect affects semantic processing and recall of learned materials. Thirty-nine females participated in an electroencephalogram (EEG) task measuring semantic processing using the

  8. EEG theta and beta power spectra in adolescents with ADHD versus adolescents with ASD + ADHD.

    NARCIS (Netherlands)

    Bink, M.; van Boxtel, G.J.M.; Popma, A.; Bongers, I.L.; Denissen, A.J.; van Nieuwenhuizen, Ch.

    2015-01-01

    Attention problems are common in youngsters with attention deficit hyperactivity disorder (ADHD) as well as in adolescents with combined autism spectrum disorder (ASD) and ADHD. However, it is unknown whether there is psychophysiological overlap and/or a difference in electroencephalogram (EEG)

  9. EEG theta and beta power spectra in adolescents with ADHD versus adolescents with ASD plus ADHD

    NARCIS (Netherlands)

    Bink, M.; van Boxtel, G.J.M.; Popma, A.; Bongers, I.L.; Denissen, A.J.M.; van Nieuwenhuizen, Ch.

    2015-01-01

    Attention problems are common in youngsters with attention deficit hyperactivity disorder (ADHD) as well as in adolescents with combined autism spectrum disorder (ASD) and ADHD. However, it is unknown whether there is psychophysiological overlap and/or a difference in electroencephalogram (EEG)

  10. Atypical EEG Power Correlates with Indiscriminately Friendly Behavior in Internationally Adopted Children

    Science.gov (United States)

    Tarullo, Amanda R.; Garvin, Melissa C.; Gunnar, Megan R.

    2011-01-01

    While effects of institutional care on behavioral development have been studied extensively, effects on neural systems underlying these socioemotional and attention deficits are only beginning to be examined. The current study assessed electroencephalogram (EEG) power in 18-month-old internationally adopted, postinstitutionalized children (n = 37)…

  11. Feature Selection Strategy for Classification of Single-Trial EEG Elicited by Motor Imagery

    DEFF Research Database (Denmark)

    Prasad, Swati; Tan, Zheng-Hua; Prasad, Ramjee

    2011-01-01

    Brain-Computer Interface (BCI) provides new means of communication for people with motor disabilities by utilizing electroencephalographic activity. Selection of features from Electroencephalogram (EEG) signals for classification plays a key part in the development of BCI systems. In this paper, we...

  12. Low levels of sarin affect the EEG in marmoset monkeys: A pilot study

    NARCIS (Netherlands)

    Helden, H.H.P.M. van; Vanwersch, R.A.P.; Kuijpers, W.C.; Trap, H.C.; Philippens, I.H.C.; Benschop, H.P.

    2004-01-01

    The main purpose of this pilot study was to estimate the lowest observable adverse effect level (LOAEL) for the electroencephalogram (EEG) upon long-term, low-level exposure of vehicle-pretreated and pyridostigmine-pretreated marmoset monkeys to sarin vapour. This is the C·t value (t = 5 h) of

  13. Sex Differences in the Sleep EEG of Young Adults : Visual Scoring and Spectral Analysis

    NARCIS (Netherlands)

    Dijk, Derk Jan; Beersma, Domien G.M.; Bloem, Gerda M.

    1989-01-01

    Baseline sleep of 13 men (mean age of 23.5 years) and 15 women (21.9 years) was analyzed. Visual scoring of the electroencephalograms (EEGs) revealed no significant differences between the sexes in the amounts of slow-wave sleep and rapid-eye-movement (REM) sleep. Spectral analysis, however,

  14. An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2014-12-01

    Full Text Available In the modern world of automation, biological signals, especially Electroencephalogram (EEG is gaining wide attention as a source of biometric information. Eye-blinks and movement of the eyeballs produce electrical signals (contaminate the EEG signals that are collectively known as ocular artifacts. These noise signals are required to be separated from the EEG signals to obtain the accurate results. This paper reports an experiment of ocular artifacts elimination from EEG signal using blind source separation algorithm based on independent component analysis and principal component analysis. EEG signals are recorded on three conditions, which are normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of EEG signals in the range of 12-14 Hz either on normal, closed, and blinked eyes conditions is obtained. 

  15. EEG PHASE RESET OF THE DEFAULT MODE NETWORK

    Directory of Open Access Journals (Sweden)

    Robert W. Thatcher

    2014-07-01

    Full Text Available Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources located in Brodmann areas located in the human default mode network (DMN using Low Resolution Electromagnetic Tomography (LORETA of the human electroencephalogram (EEG. Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodman areas comprising the DMN in the delta frequency band. The Hilbert transform of the LORETA time series was used to compute the instantaneous phase differences between all pairs of Brodmann areas. Phase shift and lock durations were calculated based on the 1st & 2nd derivatives of the time series of phase differences. Results: Phase shift duration exhibited three discrete modes at approximately: 1- 30 msec,, 2- 55 msec and, 3- 65 msec. Phase lock duration present primarily at: 1- 300 to 350 msec and, 2- 350 msec to 450 msec. Phase shift and lock durations were inversely related and exhibited an exponential change with distance between Brodmann areas. Conclusions: The results are explained by local neural packing density of network hubs and an exponential decrease in connections with distance from a hub. The results are consistent with a discrete temporal model of brain function where anatomical hubs behave like a ‘shutter’ that opens and closes at specific durations as nodes of a network giving rise to temporarily phase locked clusters of neurons for specific durations.

  16. Development of an EEG-based workload measurement method in nuclear power plants

    International Nuclear Information System (INIS)

    Choi, Moon Kyoung; Lee, Seung Min; Ha, Jun Su; Seong, Poong Hyun

    2018-01-01

    Highlights: •A human operator’s workload in nuclear power plants(NPPs) usually has been evaluated by using subjective ratings. •Subjective rating techniques have several weaknesses such as dependence on the operator’s memory as well as bias. •We suggested an electroencephalogram (EEG)-based workload index for measuring the workload of human operators. •The suggested index was applied to evaluate the effects of operating support systems. -- Abstract: The environment of main control rooms of large scale process control systems such as nuclear power plants (NPPs) has been changed from the conventional analog type to the digital type. In digitalized advanced main control rooms, human operators conduct highly cognitive work rather than physical work compared to the case of the original control rooms in NPPs. Various operating support systems (OSSs) have been developed to reduce an operator’s workload. Most representative techniques to evaluate the workload are based on subjective ratings. However, there are some limitations including the possibility of skewed results due to self-assessment of the workload and the impossibility of continuously measuring the workload due to freezing simulation for workload assessment. As opposed to subjective ratings techniques, physiological techniques can be used for objective and continuous measurements of a human operator’s mental status by sensing the physiological changes of the autonomic or central nervous system. In this study, electroencephalogram (EEG) was used to measure the operator’s mental workload because it had been proven to be sensitive to variations of mental workload in other studies, and it allows various types of analysis. Based on various research reviews on the characteristics of brainwaves, EEG-based Workload Index (EWI) was suggested and validated through experiments. As a result, EWI is concluded to be valid for measuring an operator’s mental workload and preferable to subjective techniques

  17. Functional Brain Imaging by EEG: A Window to the Human Mind

    DEFF Research Database (Denmark)

    Stahlhut, Carsten

    representation of the human head, the tissue conductivity distribution, and electrode positions, we propose an algorithm which consider forward model uncertainties. Bayesian graphical models provide a powerful means of incorporating prior assumptions that narrow the solution space and lead to tractable posterior...

  18. Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

    DEFF Research Database (Denmark)

    Wiltshire, Travis; Euler, Matthew J.; McKinney, Ty

    2017-01-01

    Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components...

  19. Quantitative electroencephalogram (QEEG) Spectrum Analysis of Patients with Schizoaffective Disorder Compared to Normal Subjects.

    Science.gov (United States)

    Moeini, Mahdi; Khaleghi, Ali; Amiri, Nasrin; Niknam, Zahra

    2014-10-01

    The aim of this study was to achieve a better understanding of schizoaffective disorder. Therefore, we obtained electroencephalogram (EEG) signals from patients with schizoaffective disorder and analyzed them in comparison to normal subjects. Forty patients with schizoaffective disorder and 40 normal subjects were selected randomly and their electroencephalogram signals were recorded based on 10-20 international system by 23 electrodes in open- and closed-eyes while they were sitting on a chair comfortably. After preprocessing for noise removal and artifact reduction, we took 60- second segments from each recorded signals. Then, the absolute and relative powers of these segments were evaluated in all channels and in 4 frequency bands (i.e., delta, theta, alpha and beta waves). Finally, Data were analyzed by independent t-test using SPSS software. A significant decrease in relative power in the alpha band, a significant decrease in power spectra in the alpha band and a significant increase in power spectra in the beta band were found in patients compared to normal subjects (P schizoaffective patients, it can be concluded that schizoaffective disorder can be seen in schizophrenia spectrum.

  20. Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG

    Science.gov (United States)

    Bleichner, Martin G.; Debener, Stefan

    2017-01-01

    Electroencephalography (EEG) is an important clinical tool and frequently used to study the brain-behavior relationship in humans noninvasively. Traditionally, EEG signals are recorded by positioning electrodes on the scalp and keeping them in place with glue, rubber bands, or elastic caps. This setup provides good coverage of the head, but is impractical for EEG acquisition in natural daily-life situations. Here, we propose the transparent EEG concept. Transparent EEG aims for motion tolerant, highly portable, unobtrusive, and near invisible data acquisition with minimum disturbance of a user's daily activities. In recent years several ear-centered EEG solutions that are compatible with the transparent EEG concept have been presented. We discuss work showing that miniature electrodes placed in and around the human ear are a feasible solution, as they are sensitive enough to pick up electrical signals stemming from various brain and non-brain sources. We also describe the cEEGrid flex-printed sensor array, which enables unobtrusive multi-channel EEG acquisition from around the ear. In a number of validation studies we found that the cEEGrid enables the recording of meaningful continuous EEG, event-related potentials and neural oscillations. Here, we explain the rationale underlying the cEEGrid ear-EEG solution, present possible use cases and identify open issues that need to be solved on the way toward transparent EEG. PMID:28439233

  1. Electroencephalogram Similarity Analysis Using Temporal and Spectral Dynamics Analysis for Propofol and Desflurane Induced Unconsciousness

    Directory of Open Access Journals (Sweden)

    Quan Liu

    2018-01-01

    Full Text Available Important information about the state dynamics of the brain during anesthesia is unraveled by Electroencephalogram (EEG approaches. Patterns that are observed through EEG related to neural circuit mechanism under different molecular targets dependent anesthetics have recently attracted much attention. Propofol, a Gamma-amino butyric acid, is known with evidently increasing alpha oscillation. Desflurane shares the same receptor action and should be similar to propofol. To explore their dynamics, EEG under routine surgery level anesthetic depth is analyzed using multitaper spectral method from two groups: propofol (n = 28 and desflurane (n = 23. The time-varying spectrum comparison was undertaken to characterize their properties. Results show that both of the agents are dominated by slow and alpha waves. Especially, for increased alpha band feature, propofol unconsciousness shows maximum power at about 10 Hz (mean ± SD; frequency: 10.2 ± 1.4 Hz; peak power, −14.0 ± 1.6 dB, while it is approximate about 8 Hz (mean ± SD; frequency: 8.3 ± 1.3 Hz; peak power, −13.8 ± 1.6 dB for desflurane with significantly lower frequency-resolved spectra for this band. In addition, the mean power of propofol is much higher from alpha to gamma band, including slow oscillation than that of desflurane. The patterns might give us an EEG biomarker for specific anesthetic. This study suggests that both of the anesthetics exhibit similar spectral dynamics, which could provide insight into some common neural circuit mechanism. However, differences between them also indicate their uniqueness where relevant.

  2. Effects of tiapride on electroencephalograms and cognitive functions in the elderly.

    Science.gov (United States)

    Patat, A; Alberini, H; Bonhomme, D; Soubrane, C; Allain, H; Gandon, J M

    1999-07-01

    Tiapride is a substituted benzamide with selective dopamine D2 and D3-antagonist properties which appears to have preferential affinity for extra-striatal dopamine receptors. Tiapride is used in the treatment of agitation, aggressiveness and anxiety in the elderly. To define the effects of a single dose of tiapride 100 mg on psychomotor performance and cognitive functions and electroencephalogram (EEG), a randomized, double-blind, three-way crossover, placebo-controlled study using lorazepam 1 mg as a positive control was carried out in 12 elderly individuals (six women and six men, mean age +/- SD: 69 +/- 3 years). A 1-week wash-out interval was allowed between each administration. Psychomotor and cognitive functions were assessed using both objective [EEG, critical flicker fusion, simple reaction time, tapping, body sway, continuous performance task (CPT), digit symbol substitution test, Sternberg memory scanning and a learning memory test using word lists] and subjective (visual analogue scales) measures before and up to 6 h after dosing. Tiapride was devoid of any detrimental or sedative effects on EEG and all of the performance tasks used and did not impair memory compared with-placebo. In contrast, a single dose of lorazepam produced significant deleterious effects on psychomotor performance (decrease in tapping and in sustained attention (CPT) and an increase in reaction time and body sway), and sedative effects on EEG (significant increase in delta and decrease in alpha waves) as well as significant impairment in working memory (Sternberg) and anterograde amnesia (decrease in immediate and delayed free recall) up to 6 h after dosing compared with placebo and tiapride. In conclusion, the present study showed that in contrast to lorazepam 1 mg there is no evidence to suggest that a single dose of tiapride 100 mg has any sedative and amnestic effects in the elderly which may interfere with everyday life activities.

  3. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

    Science.gov (United States)

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

    Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  4. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

    Science.gov (United States)

    Truong, Nhan Duy; Nguyen, Anh Duy; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Ippolito, Samuel; Kavehei, Omid

    2018-05-07

    Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

  5. Detection of independent functional networks during music listening using electroencephalogram and sLORETA-ICA.

    Science.gov (United States)

    Jäncke, Lutz; Alahmadi, Nsreen

    2016-04-13

    The measurement of brain activation during music listening is a topic that is attracting increased attention from many researchers. Because of their high spatial accuracy, functional MRI measurements are often used for measuring brain activation in the context of music listening. However, this technique faces the issues of contaminating scanner noise and an uncomfortable experimental environment. Electroencephalogram (EEG), however, is a neural registration technique that allows the measurement of neurophysiological activation in silent and more comfortable experimental environments. Thus, it is optimal for recording brain activations during pleasant music stimulation. Using a new mathematical approach to calculate intracortical independent components (sLORETA-IC) on the basis of scalp-recorded EEG, we identified specific intracortical independent components during listening of a musical piece and scales, which differ substantially from intracortical independent components calculated from the resting state EEG. Most intracortical independent components are located bilaterally in perisylvian brain areas known to be involved in auditory processing and specifically in music perception. Some intracortical independent components differ between the music and scale listening conditions. The most prominent difference is found in the anterior part of the perisylvian brain region, with stronger activations seen in the left-sided anterior perisylvian regions during music listening, most likely indicating semantic processing during music listening. A further finding is that the intracortical independent components obtained for the music and scale listening are most prominent in higher frequency bands (e.g. beta-2 and beta-3), whereas the resting state intracortical independent components are active in lower frequency bands (alpha-1 and theta). This new technique for calculating intracortical independent components is able to differentiate independent neural networks associated

  6. Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

    Directory of Open Access Journals (Sweden)

    Issaku Kawashima

    2017-07-01

    Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

  7. Illumination influences working memory: an EEG study.

    Science.gov (United States)

    Park, Jin Young; Min, Byoung-Kyong; Jung, Young-Chul; Pak, Hyensou; Jeong, Yeon-Hong; Kim, Eosu

    2013-09-05

    Illumination conditions appear to influence working efficacy in everyday life. In the present study, we obtained electroencephalogram (EEG) correlates of working-memory load, and investigated how these waveforms are modulated by illumination conditions. We hypothesized that illumination conditions may affect cognitive performance. We designed an EEG study to monitor and record participants' EEG during the Sternberg working memory task under four different illumination conditions. Illumination conditions were generated with a factorial design of two color-temperatures (3000 and 7100 K) by two illuminance levels (150 and 700 lx). During a working memory task, we observed that high illuminance led to significantly lower frontal EEG theta activity than did low illuminance. These differences persisted despite no significant difference in task performance between illumination conditions. We found that the latency of an early event-related potential component, such as N1, was significantly modulated by the illumination condition. The fact that the illumination condition affects brain activity but not behavioral performance suggests that the lighting conditions used in the present study did not influence the performance stage of behavioral processing. Nevertheless, our findings provide objective evidence that illumination conditions modulate brain activity. Further studies are necessary to refine the optimal lighting parameters for facilitating working memory. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

  8. Amplitude spectrum EEG signal evidence for the dissociation of motor and perceptual spatial working memory in the human brain.

    Science.gov (United States)

    Smyrnis, Nikolaos; Protopapa, Foteini; Tsoukas, Evangelos; Balogh, Allison; Siettos, Constantinos I; Evdokimidis, Ioannis

    2014-02-01

    This study investigated the question whether spatial working memory related to movement plans (motor working memory) and spatial working memory related to spatial attention and perceptual processes (perceptual spatial working memory) share the same neurophysiological substrate or there is evidence for separate motor and perceptual working memory streams of processing. Towards this aim, ten healthy human subjects performed delayed responses to visual targets presented at different spatial locations. Two tasks were attained, one in which the spatial location of the target was the goal for a pointing movement and one in which the spatial location of the target was used for a perceptual (yes or no) change detection. Each task involved two conditions: a memory condition in which the target remained visible only for the first 250 ms of the delay period and a delay condition in which the target location remained visible throughout the delay period. The amplitude spectrum analysis of the EEG revealed that the alpha (8-12 Hz) band signal was smaller, while the beta (13-30 Hz) and gamma (30-45 Hz) band signals were larger in the memory compared to the non-memory condition. The alpha band signal difference was confined to the frontal midline area; the beta band signal difference extended over the right hemisphere and midline central area, and the gamma band signal difference was confined to the right occipitoparietal area. Importantly, both in beta and gamma bands, we observed a significant increase in the movement-related compared to the perceptual-related memory-specific amplitude spectrum signal in the central midline area. This result provides clear evidence for the dissociation of motor and perceptual spatial working memory.

  9. Resonance detection of EEG signals using two-layer wavelet analysis

    International Nuclear Information System (INIS)

    Abdallah, H. M; Odeh, F.S.

    2000-01-01

    This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs

  10. Diagnosis and interpretation of EEG on non-convulsive status epilepticus

    Directory of Open Access Journals (Sweden)

    Xiao-gang KANG

    2015-11-01

    Full Text Available It is difficult to diagnose non-convulsive status epilepticus (NCSE clinically because of the complicated etiology and various clinical and electroencephalographic features of NCSE without a universally accepted definition. Although the diagnosis of NCSE relies largely on electroencephalogram (EEG findings, the determination of NCSE on EEG is inevitably subjective, and the EEG changes of most patients is lack of specificity. As the diagnosis of NCSE is related to clinical and electroencephalographic manifestations, diagnostic criteria for NCSE should take into account both clinical and electroencephalographic features, and their response to antiepileptic drugs (AEDs. DOI: 10.3969/j.issn.1672-6731.2015.11.005

  11. Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA–WT during Working Memory Tasks

    Directory of Open Access Journals (Sweden)

    Noor Kamal Al-Qazzaz

    2017-06-01

    Full Text Available Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD, 15 stroke-related patients with mild cognitive impairment (MCI, and 15 healthy subjects during a working memory (WM task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA and wavelet transform (WT, that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation X C o r r and peak signal to noise ratio ( P S N R (ANOVA, p ˂ 0.05. The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, p ˂ 0.05. Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke

  12. EEG-guided transcranial magnetic stimulation reveals rapid shifts in motor cortical excitability during the human sleep slow oscillation

    DEFF Research Database (Denmark)

    Bergmann, Til O; Mölle, Matthias; Schmidt, Marlit A

    2012-01-01

    Evoked cortical responses do not follow a rigid input–output function but are dynamically shaped by intrinsic neural properties at the time of stimulation. Recent research has emphasized the role of oscillatory activity in determining cortical excitability. Here we employed EEG-guided transcranial......, closely resembling a spontaneous SO. However, both MEPs and TEPs were consistently larger when evoked during SO up-states than during down-states, and ampliudes within each SO state depended on the actual EEG potential at the time and site of stimulation. These results provide first-time evidence...... magnetic stimulation (TMS) during non-rapid eye movement sleep to examine whether the spontaneous

  13. Tele-transmission of EEG recordings.

    Science.gov (United States)

    Lemesle, M; Kubis, N; Sauleau, P; N'Guyen The Tich, S; Touzery-de Villepin, A

    2015-03-01

    EEG recordings can be sent for remote interpretation. This article aims to define the tele-EEG procedures and technical guidelines. Tele-EEG is a complete medical act that needs to be carried out with the same quality requirements as a local one in terms of indications, formulation of the medical request and medical interpretation. It adheres to the same quality requirements for its human resources and materials. It must be part of a medical organization (technical and medical network) and follow all rules and guidelines of good medical practices. The financial model of this organization must include costs related to performing the EEG recording, operating and maintenance of the tele-EEG network and medical fees of the physician interpreting the EEG recording. Implementing this organization must be detailed in a convention between all parties involved: physicians, management of the healthcare structure, and the company providing the tele-EEG service. This convention will set rules for network operation and finance, and also the continuous training of all staff members. The tele-EEG system must respect all rules for safety and confidentiality, and ensure the traceability and storing of all requests and reports. Under these conditions, tele-EEG can optimize the use of human resources and competencies in its zone of utilization and enhance the organization of care management. Copyright © 2015. Published by Elsevier SAS.

  14. Telemetry video-electroencephalography (EEG) in rats, dogs and non-human primates: methods in follow-up safety pharmacology seizure liability assessments.

    Science.gov (United States)

    Bassett, Leanne; Troncy, Eric; Pouliot, Mylene; Paquette, Dominique; Ascah, Alexis; Authier, Simon

    2014-01-01

    Non-clinical seizure liability studies typically aim to: 1) confirm the nature of EEG activity during abnormal clinical signs, 2) identify premonitory clinical signs, 3) measure plasma levels at seizure onset, 4) demonstrate that drug-induced seizures are self-limiting, 5) confirm that conventional drugs (e.g. diazepam) can treat drug-induced seizures and 6) confirm the no observed adverse effect level (NOAEL) at EEG. Our aim was to originally characterize several of these items in a three species comparative study. Cynomolgus monkey, Beagle dog and Sprague-Dawley rat with EEG telemetry transmitters were used to obtain EEG using the 10-20 system. Pentylenetetrazol (PTZ) was used to determine seizure threshold or as a positive seizurogenic agent. Clinical signs were recorded and premonitory signs were evaluated. In complement, other pharmacological agents were used to illustrate various safety testing strategies. Intravenous PTZ doses required to induce clonic convulsions were 36.1 (3.8), 56.1 (12.7) and 49.4 (11.7) mg/kg, in Beagle dogs, cynomolgus monkeys and Sprague-Dawley rats, respectively. Premonitory clinical signs typically included decreased physical activity, enhanced physiological tremors, hypersalivation, ataxia, emesis (except in rats) and myoclonus. In Sprague-Dawley rats, amphetamine (PO) increased high (approximately 40-120Hz), and decreased low (1-14Hz) frequencies. In cynomolgus monkeys, caffeine (IM) increased power in high (14-127Hz), and attenuated power in low (1-13Hz) frequencies. In the rat PTZ infusion seizure threshold model, yohimbine (SC and IV) and phenobarbital (IP) confirmed to be reliable positive controls as pro- and anticonvulsants, respectively. Telemetry video-EEG for seizure liability investigations was characterized in three species. Rats represent a first-line model in seizure liability assessments. Beagle dogs are often associated with overt susceptibility to seizure and are typically used in seizure liability studies only if

  15. Correlation between perceived stigma and EEG paroxysmal abnormality in childhood epilepsy.

    Science.gov (United States)

    Kanemura, Hideaki; Sano, Fumikazu; Ohyama, Tetsuo; Sugita, Kanji; Aihara, Masao

    2015-11-01

    We investigated the relationship between abnormal electroencephalogram (EEG) findings such as localized EEG paroxysmal abnormality (PA) and the perception of stigma to determine EEG factors associated with perceived stigma in childhood epilepsy. Participants comprised 40 patients (21 boys, 19 girls; mean age, 14.6 years) with epilepsy at enrollment. The criteria for inclusion were as follows: 1) age of 12-18 years, inclusive; 2) ≥6 months after epilepsy onset; 3) the ability to read and speak Japanese; and 4) the presence of EEG PA. Fifteen healthy seizure-free children were included as a control group. Participants were asked to rate how often they felt or acted in the ways described in the items of the Child Stigma Scale using a 5-point scale. Electroencephalogram paroxysms were classified based on the presence of spikes, sharp waves, or spike-wave complexes, whether focal or generalized. Participants showed significantly higher stigma scores than healthy subjects (pstigma. The average total scores of patients presenting with EEG PA at generalized, frontal, RD, midtemporal, and occipital regions were 2.3, 4.0, 2.4, 3.2, and 2.2, respectively. The scores of all questions were higher in the frontal group than those in other regions (pstigma than children presenting with nonfrontal EEG PA (pstigma. Further studies are needed to confirm whether frontal EEG PA may function as a mediator of emotional responses such as perceived stigma in childhood epilepsy. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Interictal Epileptiform Discharges (IEDs) classification in EEG data of epilepsy patients

    Science.gov (United States)

    Puspita, J. W.; Soemarno, G.; Jaya, A. I.; Soewono, E.

    2017-12-01

    Interictal Epileptiform Dischargers (IEDs), which consists of spike waves and sharp waves, in human electroencephalogram (EEG) are characteristic signatures of epilepsy. Spike waves are characterized by a pointed peak with a duration of 20-70 ms, while sharp waves has a duration of 70-200 ms. The purpose of the study was to classify spike wave and sharp wave of EEG data of epilepsy patients using Backpropagation Neural Network. The proposed method consists of two main stages: feature extraction stage and classification stage. In the feature extraction stage, we use frequency, amplitude and statistical feature, such as mean, standard deviation, and median, of each wave. The frequency values of the IEDs are very sensitive to the selection of the wave baseline. The selected baseline must contain all data of rising and falling slopes of the IEDs. Thus, we have a feature that is able to represent the type of IEDs, appropriately. The results show that the proposed method achieves the best classification results with the recognition rate of 93.75 % for binary sigmoid activation function and learning rate of 0.1.

  17. ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    2017-03-01

    Full Text Available The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC and radial basis function networks (RBFNNs. We have used Discrete Wavelet Transform (DWT technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.

  18. Experimental validation of the influence of white matter anisotropy on the intracranial EEG forward solution.

    Science.gov (United States)

    Bangera, Nitin B; Schomer, Donald L; Dehghani, Nima; Ulbert, Istvan; Cash, Sydney; Papavasiliou, Steve; Eisenberg, Solomon R; Dale, Anders M; Halgren, Eric

    2010-12-01

    Forward solutions with different levels of complexity are employed for localization of current generators, which are responsible for the electric and magnetic fields measured from the human brain. The influence of brain anisotropy on the forward solution is poorly understood. The goal of this study is to validate an anisotropic model for the intracranial electric forward solution by comparing with the directly measured 'gold standard'. Dipolar sources are created at known locations in the brain and intracranial electroencephalogram (EEG) is recorded simultaneously. Isotropic models with increasing level of complexity are generated along with anisotropic models based on Diffusion tensor imaging (DTI). A Finite Element Method based forward solution is calculated and validated using the measured data. Major findings are (1) An anisotropic model with a linear scaling between the eigenvalues of the electrical conductivity tensor and water self-diffusion tensor in brain tissue is validated. The greatest improvement was obtained when the stimulation site is close to a region of high anisotropy. The model with a global anisotropic ratio of 10:1 between the eigenvalues (parallel: tangential to the fiber direction) has the worst performance of all the anisotropic models. (2) Inclusion of cerebrospinal fluid as well as brain anisotropy in the forward model is necessary for an accurate description of the electric field inside the skull. The results indicate that an anisotropic model based on the DTI can be constructed non-invasively and shows an improved performance when compared to the isotropic models for the calculation of the intracranial EEG forward solution.

  19. Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner.

    Science.gov (United States)

    Muthuraman, M; Galka, A; Hong, V N; Heute, U; Deuschl, G; Raethjen, J

    2013-01-01

    Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.

  20. Brain-computer interfaces for EEG neurofeedback: peculiarities and solutions.

    Science.gov (United States)

    Huster, René J; Mokom, Zacharais N; Enriquez-Geppert, Stefanie; Herrmann, Christoph S

    2014-01-01

    Neurofeedback training procedures designed to alter a person's brain activity have been in use for nearly four decades now and represent one of the earliest applications of brain-computer interfaces (BCI). The majority of studies using neurofeedback technology relies on recordings of the electroencephalogram (EEG) and applies neurofeedback in clinical contexts, exploring its potential as treatment for psychopathological syndromes. This clinical focus significantly affects the technology behind neurofeedback BCIs. For example, in contrast to other BCI applications, neurofeedback BCIs usually rely on EEG-derived features with only a minimum of additional processing steps being employed. Here, we highlight the peculiarities of EEG-based neurofeedback BCIs and consider their relevance for software implementations. Having reviewed already existing packages for the implementation of BCIs, we introduce our own solution which specifically considers the relevance of multi-subject handling for experimental and clinical trials, for example by implementing ready-to-use solutions for pseudo-/sham-neurofeedback. © 2013.

  1. SYNDROMES OF BEHAVIORAL AND SPEECH DISORDERS ASSOCIATED WITH BENIGN EPILEPTIFORM DISCHARGES OF CHILDHOOD ON ELECTROENCEPHALOGRAM

    Directory of Open Access Journals (Sweden)

    I. A. Sadekov

    2017-01-01

    Full Text Available Objective: to assess the role and significance of benign epileptiform discharges of childhood (BEDC on electroencephalogram (EEG in development of speech and behaviorial disorders in children.Materials and methods. 90 children aged 3–7 years were included in the study: 30 of them were healthy, 30 had attention deficit hyperactivity disorder (ADHD, and 30 had expressive language disorder (ELD. We analyzed the role of persistent epileptiform activity (BEDC type in EEG as well as frontal intermittent rhythmic delta activity in the development of some neuropsychiatric disorders and speech disorders in children.Results. We suggest to allocate a special variant of ADHD – epileptiform disintegration of behavior; we also propose the strategies for its therapeutic correction.Conclusion. Detection of epileptiform activity (BEDC type on EEG in children with ELD is a predictor of cognitive disorders development and requires therapeutic correction, which should be aimed at stimulation of brain maturation. Detection of frontal intermittent rhythmic delta activity in children with ELD requires neurovisualization with further determining of treatment strategy.

  2. Electroencephalogram complexity analysis in children with attention-deficit/hyperactivity disorder during a visual cognitive task.

    Science.gov (United States)

    Zarafshan, Hadi; Khaleghi, Ali; Mohammadi, Mohammad Reza; Moeini, Mahdi; Malmir, Nastaran

    2016-01-01

    The aim of this study was to investigate electroencephalogram (EEG) dynamics using complexity analysis in children with attention-deficit/hyperactivity disorder (ADHD) compared with healthy control children when performing a cognitive task. Thirty 7-12-year-old children meeting Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) criteria for ADHD and 30 healthy control children underwent an EEG evaluation during a cognitive task, and Lempel-Ziv complexity (LZC) values were computed. There were no significant differences between ADHD and control groups on age and gender. The mean LZC of the ADHD children was significantly larger than healthy children over the right anterior and right posterior regions during the cognitive performance. In the ADHD group, complexity of the right hemisphere was higher than that of the left hemisphere, but the complexity of the left hemisphere was higher than that of the right hemisphere in the normal group. Although fronto-striatal dysfunction is considered conclusive evidence for the pathophysiology of ADHD, our arithmetic mental task has provided evidence of structural and functional changes in the posterior regions and probably cerebellum in ADHD.

  3. Four dimensional chaos and intermittency in a mesoscopic model of the electroencephalogram.

    Science.gov (United States)

    Dafilis, Mathew P; Frascoli, Federico; Cadusch, Peter J; Liley, David T J

    2013-06-01

    The occurrence of so-called four dimensional chaos in dynamical systems represented by coupled, nonlinear, ordinary differential equations is rarely reported in the literature. In this paper, we present evidence that Liley's mesoscopic theory of the electroencephalogram (EEG), which has been used to describe brain activity in a variety of clinically relevant contexts, possesses a chaotic attractor with a Kaplan-Yorke dimension significantly larger than three. This accounts for simple, high order chaos for a physiologically admissible parameter set. Whilst the Lyapunov spectrum of the attractor has only one positive exponent, the contracting dimensions are such that the integer part of the Kaplan-Yorke dimension is three, thus giving rise to four dimensional chaos. A one-parameter bifurcation analysis with respect to the parameter corresponding to extracortical input is conducted, with results indicating that the origin of chaos is due to an inverse period doubling cascade. Hence, in the vicinity of the high order, strange attractor, the model is shown to display intermittent behavior, with random alternations between oscillatory and chaotic regimes. This phenomenon represents a possible dynamical justification of some of the typical features of clinically established EEG traces, which can arise in the case of burst suppression in anesthesia and epileptic encephalopathies in early infancy.

  4. Information for People Treated with Human Growth Hormone (Summary)

    Science.gov (United States)

    ... person in Austria received hormone made by a pharmaceutical company. Are people treated with pituitary hGH at ... are the electroencephalogram (EEG) and magnetic resonance imaging (MRI). While these brain tests are useful if they ...

  5. Artifact removal from EEG signals using adaptive filters in cascade

    Science.gov (United States)

    Garcés Correa, A.; Laciar, E.; Patiño, H. D.; Valentinuzzi, M. E.

    2007-11-01

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

  6. Artifact removal from EEG signals using adaptive filters in cascade

    International Nuclear Information System (INIS)

    Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E

    2007-01-01

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records

  7. Artifact removal from EEG signals using adaptive filters in cascade

    Energy Technology Data Exchange (ETDEWEB)

    Garces Correa, A [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Laciar, E [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Patino, H D [Instituto de Automatica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Valentinuzzi, M E [Instituto Superior de Investigaciones Biologicas (INSIBIO), UNT-CONICET, Tucuman (Argentina)

    2007-11-15

    Artifacts in EEG (electroencephalogram) records are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). These noise sources increase the difficulty in analyzing the EEG and to obtaining clinical information. For this reason, it is necessary to design specific filters to decrease such artifacts in EEG records. In this paper, a cascade of three adaptive filters based on a least mean squares (LMS) algorithm is proposed. The first one eliminates line interference, the second adaptive filter removes the ECG artifacts and the last one cancels EOG spikes. Each stage uses a finite impulse response (FIR) filter, which adjusts its coefficients to produce an output similar to the artifacts present in the EEG. The proposed cascade adaptive filter was tested in five real EEG records acquired in polysomnographic studies. In all cases, line-frequency, ECG and EOG artifacts were attenuated. It is concluded that the proposed filter reduces the common artifacts present in EEG signals without removing significant information embedded in these records.

  8. EEG dynamical correlates of focal and diffuse causes of coma.

    Science.gov (United States)

    Kafashan, MohammadMehdi; Ryu, Shoko; Hargis, Mitchell J; Laurido-Soto, Osvaldo; Roberts, Debra E; Thontakudi, Akshay; Eisenman, Lawrence; Kummer, Terrance T; Ching, ShiNung

    2017-11-15

    Rapidly determining the causes of a depressed level of consciousness (DLOC) including coma is a common clinical challenge. Quantitative analysis of the electroencephalogram (EEG) has the potential to improve DLOC assessment by providing readily deployable, temporally detailed characterization of brain activity in such patients. While used commonly for seizure detection, EEG-based assessment of DLOC etiology is less well-established. As a first step towards etiological diagnosis, we sought to distinguish focal and diffuse causes of DLOC through assessment of temporal dynamics within EEG signals. We retrospectively analyzed EEG recordings from 40 patients with DLOC with consensus focal or diffuse culprit pathology. For each recording, we performed a suite of time-series analyses, then used a statistical framework to identify which analyses (features) could be used to distinguish between focal and diffuse cases. Using cross-validation approaches, we identified several spectral and non-spectral EEG features that were significantly different between DLOC patients with focal vs. diffuse etiologies, enabling EEG-based classification with an accuracy of 76%. Our findings suggest that DLOC due to focal vs. diffuse injuries differ along several electrophysiological parameters. These results may form the basis of future classification strategies for DLOC and coma that are more etiologically-specific and therefore therapeutically-relevant.

  9. Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

    Science.gov (United States)

    Kumar, Surendra; Ghosh, Subhojit; Tetarway, Suhash; Sinha, Rakesh Kumar

    2015-07-01

    In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

  10. Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing.

    Science.gov (United States)

    Buteneers, Pieter; Verstraeten, David; van Mierlo, Pieter; Wyckhuys, Tine; Stroobandt, Dirk; Raedt, Robrecht; Hallez, Hans; Schrauwen, Benjamin

    2011-11-01

    In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Identifying the effects of microsaccades in tripolar EEG signals.

    Science.gov (United States)

    Bellisle, Rachel; Steele, Preston; Bartels, Rachel; Lei Ding; Sunderam, Sridhar; Besio, Walter

    2017-07-01

    Microsaccades are tiny, involuntary eye movements that occur during fixation, and they are necessary to human sight to maintain a sharp image and correct the effects of other fixational movements. Researchers have theorized and studied the effects of microsaccades on electroencephalography (EEG) signals to understand and eliminate the unwanted artifacts from EEG. The tripolar concentric ring electrode (TCRE) sensors are used to acquire TCRE EEG (tEEG). The tEEG detects extremely focal signals from directly below the TCRE sensor. We have noticed a slow wave frequency found in some tEEG recordings. Therefore, we conducted the current work to determine if there was a correlation between the slow wave in the tEEG and the microsaccades. This was done by analyzing the coherence of the frequency spectrums of both tEEG and eye movement in recordings where microsaccades are present. Our preliminary findings show that there is a correlation between the two.

  12. Analysis on the training effect of criteria and practical guidance for determination of brain death: electroencephalogram

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    Wei-bi CHEN

    2015-12-01

    Full Text Available Objective To analyze the training results of electroencephalogram (EEG for brain death determination and to improve the training program. Methods A total of 114 trainees received theoretical training, simulation skills training, bedside skills training and test analysis. The composition of the trainees and the results of EEG tests were analyzed. The error rates of 5 knowledge points of EEG tests were calculated. Univariate and multivariate backward Logistic regression analyses were used to analyze the influence of factors including sex, age, specialty, professional category, professional qualification and hospital level on the error rates. Results All of 114 trainees came from 72 hospitals. Among them, 91 trainees (79.82% were between 30-49 years old, 108 trainees (94.74% came from third grade, grade A hospitals, and most of them were from Department of Neurology (57.89% , 66/114 and Electrophysiology (19.30% , 22/114. There were 98 clinicians (85.96% and 52 trainees (45.61% had intermediate certificate. Of the 5 knowledge points, the total error rate was 9.19% (204/2221. Among them, the error rate of parameter setting was the highest (11.40% , 26/228, followed by those of result determination (10.44%, 80/766, recording techniques (10.25%, 69/673, environmental requirements (7.46%, 17/228 and pitfalls (3.68%, 12/326. The error rate of trainees who were older than 50 was significantly higher than that in other ages (P = 0.000, for all. The error rate of technicians was higher than that of clinicians (P = 0.039. Univariate and multivariate Logistic regression analyses showed that age was independent risk factor associated with high error rates (OR = 1.382, 95%CI: 1.156-1.652; P = 0.000. Conclusions Among the trainees, degree of mastering the knowledge points is different. The training program should be optimized according to the trainees. More attention should be paid to the difference of EEG between brain death determination and routine check to

  13. Model-based analysis and optimization of the mapping of cortical sources in the spontaneous scalp EEG

    NARCIS (Netherlands)

    Sazonov, A.; Bergmans, J.W.M.; Cluitmans, P.J.M.; Griep, P.A.M.; Arends, J.B.A.M.; Boon, P.A.J.M.

    2007-01-01

    The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM) is fully determined by an observation function (OF) matrix. This paper analyses the

  14. How Sex and College Major Relate to Mental Rotation Accuracy and Preferred Strategy: An Electroencephalographic (EEG) Investigation

    Science.gov (United States)

    Li, Yingli; O'Boyle, Michael

    2013-01-01

    The electroencephalogram (EEG) was used to investigate variation in mental rotation (MR) strategies between males and females and different college majors. Beta activation was acquired from 40 participants (10 males and 10 females in physical science; 10 males and 10 females in social science) when performing the Vandenberg and Kuse (1978) mental…

  15. The relationship between hippocampal EEG theta activity and locomotr behaviour in freely moving rats: effects of vigabatrin

    NARCIS (Netherlands)

    Bouwman, B.M.; Lier, H. van; Nitert, H.E.J.; Drinkenburg, W.H.I.M.; Coenen, A.M.L.; Rijn, C.M. van

    2005-01-01

    The relationship between hippocampal electroencephalogram (EEG) theta activity and locomotor speed in both spontaneous and forced walking conditions was studied in rats after vigabatrin injection (500 mg/kg i.p.). Vigabatrin increased the percentage of time that rats spent being immobile. During

  16. Analysis of the Influence of Complexity and Entropy of Odorant on Fractal Dynamics and Entropy of EEG Signal.

    Science.gov (United States)

    Namazi, Hamidreza; Akrami, Amin; Nazeri, Sina; Kulish, Vladimir V

    2016-01-01

    An important challenge in brain research is to make out the relation between the features of olfactory stimuli and the electroencephalogram (EEG) signal. Yet, no one has discovered any relation between the structures of olfactory stimuli and the EEG signal. This study investigates the relation between the structures of EEG signal and the olfactory stimulus (odorant). We show that the complexity of the EEG signal is coupled with the molecular complexity of the odorant, where more structurally complex odorant causes less fractal EEG signal. Also, odorant having higher entropy causes the EEG signal to have lower approximate entropy. The method discussed here can be applied and investigated in case of patients with brain diseases as the rehabilitation purpose.

  17. Wearable ear EEG for brain interfacing

    Science.gov (United States)

    Schroeder, Eric D.; Walker, Nicholas; Danko, Amanda S.

    2017-02-01

    Brain-computer interfaces (BCIs) measuring electrical activity via electroencephalogram (EEG) have evolved beyond clinical applications to become wireless consumer products. Typically marketed for meditation and neu- rotherapy, these devices are limited in scope and currently too obtrusive to be a ubiquitous wearable. Stemming from recent advancements made in hearing aid technology, wearables have been shrinking to the point that the necessary sensors, circuitry, and batteries can be fit into a small in-ear wearable device. In this work, an ear-EEG device is created with a novel system for artifact removal and signal interpretation. The small, compact, cost-effective, and discreet device is demonstrated against existing consumer electronics in this space for its signal quality, comfort, and usability. A custom mobile application is developed to process raw EEG from each device and display interpreted data to the user. Artifact removal and signal classification is accomplished via a combination of support matrix machines (SMMs) and soft thresholding of relevant statistical properties.

  18. Automatic interpretation and writing report of the adult waking electroencephalogram.

    Science.gov (United States)

    Shibasaki, Hiroshi; Nakamura, Masatoshi; Sugi, Takenao; Nishida, Shigeto; Nagamine, Takashi; Ikeda, Akio

    2014-06-01

    Automatic interpretation of the EEG has so far been faced with significant difficulties because of a large amount of spatial as well as temporal information contained in the EEG, continuous fluctuation of the background activity depending on changes in the subject's vigilance and attention level, the occurrence of paroxysmal activities such as spikes and spike-and-slow-waves, contamination of the EEG with a variety of artefacts and the use of different recording electrodes and montages. Therefore, previous attempts of automatic EEG interpretation have been focussed only on a specific EEG feature such as paroxysmal abnormalities, delta waves, sleep stages and artefact detection. As a result of a long-standing cooperation between clinical neurophysiologists and system engineers, we report for the first time on a comprehensive, computer-assisted, automatic interpretation of the adult waking EEG. This system analyses the background activity, intermittent abnormalities, artefacts and the level of vigilance and attention of the subject, and automatically presents its report in written form. Besides, it also detects paroxysmal abnormalities and evaluates the effects of intermittent photic stimulation and hyperventilation on the EEG. This system of automatic EEG interpretation was formed by adopting the strategy that the qualified EEGers employ for the systematic visual inspection. This system can be used as a supplementary tool for the EEGer's visual inspection, and for educating EEG trainees and EEG technicians. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  19. ORIGINAL ARTICLE EEG changes and neuroimaging abnormalities ...

    African Journals Online (AJOL)

    salah

    Clinical Genetics Department, Human Genetics & Genome Research Division, ... neuroimaging changes of the brain and EEG abnormalities in correlation to the ... level and by developmental changes2. .... for IQ as a confounding factor.30.

  20. INTELLIGENT EEG ANALYSIS

    Directory of Open Access Journals (Sweden)

    M. Murugesan

    2011-04-01

    Full Text Available Brain is the wonderful organ of human body. It is the agent of information collection and transformation. The neural activity of the human brain starts between the 17th and 23rd week of prenatal development. It is believed that from this early stage and throughout life electrical signals are generated by the brain function but also the status of the whole body. Understanding of neuronal functions and neurophysiologic properties of the brain function together with the mechanisms underlying the generation of signals and their recording is, however, vital for those who deal with these signals for detection, diagnosis, and treatment of brain disorders and the related diseases. This research paper concentrated only on brain tumor detection. Using minimum electrode location the brain tumor possibility is detected. This paper is separated into two parts: the First part deals with electrode location on the scalp and the second part deals with how the fuzzy logic rule based algorithm is applied for estimation of brain tumor from EEG. Basically 8 locations are identified. After acquiring the pure EEG signal Fuzzy Logic Rule is applied to predict the possibility of brain tumor.

  1. Predicting Outcome in Comatose Patients: The Role of EEG Reactivity to Quantifiable Electrical Stimuli

    Directory of Open Access Journals (Sweden)

    Gang Liu

    2016-01-01

    Full Text Available Objective. To test the value of quantifiable electrical stimuli as a reliable method to assess electroencephalogram reactivity (EEG-R for the early prognostication of outcome in comatose patients. Methods. EEG was recorded in consecutive adults in coma after cardiopulmonary resuscitation (CPR or stroke. EEG-R to standard electrical stimuli was tested. Each patient received a 3-month follow-up by the Glasgow-Pittsburgh cerebral performance categories (CPC or modified Rankin scale (mRS score. Results. Twenty-two patients met the inclusion criteria. In the CPR group, 6 of 7 patients with EEG-R had good outcomes (positive predictive value (PPV, 85.7% and 4 of 5 patients without EEG-R had poor outcomes (negative predictive value (NPV, 80%. The sensitivity and specificity were 85.7% and 80%, respectively. In the stroke group, 6 of 7 patients with EEG-R had good outcomes (PPV, 85.7%; all of the 3 patients without EEG-R had poor outcomes (NPV, 100%. The sensitivity and specificity were 100% and 75%, respectively. Of all patients, the presence of EEG-R showed 92.3% sensitivity, 77.7% specificity, 85.7% PPV, and 87.5% NPV. Conclusion. EEG-R to quantifiable electrical stimuli might be a good positive predictive factor for the prognosis of outcome in comatose patients after CPR or stroke.

  2. The prognostic value of amplitude-integrated EEG in full-term neonates with seizures.

    Directory of Open Access Journals (Sweden)

    Dandan Zhang

    Full Text Available Neonatal seizures pose a high risk for adverse outcome in survived infants. While the prognostic value of amplitude-integrated electroencephalogram (aEEG is well established in neonates with encephalopathy and asphyxia, neonatal seizure studies focusing on the direct correlation between early aEEG measurement and subsequent neurologic outcome are scarce. In this study, the prognostic value of aEEG features was systematically analyzed in 143 full-term neonates to identify prognostic indicators of neurodevelopmental outcome. Neonatal aEEG features of background pattern, cyclicity, and seizure activity, as well as the etiology of neonatal seizures, were significantly associated with neurodevelopmental outcome at one year of age. aEEG background pattern was highly associated with neurologic outcomes (χ² = 116.9, followed by aEEG cyclicity (χ² = 87.2 and seizure etiology (χ² = 79.3. Multiple linear regression showed that the four predictors explained 71.2% of the variation in neurological outcome, with standardized β coefficients of 0.44, 0.24, 0.22, and 0.14 for the predictors of aEEG background pattern, cyclicity, etiology, and aEEG seizure activity, respectively. This clinically applicable scoring system based on etiology and three aEEG indices would allow pediatricians to assess the risk for neurodevelopmental impairment and facilitate an early intervention in newborns developing seizures.

  3. A preliminary study of muscular artifact cancellation in single-channel EEG.

    Science.gov (United States)

    Chen, Xun; Liu, Aiping; Peng, Hu; Ward, Rabab K

    2014-10-01

    Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

  4. Meditation and the EEG

    OpenAIRE

    West, Michael

    1980-01-01

    Previous research on meditation and the EEG is described, and findings relating to EEG patterns during meditation are discussed. Comparisons of meditation with other altered states are reviewed and it is concluded that, on the basis of existing EEG evidence, there is some reason for differentiating between meditation and drowsing. Research on alpha-blocking and habituation of the blocking response during meditation is reviewed, and the effects of meditation on EEG patterns outside of meditati...

  5. Recognizing emotions from EEG subbands using wavelet analysis.

    Science.gov (United States)

    Candra, Henry; Yuwono, Mitchell; Handojoseno, Ardi; Chai, Rifai; Su, Steven; Nguyen, Hung T

    2015-01-01

    Objectively recognizing emotions is a particularly important task to ensure that patients with emotional symptoms are given the appropriate treatments. The aim of this study was to develop an emotion recognition system using Electroencephalogram (EEG) signals to identify four emotions including happy, sad, angry, and relaxed. We approached this objective by firstly investigating the relevant EEG frequency band followed by deciding the appropriate feature extraction method. Two features were considered namely: 1. Wavelet Energy, and 2. Wavelet Entropy. EEG Channels reduction was then implemented to reduce the complexity of the features. The ground truth emotional states of each subject were inferred using Russel's circumplex model of emotion, that is, by mapping the subjectively reported degrees of valence (pleasure) and arousal to the appropriate emotions - for example, an emotion with high valence and high arousal is equivalent to a `happy' emotional state, while low valence and low arousal is equivalent to a `sad' emotional state. The Support Vector Machine (SVM) classifier was then used for mapping each feature vector into corresponding discrete emotions. The results presented in this study indicated thatWavelet features extracted from alpha, beta and gamma bands seem to provide the necessary information for describing the aforementioned emotions. Using the DEAP (Dataset for Emotion Analysis using electroencephalogram, Physiological and Video Signals), our proposed method achieved an average sensitivity and specificity of 77.4% ± 14.1% and 69.1% ± 12.8%, respectively.

  6. Mobile EEG in epilepsy

    NARCIS (Netherlands)

    Askamp, Jessica; van Putten, Michel Johannes Antonius Maria

    2014-01-01

    The sensitivity of routine EEG recordings for interictal epileptiform discharges in epilepsy is limited. In some patients, inpatient video-EEG may be performed to increase the likelihood of finding abnormalities. Although many agree that home EEG recordings may provide a cost-effective alternative

  7. Quantitative electroencephalogram (QEEG Spectrum Analysis of Patients with Schizoaffective Disorder Compared to Normal Subjects.

    Directory of Open Access Journals (Sweden)

    Mahdi Moeini

    2014-12-01

    Full Text Available The aim of this study was to achieve a better understanding of schizoaffective disorder. Therefore, we obtained electroencephalogram (EEG signals from patients with schizoaffective disorder and analyzed them in comparison to normal subjects.Forty patients with schizoaffective disorder and 40 normal subjects were selected randomly and their electroencephalogram signals were recorded based on 10-20 international system by 23 electrodes in open- and closed-eyes while they were sitting on a chair comfortably. After preprocessing for noise removal and artifact reduction, we took 60- second segments from each recorded signals. Then, the absolute and relative powers of these segments were evaluated in all channels and in 4 frequency bands (i.e., delta, theta, alpha and beta waves. Finally, Data were analyzed by independent t-test using SPSS software.A significant decrease in relative power in the alpha band, a significant decrease in power spectra in the alpha band and a significant increase in power spectra in the beta band were found in patients compared to normal subjects (P < 0.05. The predominant wave in the centro-parietal region was the beta wave in patients, but it was the alpha band in normal subjects (P = 0.048. Also, the predominant wave of the occipital region in patients was the delta wave, while it was the alpha wave in normal subjects (P = 0.038.Considering the findings, particularly based on the significant decrease of the alpha waves in schizoaffective patients, it can be concluded that schizoaffective disorder can be seen in schizophrenia spectrum.

  8. Description and recognition of patterns in stochastic signals. [Electroencephalograms

    Energy Technology Data Exchange (ETDEWEB)

    Flik, T [Technische Univ. Berlin (F.R. Germany). Informatik-Forschungsgruppe Rechnerorganisation und Schaltwerke

    1975-10-01

    A method is shown for the description and recognition of patterns in stochastic signals such as electroencephalograms. For pattern extraction the signal is segmented at times of minimum amplitudes. The describing features consist of geometric values of the so defined patterns. The classification algorithm is based on the regression analysis, which is well known in the field of character recognition. For an economic classification a method is proposed which reduces the number of features. The quality of this pattern recognition method is demonstrated by the detection of spike wave complexes in electroencephalograms. The pattern description and recognition are provided for processing on a digital computer. (DE)

  9. Negligible motion artifacts in scalp electroencephalography (EEG during treadmill walking

    Directory of Open Access Journals (Sweden)

    Kevin eNathan

    2016-01-01

    Full Text Available Recent Mobile Brain/Body Imaging (MoBI techniques based on active electrode scalp electroencephalogram (EEG allow the acquisition and real-time analysis of brain dynamics during active unrestrained motor behavior involving whole body movements such as treadmill walking, over-ground walking and other locomotive and non-locomotive tasks. Unfortunately, MoBI protocols are prone to physiological and non-physiological artifacts, including motion artifacts that may contaminate the EEG recordings. A few attempts have been made to quantify these artifacts during locomotion tasks but with inconclusive results due in part to methodological pitfalls. In this paper, we investigate the potential contributions of motion artifacts in scalp EEG during treadmill walking at three different speeds (1.5, 3.0, and 4.5 km/h using a wireless 64 channel active EEG system and a wireless inertial sensor attached to the subject’s head. The experimental setup was designed according to good measurement practices using state-of-the-art commercially-available instruments, and the measurements were analyzed using Fourier analysis and wavelet coherence approaches. Contrary to prior claims, the subjects’ motion did not significantly affect their EEG during treadmill walking although precaution should be taken when gait speeds approach 4.5 km/h. Overall, these findings suggest how MoBI methods may be safely deployed in neural, cognitive, and rehabilitation engineering applications.

  10. Automatic burst detection for the EEG of the preterm infant.

    Science.gov (United States)

    Jennekens, Ward; Ruijs, Loes S; Lommen, Charlotte M L; Niemarkt, Hendrik J; Pasman, Jaco W; van Kranen-Mastenbroek, Vivianne H J M; Wijn, Pieter F F; van Pul, Carola; Andriessen, Peter

    2011-10-01

    To aid with prognosis and stratification of clinical treatment for preterm infants, a method for automated detection of bursts, interburst-intervals (IBIs) and continuous patterns in the electroencephalogram (EEG) is developed. Results are evaluated for preterm infants with normal neurological follow-up at 2 years. The detection algorithm (MATLAB®) for burst, IBI and continuous pattern is based on selection by amplitude, time span, number of channels and numbers of active electrodes. Annotations of two neurophysiologists were used to determine threshold values. The training set consisted of EEG recordings of four preterm infants with postmenstrual age (PMA, gestational age + postnatal age) of 29-34 weeks. Optimal threshold values were based on overall highest sensitivity. For evaluation, both observers verified detections in an independent dataset of four EEG recordings with comparable PMA. Algorithm performance was assessed by calculation of sensitivity and positive predictive value. The results of algorithm evaluation are as follows: sensitivity values of 90% ± 6%, 80% ± 9% and 97% ± 5% for burst, IBI and continuous patterns, respectively. Corresponding positive predictive values were 88% ± 8%, 96% ± 3% and 85% ± 15%, respectively. In conclusion, the algorithm showed high sensitivity and positive predictive values for bursts, IBIs and continuous patterns in preterm EEG. Computer-assisted analysis of EEG may allow objective and reproducible analysis for clinical treatment.

  11. The Performance of EEG-P300 Classification using Backpropagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2013-12-01

    Full Text Available Electroencephalogram (EEG recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA. Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.

  12. Monitoring and diagnosis of Alzheimer's disease using noninvasive compressive sensing EEG

    Science.gov (United States)

    Morabito, F. C.; Labate, D.; Morabito, G.; Palamara, I.; Szu, H.

    2013-05-01

    The majority of elderly with Alzheimer's Disease (AD) receive care at home from caregivers. In contrast to standard tethered clinical settings, a wireless, real-time, body-area smartphone-based remote monitoring of electroencephalogram (EEG) can be extremely advantageous for home care of those patients. Such wearable tools pave the way to personalized medicine, for example giving the opportunity to control the progression of the disease and the effect of drugs. By applying Compressive Sensing (CS) techniques it is in principle possible to overcome the difficulty raised by smartphones spatial-temporal throughput rate bottleneck. Unfortunately, EEG and other physiological signals are often non-sparse. In this paper, it is instead shown that the EEG of AD patients becomes actually more compressible with the progression of the disease. EEG of Mild Cognitive Impaired (MCI) subjects is also showing clear tendency to enhanced compressibility. This feature favor the use of CS techniques and ultimately the use of telemonitoring with wearable sensors.

  13. Analysis of the features of untrained human movements based on the multichannel EEG for controlling anthropomorphic robotic arm

    Science.gov (United States)

    Maksimenko, Vladimir; Runnova, Anastasia; Pchelintseva, Svetlana; Efremova, Tatiana; Zhuravlev, Maksim; Pisarchik, Alexander

    2018-04-01

    We have considered time-frequency and spatio-temporal structure of electrical brain activity, associated with real and imaginary movements based on the multichannel EEG recordings. We have found that along with wellknown effects of event-related desynchronization (ERD) in α/μ - rhythms and β - rhythm, these types of activity are accompanied by the either ERS (for real movement) or ERD (for imaginary movement) in low-frequency δ - band, located mostly in frontal lobe. This may be caused by the associated processes of decision making, which take place when subject is deciding either perform the movement or imagine it. Obtained features have been found in untrained subject which it its turn gives the possibility to use our results in the development of brain-computer interfaces for controlling anthropomorphic robotic arm.

  14. A Comparative Study of Different EEG Reference Choices for Diagnosing Unipolar Depression.

    Science.gov (United States)

    Mumtaz, Wajid; Malik, Aamir Saeed

    2018-06-02

    The choice of an electroencephalogram (EEG) reference has fundamental importance and could be critical during clinical decision-making because an impure EEG reference could falsify the clinical measurements and subsequent inferences. In this research, the suitability of three EEG references was compared while classifying depressed and healthy brains using a machine-learning (ML)-based validation method. In this research, the EEG data of 30 unipolar depressed subjects and 30 age-matched healthy controls were recorded. The EEG data were analyzed in three different EEG references, the link-ear reference (LE), average reference (AR), and reference electrode standardization technique (REST). The EEG-based functional connectivity (FC) was computed. Also, the graph-based measures, such as the distances between nodes, minimum spanning tree, and maximum flow between the nodes for each channel pair, were calculated. An ML scheme provided a mechanism to compare the performances of the extracted features that involved a general framework such as the feature extraction (graph-based theoretic measures), feature selection, classification, and validation. For comparison purposes, the performance metrics such as the classification accuracies, sensitivities, specificities, and F scores were computed. When comparing the three references, the diagnostic accuracy showed better performances during the REST, while the LE and AR showed less discrimination between the two groups. Based on the results, it can be concluded that the choice of appropriate reference is critical during the clinical scenario. The REST reference is recommended for future applications of EEG-based diagnosis of mental illnesses.

  15. Real-time inference of word relevance from electroencephalogram and eye gaze

    Science.gov (United States)

    Wenzel, M. A.; Bogojeski, M.; Blankertz, B.

    2017-10-01

    Objective. Brain-computer interfaces can potentially map the subjective relevance of the visual surroundings, based on neural activity and eye movements, in order to infer the interest of a person in real-time. Approach. Readers looked for words belonging to one out of five semantic categories, while a stream of words passed at different locations on the screen. It was estimated in real-time which words and thus which semantic category interested each reader based on the electroencephalogram (EEG) and the eye gaze. Main results. Words that were subjectively relevant could be decoded online from the signals. The estimation resulted in an average rank of 1.62 for the category of interest among the five categories after a hundred words had been read. Significance. It was demonstrated that the interest of a reader can be inferred online from EEG and eye tracking signals, which can potentially be used in novel types of adaptive software, which enrich the interaction by adding implicit information about the interest of the user to the explicit interaction. The study is characterised by the following novelties. Interpretation with respect to the word meaning was necessary in contrast to the usual practice in brain-computer interfacing where stimulus recognition is sufficient. The typical counting task was avoided because it would not be sensible for implicit relevance detection. Several words were displayed at the same time, in contrast to the typical sequences of single stimuli. Neural activity was related with eye tracking to the words, which were scanned without restrictions on the eye movements.

  16. Changes in Alpha Frequency and Power of the Electroencephalogram during Volatile-Based General Anesthesia

    Directory of Open Access Journals (Sweden)

    Darren Hight

    2017-05-01

    Full Text Available Oscillations in the electroencephalogram (EEG at the alpha frequency (8–12 Hz are thought to be ubiquitous during surgical anesthesia, but the details of how this oscillation responds to ongoing changes in volatile anesthetic concentration have not been well characterized. It is not known how often alpha oscillations are absent in the clinical context, how sensitively alpha frequency and power respond to changes in anesthetic concentration, and what effect increased age has on alpha frequency. Bipolar EEG was recorded frontally from 305 patients undergoing surgery with sevoflurane or desflurane providing general anesthesia. A new method of detecting the presence of alpha oscillations based on the stability of the rate of change of the peak frequency in the alpha range was developed. Linear concentration-response curves were fitted to assess the sensitivity of alpha power and frequency measures to changing levels of anesthesia. Alpha oscillations were seen to be inexplicably absent in around 4% of patients. Maximal alpha power increased with increasing volatile anesthetic concentrations in half of the patients, and decreased in the remaining patients. Alpha frequency decreased with increasing anesthetic concentrations in near to 90% of patients. Increasing age was associated with decreased sensitivity to volatile anesthesia concentrations, and with decreased alpha frequency, which sometimes transitioned into the theta range (5–7 Hz. While peak alpha frequency shows a consistent slowing to increasing volatile concentrations, the peak power of the oscillation does not, suggesting that frequency might be more informative of depth of anesthesia than traditional power based measures during volatile-based anesthesia. The alpha oscillation becomes slower with increasing age, even when the decreased anesthetic needs of older patients were taken into account.

  17. Endosulfan and cholinergic (muscarinic) transmission: effect on electroencephalograms and [3H]quinuclidinyl benzilate in pigeon brain

    International Nuclear Information System (INIS)

    Anand, M.; Agrawal, A.K.; Gopal, K.; Sur, R.N.; Seth, P.K.

    1986-01-01

    Single exposure of endosulfan (5 mg/kg) to pigeons (Columbia livia) caused neuronal hyperexcitability as evidence by spike discharges of 200-500 μV in the electroencephalograms (EEG) from the telencephalon and hyperstriatum, but there was not effect on the ectostriatal area. Cholinergic (muscarinic) receptor binding study using [ 3 H]quinuclidinyl benzilate ([ 3 H]QNB) as a specific ligand indicated that a single exposure to 5 mg/kg of endosulfan caused a significant increase in [ 3 H]QNB binding to the striatal membrane. Behavior study further indicated that a single dose of 200 μg/kg of oxotremorine produced a significant induction in the tremor in endosulfan-pretreated pigeons. The results of this behavioral and biochemical study indicate the involvement of a cholinergic (muscarinic) transmitter system in endosulfan-induced neurotoxicity

  18. Characterisation of the Effects of Sleep Deprivation on the Electroencephalogram Using Permutation Lempel–Ziv Complexity, a Non-Linear Analysis Tool

    Directory of Open Access Journals (Sweden)

    Pinar Deniz Tosun

    2017-12-01

    Full Text Available Specific patterns of brain activity during sleep and waking are recorded in the electroencephalogram (EEG. Time-frequency analysis methods have been widely used to analyse the EEG and identified characteristic oscillations for each vigilance state (VS, i.e., wakefulness, rapid-eye movement (REM and non-rapid-eye movement (NREM sleep. However, other aspects such as change of patterns associated with brain dynamics may not be captured unless a non-linear-based analysis method is used. In this pilot study, Permutation Lempel–Ziv complexity (PLZC, a novel symbolic dynamics analysis method, was used to characterise the changes in the EEG in sleep and wakefulness during baseline and recovery from sleep deprivation (SD. The results obtained with PLZC were contrasted with a related non-linear method, Lempel–Ziv complexity (LZC. Both measure the emergence of new patterns. However, LZC is dependent on the absolute amplitude of the EEG, while PLZC is only dependent on the relative amplitude due to symbolisation procedure and thus, more resistant to noise. We showed that PLZC discriminates activated brain states associated with wakefulness and REM sleep, which both displayed higher complexity, compared to NREM sleep. Additionally, significantly lower PLZC values were measured in NREM sleep during the recovery period following SD compared to baseline, suggesting a reduced emergence of new activity patterns in the EEG. These findings were validated using PLZC on surrogate data. By contrast, LZC was merely reflecting changes in the spectral composition of the EEG. Overall, this study implies that PLZC is a robust non-linear complexity measure, which is not dependent on amplitude variations in the signal, and which may be useful to further assess EEG alterations induced by environmental or pharmacological manipulations.

  19. Estimating Driving Performance Based on EEG Spectrum Analysis

    Directory of Open Access Journals (Sweden)

    Jung Tzyy-Ping

    2005-01-01

    Full Text Available The growing number of traffic accidents in recent years has become a serious concern to society. Accidents caused by driver's drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the driver's abilities of perception, recognition, and vehicle control abilities while sleepy. Preventing such accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level of alertness of drivers and delivering effective feedbacks to maintain their maximum performance. This paper proposes an EEG-based drowsiness estimation system that combines electroencephalogram (EEG log subband power spectrum, correlation analysis, principal component analysis, and linear regression models to indirectly estimate driver's drowsiness level in a virtual-reality-based driving simulator. Our results demonstrated that it is feasible to accurately estimate quantitatively driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulator.

  20. Joint optimization of algorithmic suites for EEG analysis.

    Science.gov (United States)

    Santana, Eder; Brockmeier, Austin J; Principe, Jose C

    2014-01-01

    Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.

  1. Corticotropin-releasing hormone induces depression-like changes of sleep electroencephalogram in healthy women.

    Science.gov (United States)

    Schüssler, P; Kluge, M; Gamringer, W; Wetter, T C; Yassouridis, A; Uhr, M; Rupprecht, R; Steiger, A

    2016-12-01

    We reported previously that repetitive intravenous injections of corticotropin-releasing hormone (CRH) around sleep onset prompt depression-like changes in certain sleep and endocrine activity parameters (e.g. decrease of slow-wave sleep during the second half of the night, blunted growth hormone peak, elevated cortisol concentration during the first half of the night). Furthermore a sexual dimorphism of the sleep-endocrine effects of the hormones growth hormone-releasing hormone and ghrelin was observed. In the present placebo-controlled study we investigated the effect of pulsatile administration of 4×50μg CRH on sleep electroencephalogram (EEG) and nocturnal cortisol and GH concentration in young healthy women. After CRH compared to placebo, intermittent wakefulness increased during the total night and the sleep efficiency index decreased. During the first third of the night, REM sleep and stage 2 sleep increased and sleep stage 3 decreased. Cortisol concentration was elevated throughout the night and during the first and second third of the night. GH secretion remained unchanged. Our data suggest that after CRH some sleep and endocrine activity parameters show also depression-like changes in healthy women. These changes are more distinct in women than in men. Copyright © 2016 Elsevier Ltd. All rights reserved.

  2. Epileptiform electroencephalogram abnormality in children with congenital sensorineural hearing loss.

    Science.gov (United States)

    El-Badry, Mohamed Mohamed; Hamdy, Nermin Aly; Sobhy, Sayed; Gamal, Reham

    2014-04-01

    This work was designed to study electroencephalogram findings in children with congenital sensorineural hearing loss and correlate these findings with the SNHL parameters as duration, etiology, severity, and type. Ninety children with bilateral congenital sensorineural hearing loss served as the study group. They were free from any neurological disorders or symptoms that are commonly associated with abnormal electroencephalogram as convulsions or loss of consciousness. Twenty children having normal hearing with no history of otological or neurological disorders served as the control group. All children participating in the study were subjected to full medical and audiological history, otological examination, neurological examination, audiological evaluation and electroencephalogram recording. Mean age of the children in the control group was 3.56 ± 2.1 years and mean age of the children in the study group was 3.8 ± 2.2 years. While none of the control children had abnormal electroencephalogram, 38 (42.2%) of children with congenital SNHL had epileptiform electroencephalogram abnormality. The epileptiform abnormality was generalized in 14 children (36.8%), focal temporal in 17 children (44.7%) and focal other than temporal in 7 children (18.4%). According to the hemispheric side affected, the abnormality was right in 14 children (36.8%), left in 10 children (26.3%) and bilateral in 14 children (36.8%). No statistically significant predominance of specific site or side of the epileptiform abnormality was found. Similarly, no statistical significant prevalent of the epileptiform abnormality was found in relation to the age or sex of children, duration of hearing loss or etiology of hearing loss (i.e., genetic vs. neonatal insults). On the other hand, the epileptiform abnormality was statistically prevalent in children with moderate degree of hearing loss, and in children with auditory neuropathy spectrum disorder. The epileptiform electroencephalogram abnormality is

  3. To what extent can dry and water-based EEG electrodes replace conductive gel ones?: A Steady State Visual Evoked Potential Brain-Computer Interface Case Study

    NARCIS (Netherlands)

    Mihajlovic, V.; Garcia Molina, G.; Peuscher, J

    2011-01-01

    Recent technological advances in the field of skin electrodes and on-body sensors indicate a possibility of having an alternative to the traditionally used conductive gel electrodes for measuring electrical signals of the brain (electroencephalogram, EEG). This paper evaluates whether water-based

  4. EEG and Coma.

    Science.gov (United States)

    Ardeshna, Nikesh I

    2016-03-01

    Coma is defined as a state of extreme unresponsiveness, in which a person exhibits no voluntary movement or behavior even to painful stimuli. The utilization of EEG for patients in coma has increased dramatically over the last few years. In fact, many institutions have set protocols for continuous EEG (cEEG) monitoring for patients in coma due to potential causes such as subarachnoid hemorrhage or cardiac arrest. Consequently, EEG plays an important role in diagnosis, managenent, and in some cases even prognosis of coma patients.

  5. Human Brain Activity Patterns beyond the Isoelectric Line of Extreme Deep Coma

    Science.gov (United States)

    Kroeger, Daniel; Florea, Bogdan; Amzica, Florin

    2013-01-01

    The electroencephalogram (EEG) reflects brain electrical activity. A flat (isoelectric) EEG, which is usually recorded during very deep coma, is considered to be a turning point between a living brain and a deceased brain. Therefore the isoelectric EEG constitutes, together with evidence of irreversible structural brain damage, one of the criteria for the assessment of brain death. In this study we use EEG recordings for humans on the one hand, and on the other hand double simultaneous intracellular recordings in the cortex and hippocampus, combined with EEG, in cats. They serve to demonstrate that a novel brain phenomenon is observable in both humans and animals during coma that is deeper than the one reflected by the isoelectric EEG, and that this state is characterized by brain activity generated within the hippocampal formation. This new state was induced either by medication applied to postanoxic coma (in human) or by application of high doses of anesthesia (isoflurane in animals) leading to an EEG activity of quasi-rhythmic sharp waves which henceforth we propose to call ν-complexes (Nu-complexes). Using simultaneous intracellular recordings in vivo in the cortex and hippocampus (especially in the CA3 region) we demonstrate that ν-complexes arise in the hippocampus and are subsequently transmitted to the cortex. The genesis of a hippocampal ν-complex depends upon another hippocampal activity, known as ripple activity, which is not overtly detectable at the cortical level. Based on our observations, we propose a scenario of how self-oscillations in hippocampal neurons can lead to a whole brain phenomenon during coma. PMID:24058669

  6. Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.

    Science.gov (United States)

    Hauk, O; Keil, A; Elbert, T; Müller, M M

    2002-01-30

    We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

  7. Changes in the Electro-encephalogram Minimal Cerebral Dysfunction

    African Journals Online (AJOL)

    1974-03-23

    Mar 23, 1974 ... have a significantly higher incidence of EEG abnormalities of a slow wave type, ... abnormal electrocortical activity associated with this syndrome. ... central nervous system result in primary disorganisation, which influences ...

  8. Problems associated with dimensional analysis of electroencephalogram data

    Energy Technology Data Exchange (ETDEWEB)

    Layne, S.; Mayer-Kress, G.; Holzfuss, J.

    1985-01-01

    The goal was to evaluate anesthetic depth for a series of 5 to 10 patients by dimensional analysis. It has been very difficult to obtain clean EEG records from the operating room. Noise is prominent due to electrocautery and to movement of the patient's head by operating room personnel. In addition, specialized EEG equipment must be used to reduce noise and to accommodate limited space in the room. This report discusses problems associated with dimensional analysis of the EEG. We choose one EEG record from a single patient, in order to study the method but not to draw general conclusions. For simplicity, we consider only two states: awake but quiet, and medium anesthesia. 14 refs., 8 figs., 1 tab.

  9. Investigating reading comprehension through EEG

    Directory of Open Access Journals (Sweden)

    Luciane Baretta

    2012-12-01

    Full Text Available http://dx.doi.org/10.5007/2175-8026.2012n63p69   Experimental studies point that different factors can influence reading comprehension, such as the topic, text type, reading task, and others. The advances in technologies for the past decades have provided researchers with several possibilities to investigate what goes on in one’s brain since their eyes meet the page until comprehension is achieved. Since the mid-80’s, numerous studies have been conducted with the use of the electroencephalogram (EEG to investigate the process of reading, through the analysis of different components – n400, n100 or n1, P2, among others. These components reveal, for example, how the brain integrates the meaning of a specific word in the semantic context of a given sentence.  based on previous studies, which demonstrate that different types of words affect cognitive load, this paper aims at investigating how the brain processes function and content words inserted in expository and narrative texts with suitable / unsuitable conclusions. results showed that the type of text and word influence the cognitive load in different scalp areas (midline, right and left hemispheres. The  n1s were more pronounced to the content words inserted in narrative texts and to the function words inserted in the expository type of texts, corroborating former studies.

  10. Comparison of Quantitative Characteristics of Early Post-resuscitation EEG Between Asphyxial and Ventricular Fibrillation Cardiac Arrest in Rats.

    Science.gov (United States)

    Chen, Bihua; Chen, Gang; Dai, Chenxi; Wang, Pei; Zhang, Lei; Huang, Yuanyuan; Li, Yongqin

    2018-04-01

    Quantitative electroencephalogram (EEG) analysis has shown promising results in studying brain injury and functional recovery after cardiac arrest (CA). However, whether the quantitative characteristics of EEG, as potential indicators of neurological prognosis, are influenced by CA causes is unknown. The purpose of this study was designed to compare the quantitative characteristics of early post-resuscitation EEG between asphyxial CA (ACA) and ventricular fibrillation CA (VFCA) in rats. Thirty-two Sprague-Dawley rats of both sexes were randomized into either ACA or VFCA group. Cardiopulmonary resuscitation was initiated after 5-min untreated CA. Characteristics of early post-resuscitation EEG were compared, and the relationships between quantitative EEG features and neurological outcomes were investigated. Compared with VFCA, serum level of S100B, neurological deficit score and brain histopathologic damage score were dramatically higher in the ACA group. Quantitative measures of EEG, including onset time of EEG burst, time to normal trace, burst suppression ratio, and information quantity, were significantly lower for CA caused by asphyxia and correlated with the 96-h neurological outcome and survival. Characteristics of earlier post-resuscitation EEG differed between cardiac and respiratory causes. Quantitative measures of EEG not only predicted neurological outcome and survival, but also have the potential to stratify CA with different causes.

  11. Electroencephalogram and magnetic resonance imaging comparison as a predicting factor for neurodevelopmental outcome in hypoxic ischemic encephalopathy infant treated with hypothermia

    Directory of Open Access Journals (Sweden)

    Francesca Del Balzo

    2014-10-01

    Full Text Available Hypoxic-ischemic encephalopathy (HIE is an important cause of acute neurological damage in newborns at (or near term. Several trials in recent years have shown that moderate hypothermia by total body cooling or selective head is an effective intervention to reduce mortality and major disability in infants survived a perinatal hypoxic-ischemic attack. Follow-up in these patients is very important to establish neurodevelopmental outcome, and specific markers can lead us to detect predicting sign for good or poor outcome. We reported a few cases of newborn with HIE treated with hypothermia, in whom the comparison between electroencephalogram (EEG and magnetic resonance imaging (MRI represents the first marker for neurodevelopment outcome prediction. The continuous EEG monitoring showed a depressed EEG activity with diffuse burst depression in 7 patients. No epileptic abnormalities were registered. In 10 out of 20 patients no abnormalities of the background activity and no epileptic abnormalities were observed. We found that a depressed EEG activity during the first 72 h of life and a diffused alteration of basal ganglia at MRI were correlated with a poor neurodevelopmental outcome at 18 months of follow-up.

  12. The Utility of EEG in Attention Deficit Hyperactivity Disorder: A Replication Study.

    Science.gov (United States)

    Swatzyna, Ronald J; Tarnow, Jay D; Roark, Alexandra; Mardick, Jacob

    2017-07-01

    The routine use of stimulants in pediatrics has increased dramatically over the past 3 decades and the long-term consequences have yet to be fully studied. Since 1978 there have been 7 articles identifying electroencephalogram (EEG) abnormalities, particularly epileptiform discharges in children with attention deficit hyperactivity disorder (ADHD). Many have studied the prevalence of these discharges in this population with varying results. An article published in 2011 suggests that EEG technology should be considered prior to prescribing stimulants to children diagnosed with ADHD due to a high prevalence of epileptiform discharges. The 2011 study found a higher prevalence (26%) of epileptiform discharges when using 23-hour and sleep-deprived EEGs in comparison with other methods of activation (hyperventilation or photostimulation) and conventional EEG. We sought to replicate the 2011 results using conventional EEG with the added qEEG technologies of automatic spike detection and low-resolution electromagnetic tomography analysis (LORETA) brain mapping. Our results showed 32% prevalence of epileptiform discharges, which suggests that an EEG should be considered prior to prescribing stimulant medications.

  13. Feature study of hysterical blindness EEG based on FastICA with combined-channel information.

    Science.gov (United States)

    Qin, Xuying; Wang, Wei; Hu, Lintao; Wang, Xu; Yuan, Xiaojie

    2015-01-01

    An appropriate feature study of hysteria electroencephalograms (EEG) would provide new insights into neural mechanisms of the disease, and also make improvements in patient diagnosis and management. The objective of this paper is to provide an explanation for what causes a particular visual loss, by associating the features of hysterical blindness EEG with brain function. An idea for the novel feature extraction for hysterical blindness EEG, utilizing combined-channel information, was applied in this paper. After channels had been combined, the sliding-window-FastICA was applied to process the combined normal EEG and hysteria EEG, respectively. Kurtosis features were calculated from the processed signals. As the comparison feature, the power spectral density of normal and hysteria EEG were computed. According to the feature analysis results, a region of brain dysfunction was located at the occipital lobe, O1 and O2. Furthermore, new abnormality was found at the parietal lobe, C3, C4, P3, and P4, that provided us with a new perspective for understanding hysterical blindness. Indicated by the kurtosis results which were consistent with brain function and the clinical diagnosis, our method was found to be a useful tool to capture features in hysterical blindness EEG.

  14. Epileptogenic developmental venous anomaly: insights from simultaneous EEG/fMRI.

    Science.gov (United States)

    Scheidegger, Olivier; Wiest, Roland; Jann, Kay; König, Thomas; Meyer, Klaus; Hauf, Martinus

    2013-04-01

    Developmental venous anomalies (DVAs) are associated with epileptic seizures; however, the role of DVA in the epileptogenesis is still not established. Simultaneous interictal electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) recordings provide supplementary information to electroclinical data about the epileptic generators, and thus aid in the differentiation of clinically equivocal epilepsy syndromes. The main objective of our study was to characterize the epileptic network in a patient with DVA and epilepsy by simultaneous EEG/fMRI recordings. A 17-year-old woman with recently emerging generalized tonic-clonic seizures, and atypical generalized discharges, was investigated using simultaneous EEG/fMRI at the university hospital. Previous high-resolution MRI showed no structural abnormalities, except a DVA in the right frontal operculum. Interictal EEG recordings showed atypical generalized discharges, corresponding to positive focal blood oxygen level dependent (BOLD) correlates in the right frontal operculum, a region drained by the DVA. Additionally, widespread cortical bilateral negative BOLD correlates in the frontal and parietal lobes were delineated, resembling a generalized epileptic network. The EEG/fMRI recordings support a right frontal lobe epilepsy, originating in the vicinity of the DVA, propagating rapidly to both frontal and parietal lobes, as expressed on the scalp EEG by secondary bilateral synchrony. The DVA may be causative of focal epilepsies in cases where no concomitant epileptogenic lesions can be detected. Advanced imaging techniques, such as simultaneous EEG/fMRI, may thus aid in the differentiation of clinically equivocal epilepsy syndromes.

  15. Content-specific coordination of listeners' to speakers' EEG during communication.

    Science.gov (United States)

    Kuhlen, Anna K; Allefeld, Carsten; Haynes, John-Dylan

    2012-01-01

    Cognitive neuroscience has recently begun to extend its focus from the isolated individual mind to two or more individuals coordinating with each other. In this study we uncover a coordination of neural activity between the ongoing electroencephalogram (EEG) of two people-a person speaking and a person listening. The EEG of one set of twelve participants ("speakers") was recorded while they were narrating short stories. The EEG of another set of twelve participants ("listeners") was recorded while watching audiovisual recordings of these stories. Specifically, listeners watched the superimposed videos of two speakers simultaneously and were instructed to attend either to one or the other speaker. This allowed us to isolate neural coordination due to processing the communicated content from the effects of sensory input. We find several neural signatures of communication: First, the EEG is more similar among listeners attending to the same speaker than among listeners attending to different speakers, indicating that listeners' EEG reflects content-specific information. Secondly, listeners' EEG activity correlates with the attended speakers' EEG, peaking at a time delay of about 12.5 s. This correlation takes place not only between homologous, but also between non-homologous brain areas in speakers and listeners. A semantic analysis of the stories suggests that listeners coordinate with speakers at the level of complex semantic representations, so-called "situation models". With this study we link a coordination of neural activity between individuals directly to verbally communicated information.

  16. Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm

    Directory of Open Access Journals (Sweden)

    E. Parvinnia

    2014-01-01

    Full Text Available Electroencephalogram (EEG signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR model are extracted from EEG signals. The classification results are evaluated using Leave one (subject out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.

  17. Automated EEG-based screening of depression using deep convolutional neural network.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat; Subha, D P

    2018-07-01

    In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI). Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Changes in the somatosensory evoked potentials and spontaneous electroencephalogram of hens during stunning in argon-induced anoxia.

    Science.gov (United States)

    Raj, A B; Gregory, N G; Wotton, S B

    1991-01-01

    This study examined the time to loss of consciousness in hens during stunning in argon-induced anoxia. Somatosensory evoked potentials (SEPs) and the spontaneous electroencephalogram (EEG) were recorded in 12 culled hens prior to and during stunning in less than 2% oxygen (air displaced by argon). An additional 20 hens were stunned with a similar concentration of oxygen and the time to loss of posture, eye closure, and the onset and duration of clonic and tonic convulsions were recorded. A further 10 hens were immersed in less than 2% oxygen for 15-17 s and their response to comb pinching was tested as soon as they had been transferred to atmospheric air. It is concluded that the birds had not lost the primary response in their SEPs by the time they started convulsing, but the reduction in the amplitude of the SEPs, changes in their spontaneous EEG and a negative response to comb pinch before the start of the convulsions indicated that the birds were unconscious when they convulsed.

  19. The evaluation value of the quantitative electroencephalogram for the prognosis of neonatal hypoxic ischemic encephalopathy and its relationship with serological indicators

    Directory of Open Access Journals (Sweden)

    Ting-Mei Dou

    2017-06-01

    Full Text Available Objective: To study the evaluation value of the quantitative electroencephalogram (qEEG for the prognosis of neonatal hypoxic ischemic encephalopathy (HIE and its relationship with serological indicators. Methods: 76 children with HIE who were born and treated in our hospital between April 2013 and February 2017 were collected as observation group, and 50 healthy newborns who were born in our hospital during the same period were collected as normal control group. qEEG parameter values of two groups of children were determined, serum levels of nerve injury indexes, nerve apoptosis indexes and oxidative stress indexes were compared between the two groups, and Pearson test was used to evaluate the inner link between qEEG parameter values and disease severity in children with HIE. Results: qEEG Fp1, Fp2, C3, C4, T3, T4, O1 and O2 loci power spectrum values of observation group were significantly lower than those of normal control group. Serum NSE, NPY, S-100B and MBP contents in observation group were higher than those in normal control group; nerve apoptosis indexes sFas, sFasL and Caspase-3 contents were higher than those in normal control group while Bcl-2 content was lower than that in normal control group; serum oxidative stress indexes AOPP and MDA contents were higher than those in normal control group while SOD content was lower than that in normal control group. Pearson test showed that qEEG Fp1, Fp2, C3, C4, T3, T4, O1 and O2 loci power spectrum values in children with HIE were directly correlated with the contents of nerve injury indexes, nerve apoptosis indexes and oxidative stress indexes. Conclusion: The qEEG parameter values in children with HIE are lower than those in normal children, and the specific values are closely related to the severity of the disease.

  20. Increased frontal electroencephalogram theta amplitude in patients with anorexia nervosa compared to healthy controls

    Directory of Open Access Journals (Sweden)

    Hestad KA

    2016-09-01

    Full Text Available Knut A Hestad,1–3 Siri Weider,3,4 Kristian Bernhard Nilsen,5–7 Marit Sæbø Indredavik,8,9 Trond Sand7,10 1Department of Research, Innlandet Hospital Trust, Brumunddal, Norway; 2Department of Public Health, Hedmark University of Applied Sciences, Elverum, Norway; 3Department of Psychology, Faculty of Social Sciences and Technology Management, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 4Department of Psychiatry, Specialised Unit for Eating Disorder Patients, Levanger Hospital, Health Trust Nord-Trøndelag, Levanger, Norway; 5Department of Neuroscience, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 6Department of Work Psychology and Physiology, National Institute of Occupational Health, Oslo, Norway; 7Department of Neurology, Section for Clinical Neurophysiology, Oslo University Hospital, Ullevål, Oslo, Norway; 8Regional Centre for Child and Youth Mental Health and Child Welfare, Faculty of Medicine, Norwegian University of Science and Technology (NTNU, Trondheim, Norway; 9Department of Child and Adolescent Psychiatry, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway; 10Department of Neurology and Clinical Neurophysiology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway Objective: To conduct a blind study of quantitative electroencephalogram-band amplitudes in patients with anorexia nervosa (AN and healthy controls.Methods: Twenty-one patients with AN and 24 controls were examined with eyes-closed 16-channel electroencephalogram. Main variables were absolute alpha, theta, and delta amplitudes in frontal, temporal, and posterior regions.Results: There were no significant differences between the AN patients and controls regarding absolute regional band amplitudes in µV. Borderline significance was found for anterior theta (P=0.051. Significantly increased left and right frontal electrode theta amplitude was found in AN patients (F3, P=0.014; F4, P

  1. Comment on the Nanoparticle Conclusions in Crüts et al. (2008, "Exposure to diesel exhaust induces changes in EEG in human volunteers"

    Directory of Open Access Journals (Sweden)

    Long Christopher M

    2008-07-01

    Full Text Available Abstract A recent publication in this journal reported interesting changes in electroencephalographic (EEG waves that occurred in 10 young, male volunteers following inhalation for one hour of elevated levels of diesel-engine exhaust fumes 1. The authors then proposed a chain of causal events that they hypothesized underlay their observed EEG changes. Their reasoning linked the observed results to nanoparticles in diesel-engine exhaust (DEE, and went on to suggest that associations between changes in ambient particulate matter (PM levels and changes in health statistics might be due to the effects of diesel-engine exhaust (DEE nanoparticles on EEG. We suggest that the extrapolations of the Crüts et al. EEG findings to casual mechanisms about how ambient levels of DEE particulate might affect electrical signals in the brain, and subsequently to how DEE particulate might alter disease risk, are premature.

  2. On analysis of electroencephalogram by multiresolution-based energetic approach

    Science.gov (United States)

    Sevindir, Hulya Kodal; Yazici, Cuneyt; Siddiqi, A. H.; Aslan, Zafer

    2013-10-01

    Epilepsy is a common brain disorder where the normal neuronal activity gets affected. Electroencephalography (EEG) is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy. On a standard EEG some abnormalities indicate epileptic activity. EEG signals like many biomedical signals are highly non-stationary by their nature. For the investigation of biomedical signals, in particular EEG signals, wavelet analysis have found prominent position in the study for their ability to analyze such signals. Wavelet transform is capable of separating the signal energy among different frequency scales and a good compromise between temporal and frequency resolution is obtained. The present study is an attempt for better understanding of the mechanism causing the epileptic disorder and accurate prediction of occurrence of seizures. In the present paper following Magosso's work [12], we identify typical patterns of energy redistribution before and during the seizure using multiresolution wavelet analysis on Kocaeli University's Medical School's data.

  3. Independent EEG sources are dipolar.

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

    Full Text Available Independent component analysis (ICA and blind source separation (BSS methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA; best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison.

  4. Sleep/Wake Physiology and Quantitative Electroencephalogram Analysis of the Neuroligin-3 Knockout Rat Model of Autism Spectrum Disorder.

    Science.gov (United States)

    Thomas, Alexia M; Schwartz, Michael D; Saxe, Michael D; Kilduff, Thomas S

    2017-10-01

    Neuroligin-3 (NLGN3) is one of the many genes associated with autism spectrum disorder (ASD). Sleep dysfunction is highly prevalent in ASD, but has not been rigorously examined in ASD models. Here, we evaluated sleep/wake physiology and behavioral phenotypes of rats with genetic ablation of Nlgn3. Male Nlgn3 knockout (KO) and wild-type (WT) rats were assessed using a test battery for ASD-related behaviors and also implanted with telemeters to record the electroencephalogram (EEG), electromyogram, body temperature, and locomotor activity. 24-h EEG recordings were analyzed for sleep/wake states and spectral composition. Nlgn3 KO rats were hyperactive, exhibited excessive chewing behavior, and had impaired prepulse inhibition to an auditory startle stimulus. KO rats also spent less time in non-rapid eye movement (NREM) sleep, more time in rapid eye movement (REM) sleep, exhibited elevated theta power (4-9 Hz) during wakefulness and REM, and elevated delta power (0.5-4 Hz) during NREM. Beta (12-30 Hz) power and gamma (30-50 Hz) power were suppressed across all vigilance states. The sleep disruptions in Nlgn3 KO rats are consistent with observations of sleep disturbances in ASD patients. The EEG provides objective measures of brain function to complement rodent behavioral analyses and therefore may be a useful tool to study ASD. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  5. Power spectral analysis of the sleep electroencephalogram in heartburn patients with or without gastroesophageal reflux disease: a feasibility study.

    Science.gov (United States)

    Budhiraja, Rohit; Quan, Stuart F; Punjabi, Naresh M; Drake, Christopher L; Dickman, Ram; Fass, Ronnie

    2010-02-01

    Determine the feasibility of using power spectrum of the sleep electroencephalogram (EEG) as a more sensitive tool than sleep architecture to evaluate the relationship between gastroesophageal reflux disease (GERD) and sleep. GERD has been shown to adversely affect subjective sleep reports but not necessarily objective sleep parameters. Data were prospectively collected from symptomatic patients with heartburn. All symptomatic patients underwent upper endoscopy. Patients without erosive esophagitis underwent pH testing. Sleep was polygraphically recorded in the laboratory. Spectral analysis was performed to determine the power spectrum in 4 bandwidths: delta (0.8 to 4.0 Hz), theta (4.1 to 8.0 Hz), alpha (8.1 to 13.0 Hz), and beta (13.1 to 20.0 Hz). Eleven heartburn patients were included in the GERD group (erosive esophagitis) and 6 heartburn patients in the functional heartburn group (negative endoscopy, pH test, response to proton pump inhibitors). The GERD patients had evidence of lower average delta-power than functional heartburn patients. Patients with GERD had greater overall alpha-power in the latter half of the night (3 hours after sleep onset) than functional heartburn patients. No significant differences were noted in conventional sleep stage summaries between the 2 groups. Among heartburn patients with GERD, EEG spectral power during sleep is shifted towards higher frequencies compared with heartburn patients without GERD despite similar sleep architecture. This feasibility study demonstrated that EEG spectral power during sleep might be the preferred tool to provide an objective analysis about the effect of GERD on sleep.

  6. EEG activation differences in the pre-motor cortex and supplementary motor area between normal individuals with high and low traits of autism.

    Science.gov (United States)

    Puzzo, Ignazio; Cooper, Nicholas R; Vetter, Petra; Russo, Riccardo

    2010-06-25

    The human mirror neuron system (hMNS) is believed to provide a basic mechanism for social cognition. Event-related desynchronization (ERD) in alpha (8-12Hz) and low beta band (12-20Hz) over sensori-motor cortex has been suggested to index mirror neurons' activity. We tested whether autistic traits revealed by high and low scores on the Autistic Quotient (AQ) in the normal population are linked to variations in the electroencephalogram (EEG) over motor, pre-motor cortex and supplementary motor area (SMA) during action observation. Results revealed that in the low AQ group, the pre-motor cortex and SMA were more active during hand action than static hand observation whereas in the high AQ group the same areas were active both during static and hand action observation. In fact participants with high traits of autism showed greater low beta ERD while observing the static hand than those with low traits and this low beta ERD was not significantly different when they watched hand actions. Over primary motor cortex, the classical alpha and low beta ERD during hand actions relative to static hand observation was found across all participants. These findings suggest that the observation-execution matching system works differently according to the degree of autism traits in the normal population and that this is differentiated in terms of the EEG according to scalp site and bandwidth. Copyright 2010 Elsevier B.V. All rights reserved.

  7. Sub-paresthesia spinal cord stimulation reverses thermal hyperalgesia and modulates low frequency EEG in a rat model of neuropathic pain.

    Science.gov (United States)

    Koyama, Suguru; Xia, Jimmy; Leblanc, Brian W; Gu, Jianwen Wendy; Saab, Carl Y

    2018-05-08

    Paresthesia, a common feature of epidural spinal cord stimulation (SCS) for pain management, presents a challenge to the double-blind study design. Although sub-paresthesia SCS has been shown to be effective in alleviating pain, empirical criteria for sub-paresthesia SCS have not been established and its basic mechanisms of action at supraspinal levels are unknown. We tested our hypothesis that sub-paresthesia SCS attenuates behavioral signs of neuropathic pain in a rat model, and modulates pain-related theta (4-8 Hz) power of the electroencephalogram (EEG), a previously validated correlate of spontaneous pain in rodent models. Results show that sub-paresthesia SCS attenuates thermal hyperalgesia and power amplitude in the 3-4 Hz range, consistent with clinical data showing significant yet modest analgesic effects of sub-paresthesia SCS in humans. Therefore, we present evidence for anti-nociceptive effects of sub-paresthesia SCS in a rat model of neuropathic pain and further validate EEG theta power as a reliable 'biosignature' of spontaneous pain.

  8. Correlation of Tc-99m HMPAO SPECT with surface EEG, MR, and CT for noninvasive localization of seizure foci

    International Nuclear Information System (INIS)

    Engelstad, B.L.; Laxer, K.D.; Dickson, H.S.; Cooper, K.E.; Huberty, J.P.; White, D.L.

    1987-01-01

    Some patients with refractory seizure disorders are candidates for surgical management. Correct preoperative lateralization is essential. Of 19 patients with seizure disorders who underwent Tc-99m HMPAO SPECT, 14 were considered to have ultimately had definitive localization by other means: consistently abnormal surface electroencephalogram (EEG), subdural or deep electrode EEG, EEG response to resection, abnormal histopatholgy, or grossly abnormal MR image. Lateralization with SPECT was (1) focal or regional hypoperfusion (11 patients) or (2) discrete focal hyperperfusion (one patient). Correct lateralization was obtained in ten of 14 with SPECT, nine of 14 with surface EEG, seven of 12 with MR, and one of eight with CT. Preoperative evaluation of patients with medically refractory seizures can be aided by Tc-99m HMPAO SPECT

  9. Extraction of features from sleep EEG for Bayesian assessment of brain development.

    Directory of Open Access Journals (Sweden)

    Vitaly Schetinin

    Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

  10. Qualitative and quantitative EEG abnormalities in violent offenders with antisocial personality disorder.

    Science.gov (United States)

    Reyes, Ana Calzada; Amador, Alfredo Alvarez

    2009-02-01

    Resting eyes closed electroencephalogram was studied in a group of violent offenders evaluated at Psychiatric Department of the Legal Medicine Institute in Cuba (18 with antisocial personality disorder, ASPD, and 10 without psychiatric diagnosis). Characteristics of the EEG visual inspection and the use of frequency domain quantitative analysis techniques (narrow band spectral parameters) are described. Both groups were compared to Cuban normative database. High incidences of electroencephalographic abnormalities were found in both groups of violent offenders. The most frequent were: electrogenesis alterations, attenuated alpha rhythm and theta and delta activities increase in the frontal lobe. In the quantitative analysis theta and delta frequencies were increased and alpha activity was decreased in both groups. Differences appear for the topographical patterns present in subjects of both groups. EEG abnormalities were more severe in ASPD than in control group. Results suggest that EEG abnormalities in violent offenders should reflect aspects of brain dysfunction related to antisocial behaviour.

  11. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    Directory of Open Access Journals (Sweden)

    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  12. Estimation of alertness levels with changes in decibel scale wavelength of EEG during dual-task simulation of auditory sonar target detection.

    Science.gov (United States)

    Arjunan, Sridhar P; Kumar, Dinesh K; Jung, Tzyy-Ping

    2010-01-01

    Changes in alertness levels can have dire consequences for people operating and controlling motorized equipment. Past research studies have shown the relationship of Electroencephalogram (EEG) with alertness of the person. This research reports the fractal analysis of EEG and estimation of the alertness levels of the individual based on the changes in the maximum fractal length (MFL) of EEG. The results indicate that MFL of only 2 channels of EEG can be used to identify the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82. This study has also reported that using the changes in MFL of EEG, the changes in alertness level of a person was estimated with a mean correlation coefficient = 0.69.

  13. EEG spectral phenotypes: heritability and association with marijuana and alcohol dependence in an American Indian community study

    OpenAIRE

    Ehlers, Cindy L.; Phillips, Evelyn; Gizer, Ian R.; Gilder, David A.; Wilhelmsen, Kirk C.

    2009-01-01

    Native Americans have some of the highest rates of marijuana and alcohol use and abuse, yet neurobiological measures associated with dependence on these substances in this population remain unknown. The present investigation evaluated the heritability of spectral characteristics of the electroencephalogram (EEG) and their correlation with marijuana and alcohol dependence in an American Indian community. Participants (n=626) were evaluated for marijuana (MJ) and alcohol (ALC) dependence, as we...

  14. Abnormal Resting-State Quantitative Electroencephalogram in Children With Central Auditory Processing Disorder: A Pilot Study.

    Science.gov (United States)

    Milner, Rafał; Lewandowska, Monika; Ganc, Małgorzata; Włodarczyk, Elżbieta; Grudzień, Diana; Skarżyński, Henryk

    2018-01-01

    In this study, we showed an abnormal resting-state quantitative electroencephalogram (QEEG) pattern in children with central auditory processing disorder (CAPD). Twenty-seven children (16 male, 11 female; mean age = 10.7 years) with CAPD and no symptoms of other developmental disorders, as well as 23 age- and sex-matched, typically developing children (TDC, 11 male, 13 female; mean age = 11.8 years) underwent examination of central auditory processes (CAPs) and QEEG evaluation consisting of two randomly presented blocks of "Eyes Open" (EO) or "Eyes Closed" (EC) recordings. Significant correlations between individual frequency band powers and CAP tests performance were found. The QEEG studies revealed that in CAPD relative to TDC there was no effect of decreased delta absolute power (1.5-4 Hz) in EO compared to the EC condition. Furthermore, children with CAPD showed increased theta power (4-8 Hz) in the frontal area, a tendency toward elevated theta power in EO block, and reduced low-frequency beta power (12-15 Hz) in the bilateral occipital and the left temporo-occipital regions for both EO and EC conditions. Decreased middle-frequency beta power (15-18 Hz) in children with CAPD was observed only in the EC block. The findings of the present study suggest that QEEG could be an adequate tool to discriminate children with CAPD from normally developing children. Correlation analysis shows relationship between the individual EEG resting frequency bands and the CAPs. Increased power of slow waves and decreased power of fast rhythms could indicate abnormal functioning (hypoarousal of the cortex and/or an immaturity) of brain areas not specialized in auditory information processing.

  15. Abnormal Resting-State Quantitative Electroencephalogram in Children With Central Auditory Processing Disorder: A Pilot Study

    Science.gov (United States)

    Milner, Rafał; Lewandowska, Monika; Ganc, Małgorzata; Włodarczyk, Elżbieta; Grudzień, Diana; Skarżyński, Henryk

    2018-01-01

    In this study, we showed an abnormal resting-state quantitative electroencephalogram (QEEG) pattern in children with central auditory processing disorder (CAPD). Twenty-seven children (16 male, 11 female; mean age = 10.7 years) with CAPD and no symptoms of other developmental disorders, as well as 23 age- and sex-matched, typically developing children (TDC, 11 male, 13 female; mean age = 11.8 years) underwent examination of central auditory processes (CAPs) and QEEG evaluation consisting of two randomly presented blocks of “Eyes Open” (EO) or “Eyes Closed” (EC) recordings. Significant correlations between individual frequency band powers and CAP tests performance were found. The QEEG studies revealed that in CAPD relative to TDC there was no effect of decreased delta absolute power (1.5–4 Hz) in EO compared to the EC condition. Furthermore, children with CAPD showed increased theta power (4–8 Hz) in the frontal area, a tendency toward elevated theta power in EO block, and reduced low-frequency beta power (12–15 Hz) in the bilateral occipital and the left temporo-occipital regions for both EO and EC conditions. Decreased middle-frequency beta power (15–18 Hz) in children with CAPD was observed only in the EC block. The findings of the present study suggest that QEEG could be an adequate tool to discriminate children with CAPD from normally developing children. Correlation analysis shows relationship between the individual EEG resting frequency bands and the CAPs. Increased power of slow waves and decreased power of fast rhythms could indicate abnormal functioning (hypoarousal of the cortex and/or an immaturity) of brain areas not specialized in auditory information processing.

  16. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

    Science.gov (United States)

    Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

    2018-01-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

  17. Nonlinear analysis of the electroencephalogram in depth of anesthesia

    Directory of Open Access Journals (Sweden)

    Oscar Leonardo Mosquera-Dussan

    2015-01-01

    Full Text Available El procesamiento digital de la señal de electroencefalograma (EEG ha tomado importancia en el monitoreo de profundidad anestésica, contribuyendo a una mejor técnica anestésica. El objetivo es realizar una revisión de métodos matemáticos no lineales aplicados recientemente al análisis de EEG la cual presenta características no lineales y no estacionarias. Una revisión fue desarrollada abarcando métodos matemáticos no lineales en el dominio del tiempo y frecuencia, los cuales han sido aplicados recientemente al análisis de EEG: Entropía Aproximada, Entropía Muestral, Entropía Espectral, Entropía Permutada, Transformada Wavelet, Entropía Wavelet, Bispectro, Bicoherencia y Transformada Hilbert Huang. Los algoritmos implementados fueron probados en un registro EEG de un paciente en la Clínica Universidad de La Sabana. Resultados publicados en la literatura a fin del tema son discutidos. Técnicas no lineales como el análisis de entropía, y la combinación con transformadas Wavelet y Hilbert Huang en el dominio de la frecuencia han presentado resultados prometedores en clasificación de estados de profundidad anestésica.

  18. Avian reflex and electroencephalogram responses in different states of consciousness.

    Science.gov (United States)

    Sandercock, Dale A; Auckburally, Adam; Flaherty, Derek; Sandilands, Victoria; McKeegan, Dorothy E F

    2014-06-22

    Defining states of clinical consciousness in animals is important in veterinary anaesthesia and in studies of euthanasia and welfare assessment at slaughter. The aim of this study was to validate readily observable reflex responses in relation to different conscious states, as confirmed by EEG analysis, in two species of birds under laboratory conditions (35-week-old layer hens (n=12) and 11-week-old turkeys (n=10)). We evaluated clinical reflexes and characterised electroencephalograph (EEG) activity (as a measure of brain function) using spectral analyses in four different clinical states of consciousness: conscious (fully awake), semi-conscious (sedated), unconscious-optimal (general anaesthesia), unconscious-sub optimal (deep hypnotic state), as well as assessment immediately following euthanasia. Jaw or neck muscle tone was the most reliable reflex measure distinguishing between conscious and unconscious states. Pupillary reflex was consistently observed until respiratory arrest. Nictitating membrane reflex persisted for a short time (power (PTOT) significantly increased, whereas median (F50) and spectral edge (F95) frequencies significantly decreased. This study demonstrates that EEG analysis can differentiate between clinical states (and loss of brain function at death) in birds and provides a unique integration of reflex responses and EEG activity. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. A generic EEG artifact removal algorithm based on the multi-channel Wiener filter

    Science.gov (United States)

    Somers, Ben; Francart, Tom; Bertrand, Alexander

    2018-06-01

    Objective. The electroencephalogram (EEG) is an essential neuro-monitoring tool for both clinical and research purposes, but is susceptible to a wide variety of undesired artifacts. Removal of these artifacts is often done using blind source separation techniques, relying on a purely data-driven transformation, which may sometimes fail to sufficiently isolate artifacts in only one or a few components. Furthermore, some algorithms perform well for specific artifacts, but not for others. In this paper, we aim to develop a generic EEG artifact removal algorithm, which allows the user to annotate a few artifact segments in the EEG recordings to inform the algorithm. Approach. We propose an algorithm based on the multi-channel Wiener filter (MWF), in which the artifact covariance matrix is replaced by a low-rank approximation based on the generalized eigenvalue decomposition. The algorithm is validated using both hybrid and real EEG data, and is compared to other algorithms frequently used for artifact removal. Main results. The MWF-based algorithm successfully removes a wide variety of artifacts with better performance than current state-of-the-art methods. Significance. Current EEG artifact removal techniques often have limited applicability due to their specificity to one kind of artifact, their complexity, or simply because they are too ‘blind’. This paper demonstrates a fast, robust and generic algorithm for removal of EEG artifacts of various types, i.e. those that were annotated as unwanted by the user.

  20. Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

    Science.gov (United States)

    Mesbah, Mostefa; Balakrishnan, Malarvili; Colditz, Paul B.; Boashash, Boualem

    2012-12-01

    This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

  1. Near-lossless multichannel EEG compression based on matrix and tensor decompositions.

    Science.gov (United States)

    Dauwels, Justin; Srinivasan, K; Reddy, M Ramasubba; Cichocki, Andrzej

    2013-05-01

    A novel near-lossless compression algorithm for multichannel electroencephalogram (MC-EEG) is proposed based on matrix/tensor decomposition models. MC-EEG is represented in suitable multiway (multidimensional) forms to efficiently exploit temporal and spatial correlations simultaneously. Several matrix/tensor decomposition models are analyzed in view of efficient decorrelation of the multiway forms of MC-EEG. A compression algorithm is built based on the principle of “lossy plus residual coding,” consisting of a matrix/tensor decomposition-based coder in the lossy layer followed by arithmetic coding in the residual layer. This approach guarantees a specifiable maximum absolute error between original and reconstructed signals. The compression algorithm is applied to three different scalp EEG datasets and an intracranial EEG dataset, each with different sampling rate and resolution. The proposed algorithm achieves attractive compression ratios compared to compressing individual channels separately. For similar compression ratios, the proposed algorithm achieves nearly fivefold lower average error compared to a similar wavelet-based volumetric MC-EEG compression algorithm.

  2. An EEG-Based Person Authentication System with Open-Set Capability Combining Eye Blinking Signals.

    Science.gov (United States)

    Wu, Qunjian; Zeng, Ying; Zhang, Chi; Tong, Li; Yan, Bin

    2018-01-24

    The electroencephalogram (EEG) signal represents a subject's specific brain activity patterns and is considered as an ideal biometric given its superior forgery prevention. However, the accuracy and stability of the current EEG-based person authentication systems are still unsatisfactory in practical application. In this paper, a multi-task EEG-based person authentication system combining eye blinking is proposed, which can achieve high precision and robustness. Firstly, we design a novel EEG-based biometric evoked paradigm using self- or non-self-face rapid serial visual presentation (RSVP). The designed paradigm could obtain a distinct and stable biometric trait from EEG with a lower time cost. Secondly, the event-related potential (ERP) features and morphological features are extracted from EEG signals and eye blinking signals, respectively. Thirdly, convolutional neural network and back propagation neural network are severally designed to gain the score estimation of EEG features and eye blinking features. Finally, a score fusion technology based on least square method is proposed to get the final estimation score. The performance of multi-task authentication system is improved significantly compared to the system using EEG only, with an increasing average accuracy from 92.4% to 97.6%. Moreover, open-set authentication tests for additional imposters and permanence tests for users are conducted to simulate the practical scenarios, which have never been employed in previous EEG-based person authentication systems. A mean false accepted rate (FAR) of 3.90% and a mean false rejected rate (FRR) of 3.87% are accomplished in open-set authentication tests and permanence tests, respectively, which illustrate the open-set authentication and permanence capability of our systems.

  3. Within- and across-trial dynamics of human EEG reveal cooperative interplay between reinforcement learning and working memory.

    Science.gov (United States)

    Collins, Anne G E; Frank, Michael J

    2018-03-06

    Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.

  4. A Pervasive Approach to EEG-Based Depression Detection

    Directory of Open Access Journals (Sweden)

    Hanshu Cai

    2018-01-01

    Full Text Available Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 (92 depressed patients and 121 normal controls subjects, was constructed. The electroencephalogram (EEG signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed that K-Nearest Neighbor (KNN had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis.

  5. Resting state EEG correlates of memory consolidation.

    Science.gov (United States)

    Brokaw, Kate; Tishler, Ward; Manceor, Stephanie; Hamilton, Kelly; Gaulden, Andrew; Parr, Elaine; Wamsley, Erin J

    2016-04-01

    Numerous studies demonstrate that post-training sleep benefits human memory. At the same time, emerging data suggest that other resting states may similarly facilitate consolidation. In order to identify the conditions under which non-sleep resting states benefit memory, we conducted an EEG (electroencephalographic) study of verbal memory retention across 15min of eyes-closed rest. Participants (n=26) listened to a short story and then either rested with their eyes closed, or else completed a distractor task for 15min. A delayed recall test was administered immediately following the rest period. We found, first, that quiet rest enhanced memory for the short story. Improved memory was associated with a particular EEG signature of increased slow oscillatory activity (rest can facilitate memory, and that this may occur via an active process of consolidation supported by slow oscillatory EEG activity and characterized by decreased attention to the external environment. Slow oscillatory EEG rhythms are proposed to facilitate memory consolidation during sleep by promoting hippocampal-cortical communication. Our findings suggest that EEG slow oscillations could play a significant role in memory consolidation during other resting states as well. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Random ensemble learning for EEG classification.

    Science.gov (United States)

    Hosseini, Mohammad-Parsa; Pompili, Dario; Elisevich, Kost; Soltanian-Zadeh, Hamid

    2018-01-01

    Real-time detection of seizure activity in epilepsy patients is critical in averting seizure activity and improving patients' quality of life. Accurate evaluation, presurgical assessment, seizure prevention, and emergency alerts all depend on the rapid detection of seizure onset. A new method of feature selection and classification for rapid and precise seizure detection is discussed wherein informative components of electroencephalogram (EEG)-derived data are extracted and an automatic method is presented using infinite independent component analysis (I-ICA) to select independent features. The feature space is divided into subspaces via random selection and multichannel support vector machines (SVMs) are used to classify these subspaces. The result of each classifier is then combined by majority voting to establish the final output. In addition, a random subspace ensemble using a combination of SVM, multilayer perceptron (MLP) neural network and an extended k-nearest neighbors (k-NN), called extended nearest neighbor (ENN), is developed for the EEG and electrocorticography (ECoG) big data problem. To evaluate the solution, a benchmark ECoG of eight patients with temporal and extratemporal epilepsy was implemented in a distributed computing framework as a multitier cloud-computing architecture. Using leave-one-out cross-validation, the accuracy, sensitivity, specificity, and both false positive and false negative ratios of the proposed method were found to be 0.97, 0.98, 0.96, 0.04, and 0.02, respectively. Application of the solution to cases under investigation with ECoG has also been effected to demonstrate its utility. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Simultaneous EEG/fMRI analysis of the resonance phenomena in steady-state visual evoked responses.

    Science.gov (United States)

    Bayram, Ali; Bayraktaroglu, Zubeyir; Karahan, Esin; Erdogan, Basri; Bilgic, Basar; Ozker, Muge; Kasikci, Itir; Duru, Adil D; Ademoglu, Ahmet; Oztürk, Cengizhan; Arikan, Kemal; Tarhan, Nevzat; Demiralp, Tamer

    2011-04-01

    The stability of the steady-state visual evoked potentials (SSVEPs) across trials and subjects makes them a suitable tool for the investigation of the visual system. The reproducible pattern of the frequency characteristics of SSVEPs shows a global amplitude maximum around 10 Hz and additional local maxima around 20 and 40 Hz, which have been argued to represent resonant behavior of damped neuronal oscillators. Simultaneous electroencephalogram/functional magnetic resonance imaging (EEG/fMRI) measurement allows testing of the resonance hypothesis about the frequency-selective increases in SSVEP amplitudes in human subjects, because the total synaptic activity that is represented in the fMRI-Blood Oxygen Level Dependent (fMRI-BOLD) response would not increase but get synchronized at the resonance frequency. For this purpose, 40 healthy volunteers were visually stimulated with flickering light at systematically varying frequencies between 6 and 46 Hz, and the correlations between SSVEP amplitudes and the BOLD responses were computed. The SSVEP frequency characteristics of all subjects showed 3 frequency ranges with an amplitude maximum in each of them, which roughly correspond to alpha, beta and gamma bands of the EEG. The correlation maps between BOLD responses and SSVEP amplitude changes across the different stimulation frequencies within each frequency band showed no significant correlation in the alpha range, while significant correlations were obtained in the primary visual area for the beta and gamma bands. This non-linear relationship between the surface recorded SSVEP amplitudes and the BOLD responses of the visual cortex at stimulation frequencies around the alpha band supports the view that a resonance at the tuning frequency of the thalamo-cortical alpha oscillator in the visual system is responsible for the global amplitude maximum of the SSVEP around 10 Hz. Information gained from the SSVEP/fMRI analyses in the present study might be extrapolated to the

  8. Effects of electrical water bath stunning current frequencies on the spontaneous electroencephalogram and somatosensory evoked potentials in hens.

    Science.gov (United States)

    Raj, A B M; O'Callaghan, M

    2004-04-01

    1. The effectiveness of water bath electrical stunning of chickens with a constant root mean square (rms) current of 100 mA per bird delivered for 3 s using 100, 200, 400, 800 and 1500 Hz sine wave alternating current (AC) was investigated in layer hens. The quantitative changes occurring in the electroencephalogram (EEG) and somatosensory evoked potentials (SEPs) were used to determine the effectiveness of stunning. The changes occurring in the EEG were evaluated using Fast Fourier Transformations (FFT) and the SEPs were averaged to determine whether they were present or abolished. 2. The results of FFT indicated that stunning of chickens with a constant rms current of 100 mA per bird using 100 or 200 Hz induced epileptiform activity in all the hens, immediately followed by a reduction in the total (2 to 30 Hz) and relative (13 to 30 Hz) power contents in the EEG frequency bands indicative of unconsciousness and insensibility. The SEPs were abolished in the majority of hens stunned with 100 Hz and all the hens stunned with 200 Hz. 3. By contrast, stunning using 400, 800 or 1500 Hz failed to induce epileptiform activity in all the birds, the total and relative power contents in the EEG frequency bands showed a substantial increase, rather than reduction, and the SEPs were also retained in the majority of chickens. It is therefore suggested that stunning using these frequencies failed to stun them satisfactorily. In these birds, occurrence of a painful arousal, rather than unconsciousness, could not be ruled out. 4. It is therefore suggested that water bath electrical stunning of chickens with a minimum rms current of 100 mA per bird delivered using 100 or 200 Hz would be adequate to ensure bird welfare under commercial conditions, provided both the carotid arteries in the neck are severed at slaughter. On humanitarian and bird welfare grounds, a rms current of greater than 100 mA per bird should be applied whilst using frequencies of 400 Hz or more of sine wave AC

  9. Functional ADA polymorphism increases sleep depth and reduces vigilant attention in humans.

    Science.gov (United States)

    Bachmann, Valérie; Klaus, Federica; Bodenmann, Sereina; Schäfer, Nikolaus; Brugger, Peter; Huber, Susanne; Berger, Wolfgang; Landolt, Hans-Peter

    2012-04-01

    Homeostatically regulated slow-wave oscillations in non-rapid eye movement (REM) sleep may reflect synaptic changes across the sleep-wake continuum and the restorative function of sleep. The nonsynonymous c.22G>A polymorphism (rs73598374) of adenosine deaminase (ADA) reduces the conversion of adenosine to inosine and predicts baseline differences in sleep slow-wave oscillations. We hypothesized that this polymorphism affects cognitive functions, and investigated whether it modulates electroencephalogram (EEG), behavioral, subjective, and biochemical responses to sleep deprivation. Attention, learning, memory, and executive functioning were quantified in healthy adults. Right-handed carriers of the variant allele (G/A genotype, n = 29) performed worse on the d2 attention task than G/G homozygotes (n = 191). To test whether this difference reflects elevated homeostatic sleep pressure, sleep and sleep EEG before and after sleep deprivation were studied in 2 prospectively matched groups of G/A and G/G genotype subjects. Deep sleep and EEG 0.75- to 1.5-Hz oscillations in non-REM sleep were significantly higher in G/A than in G/G genotype. Moreover, attention and vigor were reduced, whereas waking EEG alpha activity (8.5-12 Hz), sleepiness, fatigue, and α-amylase in saliva were enhanced. These convergent data demonstrate that genetic reduction of ADA activity elevates sleep pressure and plays a key role in sleep and waking quality in humans.

  10. [Electroencephalogram Feature Selection Based on Correlation Coefficient Analysis].

    Science.gov (United States)

    Zhou, Jinzhi; Tang, Xiaofang

    2015-08-01

    In order to improve the accuracy of classification with small amount of motor imagery training data on the development of brain-computer interface (BCD systems, we proposed an analyzing method to automatically select the characteristic parameters based on correlation coefficient analysis. Throughout the five sample data of dataset IV a from 2005 BCI Competition, we utilized short-time Fourier transform (STFT) and correlation coefficient calculation to reduce the number of primitive electroencephalogram dimension, then introduced feature extraction based on common spatial pattern (CSP) and classified by linear discriminant analysis (LDA). Simulation results showed that the average rate of classification accuracy could be improved by using correlation coefficient feature selection method than those without using this algorithm. Comparing with support vector machine (SVM) optimization features algorithm, the correlation coefficient analysis can lead better selection parameters to improve the accuracy of classification.

  11. Bayesian Correlated Component Analysis for inference of joint EEG activation

    DEFF Research Database (Denmark)

    Poulsen, Andreas Trier; Kamronn, Simon Due; Parra, Lucas

    2014-01-01

    We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset.......We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset....

  12. Hypnagogic imagery and EEG activity.

    Science.gov (United States)

    Hayashi, M; Katoh, K; Hori, T

    1999-04-01

    The relationships between hypnagogic imagery and EEG activity were studied. 7 subjects (4 women and 3 men) reported the content of hypnagogic imagery every minute and the hypnagogic EEGs were classified into 5 stages according to Hori's modified criteria. The content of the hypnagogic imagery changed as a function of the hypnagogic EEG stages.

  13. 76例抽动障碍患儿脑电图及临床特征分析%Analysis on Electroencephalogram and Clinical Features of 76 Children with Tic Disorders

    Institute of Scientific and Technical Information of China (English)

    陈国元; 黄杰; 黄毅; 张小娟

    2013-01-01

    Objective To observe the electroencephalogram(EEG) manifestations and clinical features of children with tic disorders. Methods Compare the differences of EEG manifestations between children with tic disorders and control group. Also, the EEG manifestations of different clinical types were analysised. Results Among the 76 cases, EEG manifestations of 39 were normal (51. 3%), and 37 cases abnormal (48. 7%). The abnormal percentage of EEG in TTD was statistically lower than that of CTD and TS(P<0. 05). In addition, sharp wave and sharp-slow wave complex were found mainly in EEG of children with CTD. Conclusion The abnormalities of EEG in children with TD is related to clinical types, which can provide referenced evidence for clinical duration and types of TD children.%目的 分析抽动障碍(Tic Disorders,TD)患儿脑电图(Electroencephalogram,EEG)表现及其临床特征.方法 对76例TD患儿及90例健康儿童脑电图进行比较分析,并比较不同临床类型抽动障碍患儿脑电图的差异.结果 76例TD患儿中EEG正常39例,占51.3%,异常35例,占48.7%;短暂抽动症(Transient Tic Disorders,TTD)患儿脑电图异常率明显低于慢性抽动症患儿(Chronic Tic Disorders,CTD)及多发性抽动症患儿(Tourette Syndrome,TS)(P<0.05),此外,尖波,尖慢综合波多见于慢性抽动症患儿.结论 TD患儿有较高EEG异常率,脑电图异常率与临床类型有一定关系,可作为TD诊断及提示病程及临床类型的参考依据.

  14. EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

    Directory of Open Access Journals (Sweden)

    Chun-Chuan Chen

    Full Text Available Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation. In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

  15. Distinct iEEG activity patterns in temporal-limbic and prefrontal sites induced by emotional intentionality.

    Science.gov (United States)

    Singer, Neomi; Podlipsky, Ilana; Esposito, Fabrizio; Okon-Singer, Hadas; Andelman, Fani; Kipervasser, Svetlana; Neufeld, Miri Y; Goebel, Rainer; Fried, Itzhak; Hendler, Talma

    2014-11-01

    Our emotions tend to be directed towards someone or something. Such emotional intentionality calls for the integration between two streams of information; abstract hedonic value and its associated concrete content. In a previous functional magnetic resonance imaging (fMRI) study we found that the combination of these two streams, as modeled by short emotional music excerpts and neutral film clips, was associated with synergistic activation in both temporal-limbic (TL) and ventral-lateral PFC (vLPFC) regions. This additive effect implies the integration of domain-specific 'affective' and 'cognitive' processes. Yet, the low temporal resolution of the fMRI limits the characterization of such cross-domain integration. To this end, we complemented the fMRI data with intracranial electroencephalogram (iEEG) recordings from twelve patients with intractable epilepsy. As expected, the additive fMRI activation in the amygdala and vLPFC was associated with distinct spatio-temporal iEEG patterns among electrodes situated within the vicinity of the fMRI activation foci. On the one hand, TL channels exhibited a transient (0-500 msec) increase in gamma power (61-69 Hz), possibly reflecting initial relevance detection or hedonic value tagging. On the other hand, vLPFC channels showed sustained (1-12 sec) suppression of low frequency power (2.3-24 Hz), possibly mediating changes in gating, enabling an on-going readiness for content-based processing of emotionally tagged signals. Moreover, an additive effect in delta-gamma phase-amplitude coupling (PAC) was found among the TL channels, possibly reflecting the integration between distinct domain specific processes. Together, this study provides a multi-faceted neurophysiological signature for computations that possibly underlie emotional intentionality in humans. Copyright © 2014 Elsevier Ltd. All rights reserved.

  16. Testing for significance of phase synchronisation dynamics in the EEG.

    Science.gov (United States)

    Daly, Ian; Sweeney-Reed, Catherine M; Nasuto, Slawomir J

    2013-06-01

    A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

  17. Modulatory effects of aromatherapy massage intervention on electroencephalogram, psychological assessments, salivary cortisol and plasma brain-derived neurotrophic factor.

    Science.gov (United States)

    Wu, Jin-Ji; Cui, Yanji; Yang, Yoon-Sil; Kang, Moon-Seok; Jung, Sung-Cherl; Park, Hyeung Keun; Yeun, Hye-Young; Jang, Won Jung; Lee, Sunjoo; Kwak, Young Sook; Eun, Su-Yong

    2014-06-01

    Aromatherapy massage is commonly used for the stress management of healthy individuals, and also has been often employed as a therapeutic use for pain control and alleviating psychological distress, such as anxiety and depression, in oncological palliative care patients. However, the exact biological basis of aromatherapy massage is poorly understood. Therefore, we evaluated here the effects of aromatherapy massage interventions on multiple neurobiological indices such as quantitative psychological assessments, electroencephalogram (EEG) power spectrum pattern, salivary cortisol and plasma brain-derived neurotrophic factor (BDNF) levels. A control group without treatment (n = 12) and aromatherapy massage group (n = 13) were randomly recruited. They were all females whose children were diagnosed as attention deficit hyperactivity disorder and followed up in the Department of Psychiatry, Jeju National University Hospital. Participants were treated with aromatherapy massage for 40 min twice per week for 4 weeks (8 interventions). A 4-week-aromatherapy massage program significantly improved all psychological assessment scores in the Stat-Trait Anxiety Index, Beck Depression Inventory and Short Form of Psychosocial Well-being Index. Interestingly, plasma BDNF levels were significantly increased after a 4 week-aromatherapy massage program. Alpha-brain wave activities were significantly enhanced and delta wave activities were markedly reduced following the one-time aromatherapy massage treatment, as shown in the meditation and neurofeedback training. In addition, salivary cortisol levels were significantly reduced following the one-time aromatherapy massage treatment. These results suggest that aromatherapy massage could exert significant influences on multiple neurobiological indices such as EEG pattern, salivary cortisol and plasma BDNF levels as well as psychological assessments. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Estimating the Integrated Information Measure Phi from High-Density Electroencephalography during States of Consciousness in Humans.

    Science.gov (United States)

    Kim, Hyoungkyu; Hudetz, Anthony G; Lee, Joseph; Mashour, George A; Lee, UnCheol

    2018-01-01

    The integrated information theory (IIT) proposes a quantitative measure, denoted as Φ, of the amount of integrated information in a physical system, which is postulated to have an identity relationship with consciousness. IIT predicts that the value of Φ estimated from brain activities represents the level of consciousness across phylogeny and functional states. Practical limitations, such as the explosive computational demands required to estimate Φ for real systems, have hindered its application to the brain and raised questions about the utility of IIT in general. To achieve practical relevance for studying the human brain, it will be beneficial to establish the reliable estimation of Φ from multichannel electroencephalogram (EEG) and define the relationship of Φ to EEG properties conventionally used to define states of consciousness. In this study, we introduce a practical method to estimate Φ from high-density (128-channel) EEG and determine the contribution of each channel to Φ. We examine the correlation of power, frequency, functional connectivity, and modularity of EEG with regional Φ in various states of consciousness as modulated by diverse anesthetics. We find that our approximation of Φ alone is insufficient to discriminate certain states of anesthesia. However, a multi-dimensional parameter space extended by four parameters related to Φ and EEG connectivity is able to differentiate all states of consciousness. The association of Φ with EEG connectivity during clinically defined anesthetic states represents a new practical approach to the application of IIT, which may be used to characterize various physiological (sleep), pharmacological (anesthesia), and pathological (coma) states of consciousness in the human brain.

  19. Long-Term Evolution Electromagnetic Fields Exposure Modulates the Resting State EEG on Alpha and Beta Bands.

    Science.gov (United States)

    Yang, Lei; Chen, Qinghua; Lv, Bin; Wu, Tongning

    2017-05-01

    Long-term evolution (LTE) wireless telecommunication systems are widely used globally, which has raised a concern that exposure to electromagnetic fields (EMF) emitted from LTE devices can change human neural function. To date, few studies have been conducted on the effect of exposure to LTE EMF. Here, we evaluated the changes in electroencephalogram (EEG) due to LTE EMF exposure. An LTE EMF exposure system with a stable power emission, which was equivalent to the maximum emission from an LTE mobile phone, was used to radiate the subjects. Numerical simulations were conducted to ensure that the specific absorption rate in the subject's head was below the safety limits. Exposure to LTE EMF reduced the spectral power and the interhemispheric coherence in the alpha and beta bands of the frontal and temporal brain regions. No significant change was observed in the spectral power and the inter-hemispheric coherence in different timeslots during and after the exposure. These findings also corroborated those of our previous study using functional magnetic resonant imaging.

  20. Prediction of advertisement preference by fusing EEG response and sentiment analysis.

    Science.gov (United States)

    Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

    2017-08-01

    This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Automatic removal of eye-movement and blink artifacts from EEG signals.

    Science.gov (United States)

    Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

    2010-03-01

    Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

  2. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

    Directory of Open Access Journals (Sweden)

    Chin-Teng Lin

    2018-01-01

    Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

  3. A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification

    Directory of Open Access Journals (Sweden)

    Nurhan Gursel Ozmen

    2018-01-01

    Full Text Available Classification of electroencephalogram (EEG signal is important in mental decoding for brain-computer interfaces (BCI. We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i resting state, (ii mental arithmetic, (iii imagination of left hand movement, (iv imagination of right hand movement, and (v imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%, indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

  4. Pharmaco-EEG Studies in Animals: A History-Based Introduction to Contemporary Translational Applications.

    Science.gov (United States)

    Drinkenburg, Wilhelmus H I M; Ahnaou, Abdallah; Ruigt, Gé S F

    2015-01-01

    Current research on the effects of pharmacological agents on human neurophysiology finds its roots in animal research, which is also reflected in contemporary animal pharmaco-electroencephalography (p-EEG) applications. The contributions, present value and translational appreciation of animal p-EEG-based applications are strongly interlinked with progress in recording and neuroscience analysis methodology. After the pioneering years in the late 19th and early 20th century, animal p-EEG research flourished in the pharmaceutical industry in the early 1980s. However, around the turn of the millennium the emergence of structurally and functionally revealing imaging techniques and the increasing application of molecular biology caused a temporary reduction in the use of EEG as a window into the brain for the prediction of drug efficacy. Today, animal p-EEG is applied again for its biomarker potential - extensive databases of p-EEG and polysomnography studies in rats and mice hold EEG signatures of a broad collection of psychoactive reference and test compounds. A multitude of functional EEG measures has been investigated, ranging from simple spectral power and sleep-wake parameters to advanced neuronal connectivity and plasticity parameters. Compared to clinical p-EEG studies, where the level of vigilance can be well controlled, changes in sleep-waking behaviour are generally a prominent confounding variable in animal p-EEG studies and need to be dealt with. Contributions of rodent pharmaco-sleep EEG research are outlined to illustrate the value and limitations of such preclinical p-EEG data for pharmacodynamic and chronopharmacological drug profiling. Contemporary applications of p-EEG and pharmaco-sleep EEG recordings in animals provide a common and relatively inexpensive window into the functional brain early in the preclinical and clinical development of psychoactive drugs in comparison to other brain imaging techniques. They provide information on the impact of

  5. Joint EEG/fMRI state space model for the detection of directed interactions in human brains—a simulation study

    International Nuclear Information System (INIS)

    Lenz, Michael; Linke, Yannick; Timmer, Jens; Schelter, Björn; Musso, Mariachristina; Weiller, Cornelius; Tüscher, Oliver

    2011-01-01

    An often addressed challenge in neuroscience research is the assignment of different tasks to specific brain regions. In many cases several brain regions are activated during a single task. Therefore, one is also interested in the temporal evolution of brain activity to infer causal relations between activated brain regions. These causal relations may be described by a directed, task specific network which consists of activated brain regions as vertices and directed edges. The edges describe the causal relations. Inference of the task specific brain network from measurements like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) is challenging, due to the low spatial resolution of the former and the low temporal resolution of the latter. Here, we present a simulation study investigating a possible combined analysis of simultaneously measured EEG and fMRI data to address the challenge specified above. A nonlinear state space model is used to distinguish between the underlying brain states and the (simulated) EEG/fMRI measurements. We make use of a modified unscented Kalman filter and a corresponding unscented smoother for the estimation of the underlying neural activity. Model parameters are estimated using an expectation-maximization algorithm, which exploits the partial linearity of our model. Inference of the brain network structure is then achieved using directed partial correlation, a measure for Granger causality. The results indicate that the convolution effect of the fMRI forward model imposes a big challenge for the parameter estimation and reduces the influence of the fMRI in combined EEG–fMRI models. It remains to be investigated whether other models or similar combinations of other modalities such as, e.g., EEG and magnetoencephalography can increase the profit of the promising idea of combining various modalities

  6. Relationship between CT findings and electroencephalograms of children with cerebral palsy

    International Nuclear Information System (INIS)

    Hirata, Yoshiaki; Nakano, Masao; Soumiya, Kyoichi; Ito, Masamitsu; Asano, Seiji; Tsukimura, Yasuharu.

    1983-01-01

    Electroencephalograms and CT findings of 43 infants with cerebral palsy were evaluated. Nineteen of them (44%) had abnormal findings of CT. The incidences of epileptic attacks and abnormal electroencephalograms were 33% and 84% in each group, respectively, with no differences between a normal-CT group and an abnormal-CT one. In electroencephalograms, positive spikes did not correlate with abnormal CT findings, but abnormal basal waves, particularly, low voltage dysrhythmia did well. Lazy activity also correlated with the abnormal CT findings. (Ueda, J.)

  7. The effect of different EEG derivations on sleep staging in rats: the frontal midline–parietal bipolar electrode for sleep scoring

    International Nuclear Information System (INIS)

    Fang, Guangzhan; Zhang, Chunpeng; Xia, Yang; Lai, Yongxiu; Liu, Tiejun; You, Zili; Yao, Dezhong

    2009-01-01

    Most sleep-staging research has focused on developing and optimizing algorithms for sleep scoring, but little attention has been paid to the effect of different electroencephalogram (EEG) derivations on sleep staging. To explore the possible effects of EEG derivations, an automatic computer method was established and confirmed by agreement analysis between the computer and two independent raters, and four fronto-parietal bipolar leads were compared for sleep scoring in rats. The results demonstrated that different bipolar electrodes have significantly different sleep-staging accuracies, and that the optimal frontal electrode for sleep scoring is located at the anterior midline

  8. Quantitative EEG and neurofeedback in children and adolescents: anxiety disorders, depressive disorders, comorbid addiction and attention-deficit/hyperactivity disorder, and brain injury.

    Science.gov (United States)

    Simkin, Deborah R; Thatcher, Robert W; Lubar, Joel

    2014-07-01

    This article explores the science surrounding neurofeedback. Both surface neurofeedback (using 2-4 electrodes) and newer interventions, such as real-time z-score neurofeedback (electroencephalogram [EEG] biofeedback) and low-resolution electromagnetic tomography neurofeedback, are reviewed. The limited literature on neurofeedback research in children and adolescents is discussed regarding treatment of anxiety, mood, addiction (with comorbid attention-deficit/hyperactivity disorder), and traumatic brain injury. Future potential applications, the use of quantitative EEG for determining which patients will be responsive to medications, the role of randomized controlled studies in neurofeedback research, and sensible clinical guidelines are considered. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Diagnostic Accuracy of microEEG: A Miniature, Wireless EEG Device

    OpenAIRE

    Grant, Arthur C.; Abdel-Baki, Samah G.; Omurtag, Ahmet; Sinert, Richard; Chari, Geetha; Malhotra, Schweta; Weedon, Jeremy; Fenton, Andre A.; Zehtabchi, Shahriar

    2014-01-01

    Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device (“microEEG”) to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). 225 ED patients with AMS underwent 3 EEGs. EEG1 (Nicolet Monitor, “reference”) and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. ...

  10. A compact electroencephalogram recording device with integrated audio stimulation system

    Science.gov (United States)

    Paukkunen, Antti K. O.; Kurttio, Anttu A.; Leminen, Miika M.; Sepponen, Raimo E.

    2010-06-01

    A compact (96×128×32 mm3, 374 g), battery-powered, eight-channel electroencephalogram recording device with an integrated audio stimulation system and a wireless interface is presented. The recording device is capable of producing high-quality data, while the operating time is also reasonable for evoked potential studies. The effective measurement resolution is about 4 nV at 200 Hz sample rate, typical noise level is below 0.7 μVrms at 0.16-70 Hz, and the estimated operating time is 1.5 h. An embedded audio decoder circuit reads and plays wave sound files stored on a memory card. The activities are controlled by an 8 bit main control unit which allows accurate timing of the stimuli. The interstimulus interval jitter measured is less than 1 ms. Wireless communication is made through bluetooth and the data recorded are transmitted to an external personal computer (PC) interface in real time. The PC interface is implemented with LABVIEW® and in addition to data acquisition it also allows online signal processing, data storage, and control of measurement activities such as contact impedance measurement, for example. The practical application of the device is demonstrated in mismatch negativity experiment with three test subjects.

  11. 3D Printed Dry EEG Electrodes.

    Science.gov (United States)

    Krachunov, Sammy; Casson, Alexander J

    2016-10-02

    Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise.

  12. 3D Printed Dry EEG Electrodes

    Directory of Open Access Journals (Sweden)

    Sammy Krachunov

    2016-10-01

    Full Text Available Electroencephalography (EEG is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI. A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability. A solution to this problem is to use dry electrodes, which do not require conductive gel, but tend to have a higher noise floor. This paper presents a novel methodology for the design and manufacture of such dry electrodes. We manufacture the electrodes using low cost desktop 3D printers and off-the-shelf components for the first time. This allows quick and inexpensive electrode manufacturing and opens the possibility of creating electrodes that are customized for each individual user. Our 3D printed electrodes are compared against standard wet electrodes, and the performance of the proposed electrodes is suitable for BCI applications, despite the presence of additional noise.

  13. Effects of tramadol or morphine in dogs undergoing castration on intra-operative electroencephalogram responses and post-operative pain.

    Science.gov (United States)

    Kongara, K; Chambers, J P; Johnson, C B; Dukkipati, V S R

    2013-11-01

    To compare the effects of pre-operatively administered tramadol with those of morphine on electroencephalographic responses to surgery and post-operative pain in dogs undergoing castration. Dogs undergoing castration were treated with either pre-operative morphine (0.5 mg/kg S/C, n = 8) or tramadol (3 mg/kg S/C, n = 8). All dogs also received 0.05 mg/kg acepromazine and 0.04 mg/kg atropine S/C in addition to the test analgesic. Anaesthesia was induced with thiopentone administered I/V to effect and maintained with halothane in oxygen. Respiratory rate, heart rate, end-tidal halothane tension (EtHal) and end-tidal CO2 tension (EtCO2) were monitored throughout surgery. Electroencephalograms (EEG) were recorded continuously using a three electrode montage. Median frequency (F50), total power (Ptot) and 95% spectral edge frequency (F95) derived from EEG power spectra recorded before skin incision (baseline) were compared with those recorded during ligation of the spermatic cords of both testicles. Post-operatively, pain was assessed after 1, 3, 6 and 9 h using the short form of the Glasgow composite measure pain scale (CMPS-SF). Dogs premedicated with tramadol had higher mean F50 (12.2 (SD 0.2) Hz) and lower Ptot (130.39 (SD 12.1) µv(2)) compared with those premedicated with morphine (11.5 (SD 0.2) Hz and 161.8 (SD 15.1) µv(2), respectively; p0.05). The F95 of the EEG did not differ between the two groups during the ligation of either testicle (p > 0.05). Post-operatively, no significant differences in the CMPS-SF score were found between animals premedicated with tramadol and morphine at any time during the post-operative period. No dog required rescue analgesia. Tramadol and morphine administered pre-operatively provided a similar degree of post-operative analgesia in male dogs at the doses tested.

  14. Preictal dynamics of EEG complexity in intracranially recorded epileptic seizure: a case report.

    Science.gov (United States)

    Bob, Petr; Roman, Robert; Svetlak, Miroslav; Kukleta, Miloslav; Chladek, Jan; Brazdil, Milan

    2014-11-01

    Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. With respect to previously reported findings suggesting specific changes in neural complexity during preictal period, we have used measure of pointwise correlation dimension (PD2) as a sensitive indicator of nonstationary changes in complexity of the electroencephalogram (EEG) signal. Although this measure of complexity in epileptic patients was previously reported by Feucht et al (Applications of correlation dimension and pointwise dimension for non-linear topographical analysis of focal onset seizures. Med Biol Comput. 1999;37:208-217), it was not used to study changes in preictal dynamics. With this aim to study preictal changes of EEG complexity, we have examined signals from 11 multicontact depth (intracerebral) EEG electrodes located in 108 cortical and subcortical brain sites, and from 3 scalp EEG electrodes in a patient with intractable epilepsy, who underwent preoperative evaluation before epilepsy surgery. From those 108 EEG contacts, records related to 44 electrode contacts implanted into lesional structures and white matter were not included into the experimental analysis.The results show that in comparison to interictal period (at about 8-6 minutes before seizure onset), there was a statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all 64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG channels as well. Presented results suggest that using PD2 in EEG analysis may have significant implications for research of preictal dynamics and prediction of epileptic seizures.

  15. FFT transformed quantitative EEG analysis of short term memory load.

    Science.gov (United States)

    Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana

    2015-07-01

    The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.

  16. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features.

    Science.gov (United States)

    Mognon, Andrea; Jovicich, Jorge; Bruzzone, Lorenzo; Buiatti, Marco

    2011-02-01

    A successful method for removing artifacts from electroencephalogram (EEG) recordings is Independent Component Analysis (ICA), but its implementation remains largely user-dependent. Here, we propose a completely automatic algorithm (ADJUST) that identifies artifacted independent components by combining stereotyped artifact-specific spatial and temporal features. Features were optimized to capture blinks, eye movements, and generic discontinuities on a feature selection dataset. Validation on a totally different EEG dataset shows that (1) ADJUST's classification of independent components largely matches a manual one by experts (agreement on 95.2% of the data variance), and (2) Removal of the artifacted components detected by ADJUST leads to neat reconstruction of visual and auditory event-related potentials from heavily artifacted data. These results demonstrate that ADJUST provides a fast, efficient, and automatic way to use ICA for artifact removal. Copyright © 2010 Society for Psychophysiological Research.

  17. Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

    Science.gov (United States)

    Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

    2014-06-01

    Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

  18. Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.

    Science.gov (United States)

    Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon

    2017-03-01

    To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P continuity and amplitude.

  19. Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

    Science.gov (United States)

    Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

    2018-04-01

    In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

  20. Spectral Electroencephalogram Analysis for the Evaluation of Encephalopathy Grade in Children With Acute Liver Failure.

    Science.gov (United States)

    Press, Craig A; Morgan, Lindsey; Mills, Michele; Stack, Cynthia V; Goldstein, Joshua L; Alonso, Estella M; Wainwright, Mark S

    2017-01-01

    Spectral electroencephalogram analysis is a method for automated analysis of electroencephalogram patterns, which can be performed at the bedside. We sought to determine the utility of spectral electroencephalogram for grading hepatic encephalopathy in children with acute liver failure. Retrospective cohort study. Tertiary care pediatric hospital. Patients between 0 and 18 years old who presented with acute liver failure and were admitted to the PICU. None. Electroencephalograms were analyzed by spectral analysis including total power, relative δ, relative θ, relative α, relative β, θ-to-Δ ratio, and α-to-Δ ratio. Normal values and ranges were first derived using normal electroencephalograms from 70 children of 0-18 years old. Age had a significant effect on each variable measured (p liver failure were available for spectral analysis. The median age was 4.3 years, 14 of 33 were male, and the majority had an indeterminate etiology of acute liver failure. Neuroimaging was performed in 26 cases and was normal in 20 cases (77%). The majority (64%) survived, and 82% had a good outcome with a score of 1-3 on the Pediatric Glasgow Outcome Scale-Extended at the time of discharge. Hepatic encephalopathy grade correlated with the qualitative visual electroencephalogram scores assigned by blinded neurophysiologists (rs = 0.493; p encephalopathy was correlated with a total power of less than or equal to 50% of normal for children 0-3 years old, and with a relative θ of less than or equal to 50% normal for children more than 3 years old (p > 0.05). Spectral electroencephalogram classification correlated with outcome (p encephalopathy and correlates with outcome. Spectral electroencephalogram may allow improved quantitative and reproducible assessment of hepatic encephalopathy grade in children with acute liver failure.

  1. A Prospective Study of Age-dependent Changes in Propofol-induced Electroencephalogram Oscillations in Children.

    Science.gov (United States)

    Lee, Johanna M; Akeju, Oluwaseun; Terzakis, Kristina; Pavone, Kara J; Deng, Hao; Houle, Timothy T; Firth, Paul G; Shank, Erik S; Brown, Emery N; Purdon, Patrick L

    2017-08-01

    In adults, frontal electroencephalogram patterns observed during propofol-induced unconsciousness consist of slow oscillations (0.1 to 1 Hz) and coherent alpha oscillations (8 to 13 Hz). Given that the nervous system undergoes significant changes during development, anesthesia-induced electroencephalogram oscillations in children may differ from those observed in adults. Therefore, we investigated age-related changes in frontal electroencephalogram power spectra and coherence during propofol-induced unconsciousness. We analyzed electroencephalogram data recorded during propofol-induced unconsciousness in patients between 0 and 21 yr of age (n = 97), using multitaper spectral and coherence methods. We characterized power and coherence as a function of age using multiple linear regression analysis and within four age groups: 4 months to 1 yr old (n = 4), greater than 1 to 7 yr old (n = 16), greater than 7 to 14 yr old (n = 30), and greater than 14 to 21 yr old (n = 47). Total electroencephalogram power (0.1 to 40 Hz) peaked at approximately 8 yr old and subsequently declined with increasing age. For patients greater than 1 yr old, the propofol-induced electroencephalogram structure was qualitatively similar regardless of age, featuring slow and coherent alpha oscillations. For patients under 1 yr of age, frontal alpha oscillations were not coherent. Neurodevelopmental processes that occur throughout childhood, including thalamocortical development, may underlie age-dependent changes in electroencephalogram power and coherence during anesthesia. These age-dependent anesthesia-induced electroencephalogram oscillations suggest a more principled approach to monitoring brain states in pediatric patients.

  2. Individual differences in the effects of mobile phone exposure on human sleep: rethinking the problem.

    Science.gov (United States)

    Loughran, Sarah P; McKenzie, Raymond J; Jackson, Melinda L; Howard, Mark E; Croft, Rodney J

    2012-01-01

    Mobile phone exposure-related effects on the human electroencephalogram (EEG) have been shown during both waking and sleep states, albeit with slight differences in the frequency affected. This discrepancy, combined with studies that failed to find effects, has led many to conclude that no consistent effects exist. We hypothesised that these differences might partly be due to individual variability in response, and that mobile phone emissions may in fact have large but differential effects on human brain activity. Twenty volunteers from our previous study underwent an adaptation night followed by two experimental nights in which they were randomly exposed to two conditions (Active and Sham), followed by a full-night sleep episode. The EEG spectral power was increased in the sleep spindle frequency range in the first 30 min of non-rapid eye movement (non-REM) sleep following Active exposure. This increase was more prominent in the participants that showed an increase in the original study. These results confirm previous findings of mobile phone-like emissions affecting the EEG during non-REM sleep. Importantly, this low-level effect was also shown to be sensitive to individual variability. Furthermore, this indicates that previous negative results are not strong evidence for a lack of an effect and, given the far-reaching implications of mobile phone research, we may need to rethink the interpretation of results and the manner in which research is conducted in this field. Copyright © 2011 Wiley Periodicals, Inc.

  3. Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.

    Science.gov (United States)

    Zhang, Jianhua; Yin, Zhong; Wang, Rubin

    2017-01-01

    This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.

  4. Measurement and modification of the EEG and related behavior

    Science.gov (United States)

    Sterman, M. B.

    1991-01-01

    Electrophysiological changes in the sensorimotor pathways were found to accompany the effect of rhythmic EEG patterns in the sensorimotor cortex. Additionally, several striking behavioral changes were seen, including in particular an enhancement of sleep and an elevation of seizure threshold to epileptogenic agents. This raised the possibility that human seizure disorders might be influenced therapeutically by similar training. Our objective in human EEG feedback training became not only the facilitation of normal rhythmic patterns, but also the suppression of abnormal activity, thus requiring complex contingencies directed to the normalization of the sensorimotor EEG. To achieve this, a multicomponent frequency analysis was developed to extract and separate normal and abnormal elements of the EEG signal. Each of these elements was transduced to a specific component of a visual display system, and these were combined through logic circuits to present the subject with a symbolic display. Variable criteria provided for the gradual shaping of EEG elements towards the desired normal pattern. Some 50-70% of patients with poorly controlled seizure disorders experienced therapeutic benefits from this approach in our laboratory, and subsequently in many others. A more recent application of this approach to the modification of human brain function in our lab has been directed to the dichotomous problems of task overload and underload in the contemporary aviation environment. At least 70% of all aviation accidents have been attributed to the impact of these kinds of problems on crew performance. The use of EEG in this context has required many technical innovations and the application of the latest advances in EEG signal analysis. Our first goal has been the identification of relevant EEG characteristics. Additionally, we have developed a portable recording and analysis system for application in this context. Findings from laboratory and in-flight studies suggest that we

  5. Does an intraneural interface short-term implant for robotic hand control modulate sensorimotor cortical integration? An EEG-TMS co-registration study on a human amputee.

    Science.gov (United States)

    Ferreri, F; Ponzo, D; Vollero, L; Guerra, A; Di Pino, G; Petrichella, S; Benvenuto, A; Tombini, M; Rossini, L; Denaro, L; Micera, S; Iannello, G; Guglielmelli, E; Denaro, V; Rossini, P M

    2014-01-01

    Following limb amputation, central and peripheral nervous system relays partially maintain their functions and can be exploited for interfacing prostheses. The aim of this study is to investigate, for the first time by means of an EEG-TMS co-registration study, whether and how direct bidirectional connection between brain and hand prosthesis impacts on sensorimotor cortical topography. Within an experimental protocol for robotic hand control, a 26 years-old, left-hand amputated male was selected to have implanted four intrafascicular electrodes (tf-LIFEs-4) in the median and ulnar nerves of the stump for 4 weeks. Before tf-LIFE-4s implant (T0) and after the training period, once electrodes have been removed (T1), experimental subject's cortico-cortical excitability, connectivity and plasticity were tested via a neuronavigated EEG-TMS experiment. The statistical analysis clearly demonstrated a significant modulation (with t-test p < 0.0001) of EEG activity between 30 and 100 ms post-stimulus for the stimulation of the right hemisphere. When studying individual latencies in that time range, a global amplitude modulation was found in most of the TMS-evoked potentials; particularly, the GEE analysis showed significant differences between T0 and T1 condition at 30 ms (p < 0.0404), 46 ms (p < 0.0001) and 60 ms (p < 0.007) latencies. Finally, also a clear local decrement in N46 amplitude over C4 was evident. No differences between conditions were observed for the stimulation of the left hemisphere. The results of this study confirm the hypothesis that bidirectional neural interface could redirect cortical areas -deprived of their original input/output functions- toward restorative neuroplasticity. This reorganization strongly involves bi-hemispheric networks and intracortical and transcortical modulation of GABAergic inhibition.

  6. Continuous EEG signal analysis for asynchronous BCI application.

    Science.gov (United States)

    Hsu, Wei-Yen

    2011-08-01

    In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

  7. Music increases frontal EEG coherence during verbal learning.

    Science.gov (United States)

    Peterson, David A; Thaut, Michael H

    2007-02-02

    Anecdotal and some empirical evidence suggests that music can enhance learning and memory. However, the mechanisms by which music modulates the neural activity associated with learning and memory remain largely unexplored. We evaluated coherent frontal oscillations in the electroencephalogram (EEG) while subjects were engaged in a modified version of Rey's Auditory Verbal Learning Test (AVLT). Subjects heard either a spoken version of the AVLT or the conventional AVLT word list sung. Learning-related changes in coherence (LRCC) were measured by comparing the EEG during word encoding on correctly recalled trials to the immediately preceding trial on which the same word was not recalled. There were no significant changes in coherence associated with conventional verbal learning. However, musical verbal learning was associated with increased coherence within and between left and right frontal areas in theta, alpha, and gamma frequency bands. It is unlikely that the different patterns of LRCC reflect general performance differences; the groups exhibited similar learning performance. The results suggest that verbal learning with a musical template strengthens coherent oscillations in frontal cortical networks involved in verbal encoding.

  8. Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

    Directory of Open Access Journals (Sweden)

    Mousa Kadhim Wali

    2013-01-01

    Full Text Available We classify the driver distraction level (neutral, low, medium, and high based on different wavelets and classifiers using wireless electroencephalogram (EEG signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS, music player, short message service (SMS, and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5. The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.

  9. Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings

    International Nuclear Information System (INIS)

    Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

    2009-01-01

    The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings

  10. Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings.

    Science.gov (United States)

    Escudero, Javier; Hornero, Roberto; Abásolo, Daniel

    2009-02-01

    The mutual information (MI) is a measure of both linear and nonlinear dependences. It can be applied to a time series and a time-delayed version of the same sequence to compute the auto-mutual information function (AMIF). Moreover, the AMIF rate of decrease (AMIFRD) with increasing time delay in a signal is correlated with its entropy and has been used to characterize biomedical data. In this paper, we aimed at gaining insight into the dependence of the AMIFRD on several signal processing concepts and at illustrating its application to biomedical time series analysis. Thus, we have analysed a set of synthetic sequences with the AMIFRD. The results show that the AMIF decreases more quickly as bandwidth increases and that the AMIFRD becomes more negative as there is more white noise contaminating the time series. Additionally, this metric detected changes in the nonlinear dynamics of a signal. Finally, in order to illustrate the analysis of real biomedical signals with the AMIFRD, this metric was applied to electroencephalogram (EEG) signals acquired with eyes open and closed and to ictal and non-ictal intracranial EEG recordings.

  11. Creutzfeldt-Jakob disease versus anti-LGI1 limbic encephalitis in a patient with progressive cognitive dysfunction, psychiatric symptoms, involuntary facio-brachio-crural movement, and an abnormal electroencephalogram: a case report

    Directory of Open Access Journals (Sweden)

    Sun L

    2015-06-01

    Full Text Available Li Sun, Jie Cao, Chang Liu, Yudan LvDepartment of Neurology, The First Hospital of JiLin University, ChangChun, People’s Republic of ChinaAbstract: Diagnosis of Creutzfeldt-Jakob disease (CJD is often challenging in elderly individuals, not only because of its variable clinical features but also because of nonspecific changes on the electroencephalogram (EEG in the early stages of the disease. Here we report on a patient who presented with progressive cognitive dysfunction, psychiatric symptoms, involuntary facio-brachio-crural movement, and an abnormal EEG. We provide a detailed analysis and differential diagnosis between anti-leucine-rich glioma inactivated 1 (LGI1 limbic encephalitis versus CJD, in the hope of providing a new understanding of CJD. A 65-year-old Chinese man presented with slowly progressive cognitive decline with psychiatric symptoms. On admission, he presented with facial grimacing and brief left upper limb dystonic posturing lasting 1–2 seconds, with hyponatremia that was difficult to rectify. Neurological examination showed increased muscle tension in the left limb but without pathological reflexes. His early EEG showed focal periodic wave complexes. Diffusion-weighted magnetic resonance imaging showed a suspected “lace sign” in the occipital cortex. His cerebrospinal fluid was negative for LGI1 antibodies and positive for 14-3-3 brain protein. Therefore, we made a presumptive diagnosis of CJD. At the following visit, a second EEG showed paroxysmal sharp wave complexes, but the patient had a poor prognosis. Atypical facio-brachio-crural movement and nonspecific EEG changes may occasionally be found in patients with CJD or anti-LGI1 encephalitis. Clinicians should not be dissuaded from a diagnosis of CJD where the EEG does not show paroxysmal sharp wave complexes in the early stages but abnormal facio-brachio-crural movement is present.Keywords: abnormal facio-brachio-crural movement, hyponatremia, Creutzfeldt

  12. Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liu

    2014-07-01

    Full Text Available Electroencephalogram-based emotion recognition (EEG-ER has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI. However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP, and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68% and arousal (84.79% among all testing methods.

  13. Methodological aspects of EEG and Body dynamics measurements during motion.

    Directory of Open Access Journals (Sweden)

    Pedro eReis

    2014-03-01

    Full Text Available EEG involves recording, analysis, and interpretation of voltages recorded on the human scalp originating from brain grey matter. EEG is one of the favorite methods to study and understand processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements, that are performed in response to the environment. However, there are methodological difficulties when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions of how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determination of real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.

  14. Role of EEG as Biomarker in the Early Detection and Classification of Dementia

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    Noor Kamal Al-Qazzaz

    2014-01-01

    Full Text Available The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

  15. Effects of 2G and 3G mobile phones on human alpha rhythms: Resting EEG in adolescents, young adults, and the elderly.

    Science.gov (United States)

    Croft, R J; Leung, S; McKenzie, R J; Loughran, S P; Iskra, S; Hamblin, D L; Cooper, N R

    2010-09-01

    The present study was conducted to determine whether adolescents and/or the elderly are more sensitive to mobile phone (MP)-related bioeffects than young adults, and to determine this for both 2nd generation (2G) GSM, and 3rd generation (3G) W-CDMA exposures. To test this, resting alpha activity (8-12 Hz band of the electroencephalogram) was assessed because numerous studies have now reported it to be enhanced by MP exposure. Forty-one 13-15 year olds, forty-two 19-40 year olds, and twenty 55-70 year olds were tested using a double-blind crossover design, where each participant received Sham, 2G and 3G exposures, separated by at least 4 days. Alpha activity, during exposure relative to baseline, was recorded and compared between conditions. Consistent with previous research, the young adults' alpha was greater in the 2G compared to Sham condition, however, no effect was seen in the adolescent or the elderly groups, and no effect of 3G exposures was found in any group. The results provide further support for an effect of 2G exposures on resting alpha activity in young adults, but fail to support a similar enhancement in adolescents or the elderly, or in any age group as a function of 3G exposure. 2010 Wiley-Liss, Inc.

  16. A collaborative brain-computer interface for improving human performance.

    Directory of Open Access Journals (Sweden)

    Yijun Wang

    Full Text Available Electroencephalogram (EEG based brain-computer interfaces (BCI have been studied since the 1970s. Currently, the main focus of BCI research lies on the clinical use, which aims to provide a new communication channel to patients with motor disabilities to improve their quality of life. However, the BCI technology can also be used to improve human performance for normal healthy users. Although this application has been proposed for a long time, little progress has been made in real-world practices due to technical limits of EEG. To overcome the bottleneck of low single-user BCI performance, this study proposes a collaborative paradigm to improve overall BCI performance by integrating information from multiple users. To test the feasibility of a collaborative BCI, this study quantitatively compares the classification accuracies of collaborative and single-user BCI applied to the EEG data collected from 20 subjects in a movement-planning experiment. This study also explores three different methods for fusing and analyzing EEG data from multiple subjects: (1 Event-related potentials (ERP averaging, (2 Feature concatenating, and (3 Voting. In a demonstration system using the Voting method, the classification accuracy of predicting movement directions (reaching left vs. reaching right was enhanced substantially from 66% to 80%, 88%, 93%, and 95% as the numbers of subjects increased from 1 to 5, 10, 15, and 20, respectively. Furthermore, the decision of reaching direction could be made around 100-250 ms earlier than the subject's actual motor response by decoding the ERP activities arising mainly from the posterior parietal cortex (PPC, which are related to the processing of visuomotor transmission. Taken together, these results suggest that a collaborative BCI can effectively fuse brain activities of a group of people to improve the overall performance of natural human behavior.

  17. Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Yuan-Pin Lin

    2017-07-01

    Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

  18. Temporal correlation between two channels EEG of bipolar lead in the head midline is associated with sleep-wake stages.

    Science.gov (United States)

    Li, Yanjun; Tang, Xiaoying; Xu, Zhi; Liu, Weifeng; Li, Jing

    2016-03-01

    Whether the temporal correlation between inter-leads Electroencephalogram (EEG) that located on the boundary between left and right brain hemispheres is associated with sleep stages or not is still unknown. The purpose of this paper is to evaluate the role of correlation coefficients between EEG leads Fpz-Cz and Pz-Oz for automatic classification of sleep stages. A total number of 39 EEG recordings (about 20 h each) were selected from the expanded sleep database in European data format for temporal correlation analysis. Original waveform of EEG was decomposed into sub-bands δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz) and β (13-30 Hz). The correlation coefficient between original EEG leads Fpz-Cz and Pz-Oz within frequency band 0.5-30 Hz was defined as r(EEG) and was calculated every 30 s, while that between the two leads EEG in sub-bands δ, θ, α and β were defined as r(δ), r(θ), r(α) and r(β), respectively. Classification of wakefulness and sleep was processed by fixed threshold that derived from the probability density function of correlation coefficients. There was no correlation between EEG leads Fpz-Cz and Pz-Oz during wakefulness (|r| r > 0.1 for r(EEG) and r(δ)), while low correlation existed during sleep (r ≈ -0.4 for r(EEG), r(δ), r(θ), r(α) and r(β)). There were significant differences (analysis of variance, P correlation index between EEG leads Fpz-Cz and Pz-Oz could distinguish all five types of wakefulness, rapid eye movement (REM) sleep, N1 sleep, N2 sleep and N3 sleep. In conclusion, the temporal correlation between EEG bipolar leads Fpz-Cz and Pz-Oz are highly associated with sleep-wake stages. Moreover, high accuracy of sleep-wake classification could be achieved by the temporal correlation within frequency band 0.5-30 Hz between EEG leads Fpz-Cz and Pz-Oz.

  19. Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

    Science.gov (United States)

    Zhang, Honghui; Su, Jianzhong; Wang, Qingyun; Liu, Yueming; Good, Levi; Pascual, Juan M.

    2018-03-01

    This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

  20. Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets

    Science.gov (United States)

    Grubov, V. V.; Sitnikova, E.; Pavlov, A. N.; Koronovskii, A. A.; Hramov, A. E.

    2017-11-01

    Epileptic activity in the form of spike-wave discharges (SWD) appears in the electroencephalogram (EEG) during absence seizures. This paper evaluates two approaches for detecting stereotypic rhythmic activities in EEG, i.e., the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). The CWT is a well-known method of time-frequency analysis of EEG, whereas EMD is a relatively novel approach for extracting signal's waveforms. A new method for pattern recognition based on combination of CWT and EMD is proposed. It was found that this combined approach resulted to the sensitivity of 86.5% and specificity of 92.9% for sleep spindles and 97.6% and 93.2% for SWD, correspondingly. Considering strong within- and between-subjects variability of sleep spindles, the obtained efficiency in their detection was high in comparison with other methods based on CWT. It is concluded that the combination of a wavelet-based approach and empirical modes increases the quality of automatic detection of stereotypic patterns in rat's EEG.

  1. Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach

    International Nuclear Information System (INIS)

    Assecondi, Sara; Hallez, Hans; Staelens, Steven; Lemahieu, Ignace; Bianchi, Anna M; Huiskamp, Geertjan M

    2009-01-01

    The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG-fMRI. We optimized the method to reduce the user's interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS-CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS. We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.

  2. EEG biometric identification: a thorough exploration of the time-frequency domain

    Science.gov (United States)

    DelPozo-Banos, Marcos; Travieso, Carlos M.; Weidemann, Christoph T.; Alonso, Jesús B.

    2015-10-01

    Objective. Although interest in using electroencephalogram (EEG) activity for subject identification has grown in recent years, the state of the art still lacks a comprehensive exploration of the discriminant information within it. This work aims to fill this gap, and in particular, it focuses on the time-frequency representation of the EEG. Approach. We executed qualitative and quantitative analyses of six publicly available data sets following a sequential experimentation approach. This approach was divided in three blocks analysing the configuration of the power spectrum density, the representation of the data and the properties of the discriminant information. A total of ten experiments were applied. Main results. Results show that EEG information below 40 Hz is unique enough to discriminate across subjects (a maximum of 100 subjects were evaluated here), regardless of the recorded cognitive task or the sensor location. Moreover, the discriminative power of rhythms follows a W-like shape between 1 and 40 Hz, with the central peak located at the posterior rhythm (around 10 Hz). This information is maximized with segments of around 2 s, and it proved to be moderately constant across montages and time. Significance. Therefore, we characterize how EEG activity differs across individuals and detail the optimal conditions to detect subject-specific information. This work helps to clarify the results of previous studies and to solve some unanswered questions. Ultimately, it will serve as guide for the design of future biometric systems.

  3. Convergence of EEG and fMRI measures of reward anticipation.

    Science.gov (United States)

    Gorka, Stephanie M; Phan, K Luan; Shankman, Stewart A

    2015-12-01

    Deficits in reward anticipation are putative mechanisms for multiple psychopathologies. Research indicates that these deficits are characterized by reduced left (relative to right) frontal electroencephalogram (EEG) activity and blood oxygenation level-dependent (BOLD) signal abnormalities in mesolimbic and prefrontal neural regions during reward anticipation. Although it is often assumed that these two measures capture similar mechanisms, no study to our knowledge has directly examined the convergence between frontal EEG alpha asymmetry and functional magnetic resonance imaging (fMRI) during reward anticipation in the same sample. Therefore, the aim of the current study was to investigate if and where in the brain frontal EEG alpha asymmetry and fMRI measures were correlated in a sample of 40 adults. All participants completed two analogous reward anticipation tasks--once during EEG data collection and the other during fMRI data collection. Results indicated that the two measures do converge and that during reward anticipation, increased relative left frontal activity is associated with increased left anterior cingulate cortex (ACC)/medial prefrontal cortex (mPFC) and left orbitofrontal cortex (OFC) activation. This suggests that the two measures may similarly capture PFC functioning, which is noteworthy given the role of these regions in reward processing and the pathophysiology of disorders such as depression and schizophrenia. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

    Science.gov (United States)

    Zhang, Tao; Chen, Wanzhong

    2017-08-01

    Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

  5. On the use of EEG or MEG brain imaging tools in neuromarketing research.

    Science.gov (United States)

    Vecchiato, Giovanni; Astolfi, Laura; De Vico Fallani, Fabrizio; Toppi, Jlenia; Aloise, Fabio; Bez, Francesco; Wei, Daming; Kong, Wanzeng; Dai, Jounging; Cincotti, Febo; Mattia, Donatella; Babiloni, Fabio

    2011-01-01

    Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  6. On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research

    Directory of Open Access Journals (Sweden)

    Giovanni Vecchiato

    2011-01-01

    Full Text Available Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG and magnetoencephalogram (MEG methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

  7. REM Sleep EEG Instability in REM Sleep Behavior Disorder and Clonazepam Effects.

    Science.gov (United States)

    Ferri, Raffaele; Rundo, Francesco; Silvani, Alessandro; Zucconi, Marco; Bruni, Oliviero; Ferini-Strambi, Luigi; Plazzi, Giuseppe; Manconi, Mauro

    2017-08-01

    We aimed to analyze quantitatively rapid eye movement (REM) sleep electroencephalogram (EEG) in controls, drug-naïve idiopathic REM sleep behavior disorder patients (iRBD), and iRBD patients treated with clonazepam. Twenty-nine drug-naïve iRBD patients (mean age 68.2 years), 14 iRBD patients under chronic clonazepam therapy (mean age 66.3 years), and 21 controls (mean age 66.8 years) were recruited. Power spectra were obtained from sleep EEG (central derivation), using a 2-second sliding window, with 1-second steps. The power values of each REM sleep EEG spectral band (one every second) were normalized with respect to the average power value obtained during sleep stage 2 in the same individual. In drug-naïve patients, the normalized power values showed a less pronounced REM-related decrease of power in all bands with frequency sleep EEG structure changes found in this study disclose subtle but significant alterations in the cortical electrophysiology of RBD that might represent the early expression of the supposed neurodegenerative processes already taking place at this stage of the disease and might be the target of better and effective future therapeutic strategies for this condition. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

  8. Quantum neural network-based EEG filtering for a brain-computer interface.

    Science.gov (United States)

    Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

    2014-02-01

    A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

  9. Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

    Directory of Open Access Journals (Sweden)

    Keranmu Xielifuguli

    2014-01-01

    Full Text Available People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.

  10. Rapid prototyping of an EEG-based brain-computer interface (BCI).

    Science.gov (United States)

    Guger, C; Schlögl, A; Neuper, C; Walterspacher, D; Strein, T; Pfurtscheller, G

    2001-03-01

    The electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g., late stage of amyotrophic lateral sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEG-based brain-computer interface (BCI). This paper describes a new type of BCI system that uses rapid prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid prototyping is possible by using Matlab, Simulink, and the Real-Time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and offline analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board. The BCI can be controlled over the Internet, LAN or modem. This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70% and 95% could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).

  11. Development of Brain EEG Connectivity across Early Childhood: Does Sleep Play a Role?

    Directory of Open Access Journals (Sweden)

    Monique K. LeBourgeois

    2013-11-01

    Full Text Available Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills—e.g., language, cognitive control, working memory—that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8–2 Hz, theta (4.8–7.8 Hz and sleep spindles (10–14 Hz, with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation—i.e., programmed unfolding of neuronal networks—and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.

  12. Evaluating the effectiveness of using electroencephalogram power indices to measure visual fatigue.

    Science.gov (United States)

    Hsu, Bin-Wei; Wang, Mao-Jiun J

    2013-02-01

    Electroencephalography (EEG) is widely used in cognitive and behavioral research. This study evaluates the effectiveness of using the EEG power index to measure visual fatigue. Three common visual fatigue measures, critical-flicker fusion (CFF), near-point accommodation (NPA), and subjective eye-fatigue rating, were used for comparison. The study participants were 20 men with a mean age of 20.4 yr. (SD = 1.5). The experimental task was a car-racing video game. Results indicated that the EEG power indices were valid as a visual fatigue measure and the sensitivity of the objective measures (CFF and EEG power index) was higher than the subjective measure. The EEG beta and EEG alpha were effective for measuring visual fatigue in short- and long-duration tasks, respectively. EEG beta/alpha were the most effective power indexes for the visual fatigue measure.

  13. Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

    Science.gov (United States)

    Kim, Su Kyoung; Kirchner, Elsa Andrea; Stefes, Arne; Kirchner, Frank

    2017-12-14

    Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback during robot RL is advantageous, since an explicit reward function can be easily adapted. However, it is very demanding and tiresome for a human to continuously and explicitly generate feedback. Therefore, the development of implicit approaches is of high relevance. In this paper, we used an error-related potential (ErrP), an event-related activity in the human electroencephalogram (EEG), as an intrinsically generated implicit feedback (rewards) for RL. Initially we validated our approach with seven subjects in a simulated robot learning scenario. ErrPs were detected online in single trial with a balanced accuracy (bACC) of 91%, which was sufficient to learn to recognize gestures and the correct mapping between human gestures and robot actions in parallel. Finally, we validated our approach in a real robot scenario, in which seven subjects freely chose gestures and the real robot correctly learned the mapping between gestures and actions (ErrP detection (90% bACC)). In this paper, we demonstrated that intrinsically generated EEG-based human feedback in RL can successfully be used to implicitly improve gesture-based robot control during human-robot interaction. We call our approach intrinsic interactive RL.

  14. Review on solving the forward problem in EEG source analysis

    Directory of Open Access Journals (Sweden)

    Vergult Anneleen

    2007-11-01

    Full Text Available Abstract Background The aim of electroencephalogram (EEG source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter. In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM, the finite element method (FEM and the finite difference method (FDM. In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative

  15. EEG-Informed fMRI: A Review of Data Analysis Methods

    Science.gov (United States)

    Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

    2018-01-01

    The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

  16. EEG-Informed fMRI: A Review of Data Analysis Methods

    Directory of Open Access Journals (Sweden)

    Rodolfo Abreu

    2018-02-01

    Full Text Available The simultaneous acquisition of electroencephalography (EEG with functional magnetic resonance imaging (fMRI is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

  17. Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

    Science.gov (United States)

    Lamoš, Martin; Mareček, Radek; Slavíček, Tomáš; Mikl, Michal; Rektor, Ivan; Jan, Jiří

    2018-06-01

    Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral

  18. Identification of scalp EEG circadian variation using a novel correlation sum measure

    Science.gov (United States)

    Shahidi Zandi, Ali; Boudreau, Philippe; Boivin, Diane B.; Dumont, Guy A.

    2015-10-01

    Objective. In this paper, we propose a novel method to determine the circadian variation of scalp electroencephalogram (EEG) in both individual and group levels using a correlation sum measure, quantifying self-similarity of the EEG relative energy across waking epochs. Approach. We analysed EEG recordings from central-parietal and occipito-parietal montages in nine healthy subjects undergoing a 72 h ultradian sleep-wake cycle protocol. Each waking epoch (˜1 s) of every nap opportunity was decomposed using the wavelet packet transform, and the relative energy for that epoch was calculated in the desired frequency band using the corresponding wavelet coefficients. Then, the resulting set of energy values was resampled randomly to generate different subsets with equal number of elements. The correlation sum of each subset was then calculated over a range of distance thresholds, and the average over all subsets was computed. This average value was finally scaled for each nap opportunity and considered as a new circadian measure. Main results. According to the evaluation results, a clear circadian rhythm was identified in some EEG frequency ranges, particularly in 4-8 Hz and 10-12 Hz. The correlation sum measure not only was able to disclose the circadian rhythm on the group data but also revealed significant circadian variations in most individual cases, as opposed to previous studies only reporting the circadian rhythms on a population of subjects. Compared to a naive measure based on the EEG absolute energy in the frequency band of interest, the proposed measure showed a clear superiority using both individual and group data. Results also suggested that the acrophase (i.e., the peak) of the circadian rhythm in 10-12 Hz occurs close to the core body temperature minimum. Significance. These results confirm the potential usefulness of the proposed EEG-based measure as a non-invasive circadian marker.

  19. Responses to tonic heat pain in the ongoing EEG under conditions of controlled attention.

    Science.gov (United States)

    Giehl, Janet; Meyer-Brandis, Gesa; Kunz, Miriam; Lautenbacher, Stefan

    2014-03-01

    To confirm the existence of an ongoing electroencephalogram (EEG) pattern that is truly suggestive of pain, tonic heat pain was induced by small heat pulses at 1 °C above the pain threshold and compared to slightly less intense tonic non-painful heat pulses at 1 °C below the pain threshold. Twenty healthy subjects rated the sensation intensity during thermal stimulation. Possible confounding effects of attention were thoroughly controlled for by testing in four conditions: (1) focus of attention directed ipsilateral or (2) contralateral to the side of the stimulation, (3) control without a side preference, and (4) no control of attention at all. EEG was recorded via eight leads according to the 10/20 convention. Absolute power was computed for the frequency bands delta (0.5-4 Hz), theta (4-8 Hz), alpha1 (8-11 Hz), alpha2 (11-14 Hz), beta1 (14-25 Hz), and beta2 (25-35 Hz). Ratings were clearly distinct between the heat and pain conditions and suggestive for heat and pain sensations. Manipulation of attention proved to be successful by producing effects on the ratings and on the EEG activity (with lower ratings and lower EEG activity (theta, beta1, 2) over central areas for side-focused attention). During pain stimulation, lower central alpha1 and alpha2 activity and higher right-parietal and right-occipital delta power were observed compared to heat stimulation. This EEG pattern was not influenced by the manipulation of attention. Since the two types of stimuli (pain, heat) were subjectively felt differently although stimulation intensities were nearby, we conclude that this EEG pattern is clearly suggestive of pain.

  20. Development of grouped icEEG for the study of cognitive processing

    Directory of Open Access Journals (Sweden)

    Cihan Mehmet Kadipasaoglu

    2015-07-01

    Full Text Available Invasive intracranial EEG (icEEG offers a unique opportunity to study human cognitive networks at an unmatched spatiotemporal resolution. To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way. Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population. By the generation of whole-brain activity maps, grouped icEEG enables the study of intra and interregional dynamics between distributed cortical substrates exhibiting task-dependent activity. In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

  1. Effects of Viewing Displays from Different Distances on Human Visual System

    Directory of Open Access Journals (Sweden)

    Mohamed Z. Ramadan

    2017-11-01

    Full Text Available The current stereoscopic 3D displays have several human-factor issues including visual-fatigue symptoms such as eyestrain, headache, fatigue, nausea, and malaise. The viewing time and viewing distance are factors that considerably affect the visual fatigue associated with 3D displays. Hence, this study analyzes the effects of display type (2D vs. 3D and viewing distance on visual fatigue during a 60-min viewing session based on electroencephalogram (EEG relative beta power, and alpha/beta power ratio. In this study, twenty male participants watched four videos. The EEGs were recorded at two occipital lobes (O1 and O2 of each participant in the pre-session (3 min, post-session (3 min, and during a 60-min viewing session. The results showed that the decrease in relative beta power of the EEG and the increase in the alpha/beta ratio from the start until the end of the viewing session were significantly higher when watching the 3D display. When the viewing distance was increased from 1.95 m to 3.90 m, the visual fatigue was decreased in the case of the 3D-display, whereas the fatigue was increased in the case of the 2D-display. Moreover, there was approximately the same level of visual fatigue when watching videos in 2D or 3D from a long viewing distance (3.90 m.

  2. [EEG-markers of vertical postural organization in healthy persons].

    Science.gov (United States)

    Zhavoronkova, L A; Zharikova, A V; Kushnir, E M; Mikhalkova, A A

    2012-01-01

    In 10 healthy persons (22.8 +/- 0.67 years) spectral-coherence parameters of EEG were analyzed in different steps of verticalizations--from gorizontal position to seat and stand one. Maximal changes of all EEG parameters were observed in state with absence of visual control. We observed an increase of power for fast spectral bands of EEG (beta- and gamma-bands) in all conditions and additional increase of these EEG parameters was observed at situation of complication of conditions of vertical pose supporting. Results of EEG coherent analysis in conditions of human verticalization showed specific increase of coherence for the majority of rhythm ranges in the right hemisphere especially in the central-frontal and in occipital-parietal areas and for interhemispheric pairs for these leads. This fact can reflect participation of cortical as well as subcortical structures in these processes. In conditions of complicate conditions of vertical pose supporting the additional increase of EEG coherence in fast bands (beta-rhythm) was observed at the frontal areas. This fact can testify about increasing of executive functions in this conditions.

  3. Information for People Treated with Human Growth Hormone (Comprehensive Report)

    Science.gov (United States)

    ... diagnosis. Two commonly used tests are the electroencephalogram (EEG) and magnetic resonance imaging (MRI). While these brain ... htm . Can I donate blood or organs? Five cases of transmission of the ... by blood, but more study of this risk is needed. In addition, no ...

  4. Amplitude Integrated Electroencephalogram as a Prognostic Tool in Neonates with Hypoxic-Ischemic Encephalopathy: A Systematic Review.

    Directory of Open Access Journals (Sweden)

    Ruth Del Río

    Full Text Available Perinatal management and prognostic value of clinical evaluation and diagnostic tools have changed with the generalization of therapeutic hypothermia (TH in infants with hypoxic-ischemic encephalopathy (HIE.to ascertain the prognostic value of amplitude integrated electroencephalogram (aEEG in neonates with HIE considering hours of life and treatment with TH.A systematic review was performed. Inclusion criteria were studies including data of neonates with HIE, treated or not with TH, monitored with aEEG and with neurodevelopmental follow-up of at least 12 months. The period of bibliographic search was until February 2016. No language restrictions were initially applied. Consulted databases were MEDLINE, Scopus, CINHAL and the Spanish language databases GuiaSalud and Bravo. Article selection was performed by two independent reviewers. Quality for each individual paper selected was evaluated using QUADAS-2. Review Manager (RevMan version 5.3 software was used. Forest plots were constructed to graphically show sensitivity and specificity for all included studies, separating patients treated or not with hypothermia. Summary statistics were estimated using bivariate models and random effects approaches with the R package MADA from summary ROC curves. Meta-regression was used to estimate heterogeneity and trends.from the 403 articles initially identified, 17 were finally included and critically reviewed. In infants not treated with hypothermia the maximum reliability of an abnormal aEEG background to predict death or moderate/severe disability was at 36 hours of life, when a positive post-test probability of 97.90% was achieved (95%CI 88.40 to 99.40%. Positive likelihood ratio (+LR at these hours of life was 26.60 (95%CI 4.40 to 94.90 and negative likelihood ratio (-LR was 0.23 (95%CI 0.10 to 0.44. A high predictive value was already present at 6 hours of life in this group of patients, with a positive post-test probability of 88.20% (95%CI 79.80 to

  5. Use of electroencephalography (EEG) to assess CNS changes produced by pesticides with different modes of action: Effects of permethrin, deltamethrin, fipronil, imidacloprid, carbaryl, and triadimefon

    International Nuclear Information System (INIS)

    Freeborn, Danielle L.; McDaniel, Katherine L.; Moser, Virginia C.; Herr, David W.

    2015-01-01

    The electroencephalogram (EEG) is an apical measure, capable of detecting changes in brain neuronal activity produced by internal or external stimuli. We assessed whether pesticides with different modes of action produced different changes in the EEG of adult male Long–Evans rats. The EEG was recorded using two montages (visual cortex referenced to the cerebellum and to the frontal cortex) in unrestrained rats at the time of peak behavioral effects. Pesticides included: permethrin and deltamethrin (Type I and Type II pyrethroids; 2 h), fipronil (single and repeated doses; phenylpyrazole; 6 h), imidacloprid (neonicotinoid; 2 h), carbaryl (carbamate; 0.5 h), and triadimefon (triazole; 1 h), using dosages that produced approximately an ED 30 or an ED 50 –ED 80 change in motor activity. Permethrin (43, 100 mg/kg) increased amplitudes or areas (delta, alpha, or gamma bands) in the EEG. Deltamethrin (2.5, 5.5 mg/kg) reduced the amplitudes or areas of the delta, theta, alpha, beta, and gamma bands, but the changes were not dose-related. A single treatment with fipronil (25, 50 mg/kg, but not 5, 10 mg/kg) decreased gamma band area. Additional changes in the delta, theta, and gamma bands were observed when fipronil (5, 10 mg/kg) was administered for 14 days. Imidacloprid (50, 100 mg/kg) did not alter the EEG. Carbaryl (10, 50 mg/kg) decreased theta area, and decreased delta and increased beta frequency. Triadimefon (75, 150 mg/kg) produced minimal changes in the EEG. The results show that the EEG is affected differently by approximately equipotent doses of pesticides with different modes of action. - Highlights: • Pesticides with different modes of action have different effects on in vivo rodent EEG. • The EEG was also changed differently after single vs. repeated treatment with fipronil. • The data suggest that EEG may be used as an apical measure for detecting chemical effects on the central nervous system

  6. EEG in connection with coma.

    Science.gov (United States)

    Wilson, John A; Nordal, Helge J

    2013-01-08

    Coma is a dynamic condition that may have various causes. Important changes may take place rapidly, often with consequences for treatment. The purpose of this article is to provide a brief overview of EEG patterns in comas with various causes, and indicate how EEG contributes in an assessment of the prognosis for coma patients. The article is based on many years of clinical and research-based experience of EEG used for patients in coma. A self-built reference database was supplemented by searches for relevant articles in PubMed. EEG reveals immediate changes in coma, and can provide early information on cause and prognosis. It is the only diagnostic tool for detecting a non-convulsive epileptic status. Locked-in- syndrome may be overseen without EEG. Repeated EEG scans increase diagnostic certainty and make it possible to monitor the development of coma. EEG reflects brain function continuously and therefore holds a key place in the assessment and treatment of coma.

  7. Epileptic seizure detection from EEG signals with phase-amplitude cross-frequency coupling and support vector machine

    Science.gov (United States)

    Liu, Yang; Wang, Jiang; Cai, Lihui; Chen, Yingyuan; Qin, Yingmei

    2018-03-01

    As a pattern of cross-frequency coupling (CFC), phase-amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB-MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

  8. EEG spectral phenotypes: heritability and association with marijuana and alcohol dependence in an American Indian community study.

    Science.gov (United States)

    Ehlers, Cindy L; Phillips, Evelyn; Gizer, Ian R; Gilder, David A; Wilhelmsen, Kirk C

    2010-01-15

    Native Americans have some of the highest rates of marijuana and alcohol use and abuse, yet neurobiological measures associated with dependence on these substances in this population remain unknown. The present investigation evaluated the heritability of spectral characteristics of the electroencephalogram (EEG) and their correlation with marijuana and alcohol dependence in an American Indian community. Participants (n=626) were evaluated for marijuana (MJ) and alcohol (ALC) dependence, as well as other psychiatric disorders. EEGs were collected from six cortical sites and spectral power determined in five frequency bands (delta 1.0-4.0 Hz, theta 4.0-7.5 Hz, alpha 7.5-12.0 Hz, low beta 12.0-20.0 Hz and high beta/gamma 20-50 Hz). The estimated heritability (h(2)) of the EEG phenotypes was calculated using SOLAR, and ranged from 0.16 to 0.67. Stepwise linear regression was used to detect correlations between MJ and ALC dependence and the spectral characteristics of the EEG using a model that took into account: age, gender, Native American Heritage (NAH) and a lifetime diagnosis of antisocial personality and/or conduct disorder (ASPD/CD). Increases in spectral power in the delta frequency range, were significantly correlated with gender (pEEG delta and high beta/gamma activity are correlated with MJ dependence and alcohol dependence, respectively, in this community sample of Native Americans. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

  9. A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

    Science.gov (United States)

    Tsiouris, Κostas Μ; Pezoulas, Vasileios C; Zervakis, Michalis; Konitsiotis, Spiros; Koutsouris, Dimitrios D; Fotiadis, Dimitrios I

    2018-05-17

    The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an imminent seizure. In this work, Long Short-Term Memory (LSTM) networks are introduced in epileptic seizure prediction using EEG signals, expanding the use of deep learning algorithms with convolutional neural networks (CNN). A pre-analysis is initially performed to find the optimal architecture of the LSTM network by testing several modules and layers of memory units. Based on these results, a two-layer LSTM network is selected to evaluate seizure prediction performance using four different lengths of preictal windows, ranging from 15 min to 2 h. The LSTM model exploits a wide range of features extracted prior to classification, including time and frequency domain features, between EEG channels cross-correlation and graph theoretic features. The evaluation is performed using long-term EEG recordings from the open CHB-MIT Scalp EEG database, suggest that the proposed methodology is able to predict all 185 seizures, providing high rates of seizure prediction sensitivity and low false prediction rates (FPR) of 0.11-0.02 false alarms per hour, depending on the duration of the preictal window. The proposed LSTM-based methodology delivers a significant increase in seizure prediction performance compared to both traditional machine learning techniques and convolutional neural networks that have been previously evaluated in the literature. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

    Science.gov (United States)

    2013-01-01

    Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109

  11. [Electroencephalographic characteristic of cognitive-specific alerting attention in verbal learning--III: Localized characteristics of EEG spatial synchronization].

    Science.gov (United States)

    Dan'ko, S G; Kachalova, L M; Solov'eva, M L

    2010-01-01

    Electroencephalograms (EEG) were recorder in 19 standard derivations in 88 healthy subjects, while they were in the states: rest with eyes open; memorization (learning) of verbal bilingual semantic pairs (Latin and Russian languages); the retrieval of the rote information from memory (control). We compared estimates of EEG coherence in these states for the frequency bands theta (4-7 Hz), alpha-1 (7-10 Hz), alpha-2 (10-13 Hz), beta-1 (13-18 Hz), beta-2 (18-30 Hz), gamma (30-40 Hz). When compared with the rest most strongly expressed: for memorization a decrease of coherence in the pairs of derivations from frontal and central areas of the cortex in the EEG frequency bands; for retrieval an increase of coherence in interhemispheric derivation pairs of pariental-occipital region in majority of the frequency bands. For the retrieval also increases of coherence in the beta2 and gamma bands, along with coherence decreases at low frequencies take place in pairs formed by derivations from the parieto-occipital region with derivations from the frontal and the central ones. Dynamics of EEG coherence in comparisons of memorization and retrieval from the rest and each are expressed significantly more in the interhemispheric and crosshemispheric pairs of derivations than in the intrahemispheric pairs. Revealed topographic specificity of the dynamics of EEG coherence by changing the states is considered in terms of ideas about cognitive-specific forms of sustained goal-directed mental attention.

  12. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    Directory of Open Access Journals (Sweden)

    Boon-Giin Lee

    2014-09-01

    Full Text Available Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals.

  13. On some problems encountered in calculating the correlation dimension of EEG

    International Nuclear Information System (INIS)

    Dvorak, I.; Siska, J.

    1986-06-01

    Results of calculations of correlation dimension of the human EEG are presented. Effects of proband's mental activity, of the length of scrutinized signal and of the locus of registration on the computed values are studied. Evidence is given for a deterministic component in the EEG signal. (author)

  14. Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation

    Science.gov (United States)

    Saproo, Sameer; Shih, Victor; Jangraw, David C.; Sajda, Paul

    2016-12-01

    Objective. We investigated the neural correlates of workload buildup in a fine visuomotor task called the boundary avoidance task (BAT). The BAT has been known to induce naturally occurring failures of human-machine coupling in high performance aircraft that can potentially lead to a crash—these failures are termed pilot induced oscillations (PIOs). Approach. We recorded EEG and pupillometry data from human subjects engaged in a flight BAT simulated within a virtual 3D environment. Main results. We find that workload buildup in a BAT can be successfully decoded from oscillatory features in the electroencephalogram (EEG). Information in delta, theta, alpha, beta, and gamma spectral bands of the EEG all contribute to successful decoding, however gamma band activity with a lateralized somatosensory topography has the highest contribution, while theta band activity with a fronto-central topography has the most robust contribution in terms of real-world usability. We show that the output of the spectral decoder can be used to predict PIO susceptibility. We also find that workload buildup in the task induces pupil dilation, the magnitude of which is significantly correlated with the magnitude of the decoded EEG signals. These results suggest that PIOs may result from the dysregulation of cortical networks such as the locus coeruleus (LC)—anterior cingulate cortex (ACC) circuit. Significance. Our findings may generalize to similar control failures in other cases of tight man-machine coupling where gains and latencies in the control system must be inferred and compensated for by the human operators. A closed-loop intervention using neurophysiological decoding of workload buildup that targets the LC-ACC circuit may positively impact operator performance in such situations.

  15. [The contribution of the clinical examination, electroencephalogram, and brain MRI in assessing the prognosis in term newborns with neonatal encephalopathy. A cohort of 30 newborns before the introduction of treatment with hypothermia].

    Science.gov (United States)

    Jadas, V; Brasseur-Daudruy, M; Chollat, C; Pellerin, L; Devaux, A M; Marret, S

    2014-02-01

    Perinatal asphyxia complicated by hypoxic ischemic brain injury remains a source of neurological lesions. A major aim of neonatologists is to evaluate the severity of neonatal encephalopathy (NE) and to evaluate prognosis. The purpose of this study was to determine the contribution of brain MRI compared to electroencephalogram (EEG) and clinical data in assessing patients' prognosis. Thirty newborns from the pediatric resuscitation unit at Rouen university hospital were enrolled in a retrospective study between January 2006 and December 2008, prior to introduction of hypothermia treatment. All 30 newborns had at least two anamnestic criteria of perinatal asphyxia, one brain MRI in the first 5 days of life and another after 7 days of life as well as an early EEG in the first 2 days of life. Then, the infants were seen in consultation to assess neurodevelopment. This study showed a relation between NE stage and prognosis. During stage 1, prognosis was good, whereas stage 3 was associated with poor neurodevelopment outcome. Normal clinical examination before the 8th day of life was a good prognostic factor in this study. There was a relationship between severity of EEG after the 5th day of life and poor outcome. During stage 2, EEG patterns varied in severity, and brain MRI provided a better prognosis. Lesions of the basal ganglia and a decreased or absent signal of the posterior limb of the internal capsule were poor prognostic factors during brain MRI. These lesions were underestimated during standard MRI in the first days of life but were visible with diffusion sequences. Cognitive impairment affected 40% of surviving children, justifying extended pediatric follow-up. This study confirms the usefulness of brain MRI as a diagnostic tool in hypoxic ischemic encephalopathy in association with clinical data and EEG tracings. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  16. Nocturnal electroencephalogram registrations in type 1 (insulin-dependent) diabetic patients with hypoglycaemia

    DEFF Research Database (Denmark)

    Bendtson, I; Gade, J; Rosenfalck, A M

    1991-01-01

    Eight Type 1 (insulin-dependent) diabetic patients with no diabetic complications were studied overnight for two consecutive and one subsequent night with continuous monitoring of electroencephalogram and serial hormone measurements. The aims were: 1) to evaluate the influence of spontaneous...... and insulin-induced hypoglycaemia on nocturnal electroencephalogram sleep-patterns and, 2) to evaluate counter-regulatory hormone responses. Spontaneous hypoglycaemia occurred on six nights (38%) with blood glucose concentrations less than 3.0 mmol/l and on four nights less than 2.0 mmol/l. All the patients...... experienced insulin-induced hypoglycaemia with a blood glucose nadir of 1.6 (range 1.4-1.9) mmol/l. The electroencephalogram was analysed by a new method developed for this purpose in contrast to the traditional definition of delta-, theta-, alpha- and beta-activity. The blood glucose concentration could...

  17. Electroencephalogy (EEG) Feedback in Decision-Making

    Science.gov (United States)

    2015-08-26

    Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful

  18. Index of Theta/Alpha Ratio of the Quantitative Electroencephalogram in Alzheimer's Disease: A Case-Control Study.

    Science.gov (United States)

    Fahimi, Golshan; Tabatabaei, Seyed Mahmoud; Fahimi, Elnaz; Rajebi, Hamid

    2017-08-01

    Alzheimer's disease (AD) is a devastating neurodegenerative disorder in human beings associated with cognitive, behavioral and motor impairments. The main symptom of AD is dementia, which causes difficulties in carrying out daily practices. Brain waves are altered in people with AD. Relative indices of brain waves can be beneficial in the diagnosis of AD. In this case-control study, 50 patients with AD and 50 matched healthy individuals were enrolled in case and control groups respectively. With recording and analyzing of brain waves with the utilization of quantitative electroencephalogram (QEEG), index of theta/alpha ratio was assessed in both groups. The index of theta/alpha ratio was significantly higher in patients with AD in comparison to healthy individuals (Pratio obtained by QEEG provides a non-invasive diagnostic marker of AD, which may be helpful in identification of non-advanced disease in susceptible individuals.

  19. A technique to consider mismatches between fMRI and EEG/MEG sources for fMRI-constrained EEG/MEG source imaging: a preliminary simulation study

    International Nuclear Information System (INIS)

    Im, Chang-Hwan; Lee, Soo Yeol

    2006-01-01

    fMRI-constrained EEG/MEG source imaging can be a powerful tool in studying human brain functions with enhanced spatial and temporal resolutions. Recent studies on the combination of fMRI and EEG/MEG have suggested that fMRI prior information could be readily implemented by simply imposing different weighting factors to cortical sources overlapping with the fMRI activations. It has been also reported, however, that such a hard constraint may cause severe distortions or elimination of meaningful EEG/MEG sources when there are distinct mismatches between the fMRI activations and the EEG/MEG sources. If one wants to obtain the actual EEG/MEG source locations and uses the fMRI prior information as just an auxiliary tool to enhance focality of the distributed EEG/MEG sources, it is reasonable to weaken the strength of fMRI constraint when severe mismatches between fMRI and EEG/MEG sources are observed. The present study suggests an efficient technique to automatically adjust the strength of fMRI constraint according to the mismatch level. The use of the proposed technique rarely affects the results of conventional fMRI-constrained EEG/MEG source imaging if no major mismatch between the two modalities is detected; while the new results become similar to those of typical EEG/MEG source imaging without fMRI constraint if the mismatch level is significant. A preliminary simulation study using realistic EEG signals demonstrated that the proposed technique can be a promising tool to selectively apply fMRI prior information to EEG/MEG source imaging

  20. Factor Analysis for Finding Invariant Neural Descriptors of Human Emotions

    Directory of Open Access Journals (Sweden)

    Vitor Pereira

    2018-01-01

    Full Text Available A major challenge in decoding human emotions from electroencephalogram (EEG data is finding representations that are invariant to inter- and intrasubject differences. Most of the previous studies are focused in building an individual discrimination model for every subject (subject dependent model. Building subject-independent models is a harder problem due to the high data variability between different subjects and different experiments with the same subject. This paper explores, for the first time, the Factor Analysis as an efficient technique to extract temporal and spatial EEG features suitable to build brain-computer interface for decoding human emotions across various subjects. Our findings show that early waves (temporal window of 200–400 ms after the stimulus onset carry more information about the valence of the emotion. Also, spatial location of features, with a stronger impact on the emotional valence, occurs in the parietal and occipital regions of the brain. All discrimination models (NN, SVM, kNN, and RF demonstrate better discrimination rate of the positive valence. These results match closely experimental psychology hypothesis that, during early periods after the stimulus presentation, the brain response—to images with highly positive valence—is stronger.

  1. Gender effect in human brain responses to bottom-up and top-down attention using the EEG 3D-Vector Field Tomography.

    Science.gov (United States)

    Kosmidou, Vasiliki E; Adam, Aikaterini; Papadaniil, Chrysa D; Tsolaki, Magda; Hadjileontiadis, Leontios J; Kompatsiaris, Ioannis

    2015-01-01

    The effect of gender in rapidly allocating attention to objects, features or locations, as reflected in brain activity, is examined in this study. A visual-attention task, consisting of bottom-up (visual pop-out) and top-down (visual search) conditions during stimuli of four triangles, i.e., a target and three distractors, was engaged. In pop-out condition, both color and orientation of the distractors differed from target, while in search condition they differed only in orientation. During the task, high-density EEG (256 channels) data were recorded and analyzed by means of behavioral, event-related potentials, i.e., the P300 component and brain source localization analysis using 3D-Vector Field Tomography (3D-VFT). Twenty subjects (half female; 32±4.7 years old) participated in the experiments, performing 60 trials for each condition. Behavioral analysis revealed that both female and male outperformed in the pop-out condition compared to the search one, with respect to accuracy and reaction time, whereas no gender-related statistical significant differences were found. Nevertheless, in the search condition, higher P300 amplitudes were detected for females compared to males (p left inferior frontal and superior temporal gyri, whereas in males it was found in the right inferior frontal and superior temporal gyri. Overall, the experimental results show that visual attention depends on contributions from different brain lateralization linked to gender, posing important implications in studying developmental disorders, characterized by gender differences.

  2. Interhemispheric EEG differences in olfactory bulbectomized rats with different cognitive abilities and brain beta-amyloid levels.

    Science.gov (United States)

    Bobkova, Natalia; Vorobyov, Vasily; Medvinskaya, Natalia; Aleksandrova, Irina; Nesterova, Inna

    2008-09-26

    Alterations in electroencephalogram (EEG) asymmetry and deficits in interhemispheric integration of information have been shown in patients with Alzheimer's disease (AD). However, no direct evidence of an association between EEG asymmetry, morphological markers in the brain, and cognition was found either in AD patients or in AD models. In this study we used rats with bilateral olfactory bulbectomy (OBX) as one of the AD models and measured their learning/memory abilities, brain beta-amyloid levels and EEG spectra in symmetrical frontal and occipital cortices. One year after OBX or sham-surgery, the rats were tested with the Morris water paradigm and assigned to three groups: sham-operated rats, SO, and OBX rats with virtually normal, OBX(+), or abnormal, OBX(-), learning (memory) abilities. In OBX vs. SO, the theta EEG activity was enhanced to a higher extent in the right frontal cortex and in the left occipital cortex. This produced significant interhemispheric differences in the frontal cortex of the OBX(-) rats and in the occipital cortex of both OBX groups. The beta1 EEG asymmetry in SO was attenuated in OBX(+) and completely eliminated in OBX(-). OBX produced highly significant beta2 EEG decline in the right frontal cortex, with OBX(-)>OBX(+) rank order of strength. The beta-amyloid level, examined by post-mortem immunological DOT-analysis in the cortex-hippocampus samples, was about six-fold higher in OBX(-) than in SO, but significantly less (enhanced by 82% vs. SO) in OBX(+) than in OBX(-). The involvement of the brain mediatory systems in the observed EEG asymmetry differences is discussed.

  3. Brain source localization: A new method based on MUltiple SIgnal Classification algorithm and spatial sparsity of the field signal for electroencephalogram measurements

    Science.gov (United States)

    Vergallo, P.; Lay-Ekuakille, A.

    2013-08-01

    Brain activity can be recorded by means of EEG (Electroencephalogram) electrodes placed on the scalp of the patient. The EEG reflects the activity of groups of neurons located in the head, and the fundamental problem in neurophysiology is the identification of the sources responsible of brain activity, especially if a seizure occurs and in this case it is important to identify it. The studies conducted in order to formalize the relationship between the electromagnetic activity in the head and the recording of the generated external field allow to know pattern of brain activity. The inverse problem, that is given the sampling field at different electrodes the underlying asset must be determined, is more difficult because the problem may not have a unique solution, or the search for the solution is made difficult by a low spatial resolution which may not allow to distinguish between activities involving sources close to each other. Thus, sources of interest may be obscured or not detected and known method in source localization problem as MUSIC (MUltiple SIgnal Classification) could fail. Many advanced source localization techniques achieve a best resolution by exploiting sparsity: if the number of sources is small as a result, the neural power vs. location is sparse. In this work a solution based on the spatial sparsity of the field signal is presented and analyzed to improve MUSIC method. For this purpose, it is necessary to set a priori information of the sparsity in the signal. The problem is formulated and solved using a regularization method as Tikhonov, which calculates a solution that is the better compromise between two cost functions to minimize, one related to the fitting of the data, and another concerning the maintenance of the sparsity of the signal. At the first, the method is tested on simulated EEG signals obtained by the solution of the forward problem. Relatively to the model considered for the head and brain sources, the result obtained allows to

  4. [Exploration on Electroencephalogram Mechanism Differences of Negative Emotions Induced by Disgusted and Sad Situation Images].

    Science.gov (United States)

    Wang, Xin; Jin, Jingna; Li, Song; Liu, Zhipeng; Yin, Tao

    2015-12-01

    Evolutionary psychology holds such an opinion that negative situation may threaten survival, trigger avoidance motive and have poor effects on the human-body function and the psychological quality. Both disgusted and sad situations can induce negative emotions. However, differences between the two situations on attention capture and emotion cognition during the emotion induction are still not well known. Typical disgusted and sad situation images were used in the present study to induce two negative emotions, and 15 young students (7 males and 8 females, aged 27 ± 3) were recruited in the experiments. Electroencephalogram of 32 leads was recorded when the subjects were viewing situation images, and event-related potentials (ERP) of all leads were obtained for future analysis. Paired sample t tests were carried out on two ERP signals separately induced by disgusted and sad situation images to get time quantum with significant statistical differences between the two ERP signals. Root-mean-square deviations of two ERP signals during each time quantum were calculated and the brain topographic map based on root-mean-square deviations was drawn to display differences of two ERP signals in spatial. Results showed that differences of ERP signals induced by disgusted and sad situation images were mainly manifested in T1 (120-450 ms) early and T2 (800-1,000 ms) later. During the period of T1, the occipital lobe reflecting attention capture was activated by both disgusted and sad situation images, but the prefrontal cortex reflecting emotion sense was activated only by disgusted situation images. During the period of T2, the prefrontal cortex was activated by both disgusted and sad situation images. However, the parietal lobe was activated only by disgusted situation images, which showed stronger emotional perception. The research results would have enlightenment to deepen understanding of negative emotion and to exploredeep cognitive neuroscience mechanisms of negative

  5. Physiological artifacts in scalp EEG and ear-EEG.

    Science.gov (United States)

    Kappel, Simon L; Looney, David; Mandic, Danilo P; Kidmose, Preben

    2017-08-11

    A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

  6. Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

    Science.gov (United States)

    Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

    2014-03-01

    Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

  7. Odds Ratio Product of Sleep EEG as a Continuous Measure of Sleep State

    Science.gov (United States)

    Younes, Magdy; Ostrowski, Michele; Soiferman, Marc; Younes, Henry; Younes, Mark; Raneri, Jill; Hanly, Patrick

    2015-01-01

    Study Objectives: To develop and validate an algorithm that provides a continuous estimate of sleep depth from the electroencephalogram (EEG). Design: Retrospective analysis of polysomnograms. Setting: Research laboratory. Participants: 114 patients who underwent clinical polysomnography in sleep centers at the University of Manitoba (n = 58) and the University of Calgary (n = 56). Interventions: None. Measurements and Results: Power spectrum of EEG was determined in 3-second epochs and divided into delta, theta, alpha-sigma, and beta frequency bands. The range of powers in each band was divided into 10 aliquots. EEG patterns were assigned a 4-digit number that reflects the relative power in the 4 frequency ranges (10,000 possible patterns). Probability of each pattern occurring in 30-s epochs staged awake was determined, resulting in a continuous probability value from 0% to 100%. This was divided by 40 (% of epochs staged awake) producing the odds ratio product (ORP), with a range of 0–2.5. In validation testing, average ORP decreased progressively as EEG progressed from wakefulness (2.19 ± 0.29) to stage N3 (0.13 ± 0.05). ORP sleep and ORP > 2.0 predicted wakefulness in > 95% of 30-s epochs. Epochs with intermediate ORP occurred in unstable sleep with a high arousal index (> 70/h) and were subject to much interrater scoring variability. There was an excellent correlation (r2 = 0.98) between ORP in current 30-s epochs and the likelihood of arousal or awakening occurring in the next 30-s epoch. Conclusions: Our results support the use of the odds ratio product (ORP) as a continuous measure of sleep depth. Citation: Younes M, Ostrowski M, Soiferman M, Younes H, Younes M, Raneri J, Hanly P. Odds ratio product of sleep EEG as a continuous measure of sleep state. SLEEP 2015;38(4):641–654. PMID:25348125

  8. Nucleated red blood cells and early EEG: predicting Sarnat stage and two year outcome.

    LENUS (Irish Health Repository)

    Walsh, B H

    2012-01-31

    AIMS: Hypoxic Ischaemic Encephalopathy (HIE) causes characteristic changes of the electroencephalogram (EEG), and a raised Nucleated Red Blood Cell (NRBC) count compared to controls. We wished to examine whether combining these markers could improve their ability to predict HIE severity in the first 24h. METHODS: Term infants with HIE were recruited. NRBC count and continuous multi-channel EEG were recorded within the first 24h. Neurological assessment was carried out at 24 months. A control population with NRBC counts in the first 24h was recruited. RESULTS: 44 infants with HIE and 43 control infants were recruited. Of the HIE population 39 completed a 2 year follow-up. The median NRBC count differed significantly between the controls and those with HIE (3\\/100 WBC [range of 0-11] vs 12.3\\/100 WBC [0-240]) (p<0.001). Within the HIE population the median NRBC count was significantly greater in infants with moderate\\/severe HIE than mild (16\\/100 WBC [range of 0-240] vs 8\\/100 WBC [1-23]) (p=0.016), and among infants with abnormal outcome compared to normal (21.3\\/100 WBC [1-239.8] vs 8.3\\/100 WBC [0-50])(p=0.03). The predictive ability of EEG changed with time post-delivery, therefore results are given at both 12 and 24h of age. At both time points the combined marker had a stronger correlation than EEG alone; with HIE severity (12h: r=0.661 vs r=0.622), (24h: r=0.645 vs r=0.598), and with outcome at 2 years (12h: r=0.756 vs r=0.652), (24h: r=0.802 vs r=0.746). CONCLUSION: Combining early EEG and NRBC count to predict HIE severity and neurological outcome, improved the predictive ability of either in isolation.

  9. Impact of metal artefacts due to EEG electrodes in brain PET/CT imaging

    International Nuclear Information System (INIS)

    Lemmens, Catherine; Nuyts, Johan; Dupont, Patrick; Montandon, Marie-Louise; Ratib, Osman; Zaidi, Habib

    2008-01-01

    The goal of this study is to investigate the impact of electroencephalogram (EEG) electrodes on the visual quality and quantification of 18 F-FDG PET images in neurological PET/CT examinations. For this purpose, the scans of 20 epilepsy patients with EEG monitoring were used. The CT data were reconstructed with filtered backprojection (FBP) and with a metal artefact reduction (MAR) algorithm. Both data sets were used for CT-based attenuation correction (AC) of the PET data. Also, a calculated AC (CALC) technique was considered. A volume of interest (VOI)-based analysis and a voxel-based quantitative analysis were performed to compare the different AC methods. Images were also evaluated visually by two observers. It was shown with simulations and phantom measurements that from the considered AC methods, the MAR-AC can be used as the reference in this setting. The visual assessment of PET images showed local hot spots outside the brain corresponding to the locations of the electrodes when using FBP-AC. In the brain, no abnormalities were observed. The quantitative analysis showed a very good correlation between PET-FBP-AC and PET-MAR-AC, with a statistically significant positive bias in the PET-FBP-AC images of about 5-7% in most brain voxels. There was also good correlation between PET-CALC-AC and PET-MAR-AC, but in the PET-CALC-AC images, regions with both a significant positive and negative bias were observed. EEG electrodes give rise to local hot spots outside the brain and a positive quantification bias in the brain. However, when diagnosis is made by mere visual assessment, the presence of EEG electrodes does not seem to alter the diagnosis. When quantification is performed, the bias becomes an issue especially when comparing brain images with and without EEG monitoring

  10. Impact of metal artefacts due to EEG electrodes in brain PET/CT imaging

    Energy Technology Data Exchange (ETDEWEB)

    Lemmens, Catherine; Nuyts, Johan; Dupont, Patrick [Department of Nuclear Medicine and Medical Imaging Center, University Hospital Gasthuisberg and Katholieke Universiteit Leuven, Leuven (Belgium); Montandon, Marie-Louise; Ratib, Osman; Zaidi, Habib [Division of Nuclear Medicine, Geneva University Hospital, CH-1211 Geneva (Switzerland)], E-mail: catherine.lemmens@uz.kuleuven.be

    2008-08-21

    The goal of this study is to investigate the impact of electroencephalogram (EEG) electrodes on the visual quality and quantification of {sup 18}F-FDG PET images in neurological PET/CT examinations. For this purpose, the scans of 20 epilepsy patients with EEG monitoring were used. The CT data were reconstructed with filtered backprojection (FBP) and with a metal artefact reduction (MAR) algorithm. Both data sets were used for CT-based attenuation correction (AC) of the PET data. Also, a calculated AC (CALC) technique was considered. A volume of interest (VOI)-based analysis and a voxel-based quantitative analysis were performed to compare the different AC methods. Images were also evaluated visually by two observers. It was shown with simulations and phantom measurements that from the considered AC methods, the MAR-AC can be used as the reference in this setting. The visual assessment of PET images showed local hot spots outside the brain corresponding to the locations of the electrodes when using FBP-AC. In the brain, no abnormalities were observed. The quantitative analysis showed a very good correlation between PET-FBP-AC and PET-MAR-AC, with a statistically significant positive bias in the PET-FBP-AC images of about 5-7% in most brain voxels. There was also good correlation between PET-CALC-AC and PET-MAR-AC, but in the PET-CALC-AC images, regions with both a significant positive and negative bias were observed. EEG electrodes give rise to local hot spots outside the brain and a positive quantification bias in the brain. However, when diagnosis is made by mere visual assessment, the presence of EEG electrodes does not seem to alter the diagnosis. When quantification is performed, the bias becomes an issue especially when comparing brain images with and without EEG monitoring.

  11. Effects of nootropics on the EEG in conscious rats and their modification by glutamatergic inhibitors.

    Science.gov (United States)

    Vorobyov, Vasily; Kaptsov, Vladimir; Kovalev, Georgy; Sengpiel, Frank

    2011-05-30

    To study the effects of acute and repeated injections of nootropics and to learn how glutamate receptors might be involved in their mediation, the frequency spectra of cortical and hippocampal electroencephalogram (EEG) were analyzed in non-narcotized rats subcutaneously injected repeatedly with Piracetam (400mg/kg) or its analogue, Noopept (0.2mg/kg), after intracerebroventricular infusions of saline (5 μl) or the antagonists of NMDA and quisqualate/AMPA receptors: CPP (0.1 nmol) and GDEE (1 μmol), respectively. Piracetam increased alpha/beta1 EEG activity in the left frontal cortex, and alpha activity in both the right cortex and hippocampus, with a 10-min latency and 40-min duration. Noopept increased alpha/beta1 activity, with 30-min latency and 40-min duration in all brain areas. CPP pretreatment eliminated Piracetam EEG effects; reduced Noopept effects in the cortex and completely suppressed them in the hippocampus. After four injections of Piracetam, EEG effects were very small in the cortex, and completely lacking in the hippocampus, while GDEE pretreatment partially recovered them. The effect of Noopept in the alpha/beta1 ranges was replaced by increased beta2 activity after the eighth injection, while no effects were observed after the ninth one. GDEE pretreatment restored the effect of Noopept in the beta2 frequency range. These results demonstrate similarities in EEG effects and their mediatory mechanisms for Piracetam and its much more effective analogue, Noopept. Activation of NMDA receptors is involved in the effects of a single injection of the nootropics, whereas activation of quisqualate/AMPA receptors is associated with the decrease in their efficacy after repeated use. Copyright © 2011 Elsevier Inc. All rights reserved.

  12. Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification.

    Science.gov (United States)

    Lu, Na; Li, Tengfei; Pan, Jinjin; Ren, Xiaodong; Feng, Zuren; Miao, Hongyu

    2015-05-01

    Electroencephalogram (EEG) provides a non-invasive approach to measure the electrical activities of brain neurons and has long been employed for the development of brain-computer interface (BCI). For this purpose, various patterns/features of EEG data need to be extracted and associated with specific events like cue-paced motor imagery. However, this is a challenging task since EEG data are usually non-stationary time series with a low signal-to-noise ratio. In this study, we propose a novel method, called structure constrained semi-nonnegative matrix factorization (SCS-NMF), to extract the key patterns of EEG data in time domain by imposing the mean envelopes of event-related potentials (ERPs) as constraints on the semi-NMF procedure. The proposed method is applicable to general EEG time series, and the extracted temporal features by SCS-NMF can also be combined with other features in frequency domain to improve the performance of motor imagery classification. Real data experiments have been performed using the SCS-NMF approach for motor imagery classification, and the results clearly suggest the superiority of the proposed method. Comparison experiments have also been conducted. The compared methods include ICA, PCA, Semi-NMF, Wavelets, EMD and CSP, which further verified the effectivity of SCS-NMF. The SCS-NMF method could obtain better or competitive performance over the state of the art methods, which provides a novel solution for brain pattern analysis from the perspective of structure constraint. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing.

    Science.gov (United States)

    Zhang, Zhongnan; Wen, Tingxi; Huang, Wei; Wang, Meihong; Li, Chunfeng

    2017-01-01

    Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

  14. Stimulus-dependent spiking relationships with the EEG

    Science.gov (United States)

    Snyder, Adam C.

    2015-01-01

    The development and refinement of noninvasive techniques for imaging neural activity is of paramount importance for human neuroscience. Currently, the most accessible and popular technique is electroencephalography (EEG). However, nearly all of what we know about the neural events that underlie EEG signals is based on inference, because of the dearth of studies that have simultaneously paired EEG recordings with direct recordings of single neurons. From the perspective of electrophysiologists there is growing interest in understanding how spiking activity coordinates with large-scale cortical networks. Evidence from recordings at both scales highlights that sensory neurons operate in very distinct states during spontaneous and visually evoked activity, which appear to form extremes in a continuum of coordination in neural networks. We hypothesized that individual neurons have idiosyncratic relationships to large-scale network activity indexed by EEG signals, owing to the neurons' distinct computational roles within the local circuitry. We tested this by recording neuronal populations in visual area V4 of rhesus macaques while we simultaneously recorded EEG. We found substantial heterogeneity in the timing and strength of spike-EEG relationships and that these relationships became more diverse during visual stimulation compared with the spontaneous state. The visual stimulus apparently shifts V4 neurons from a state in which they are relatively uniformly embedded in large-scale network activity to a state in which their distinct roles within the local population are more prominent, suggesting that the specific way in which individual neurons relate to EEG signals may hold clues regarding their computational roles. PMID:26108954

  15. EEG datasets for motor imagery brain-computer interface.

    Science.gov (United States)

    Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

    2017-07-01

    Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

  16. Changes in EEG indices and serotonin concentrations in depression and anxiety disorders

    Directory of Open Access Journals (Sweden)

    I. V. Kichuk

    2016-01-01

    Full Text Available Electroencephalogram (EEG is an important tool to study brain function. EEG can evaluate the current functional state of the brain with high temporal resolution and identify metabolic and ion disorders that cannot be detected by magnetic resonance imaging.Objective: to analyze the relationship between some neurophysiological and biochemical parameters with a Neuro-KM hardware-software complex for the topographic mapping of brain electrical activity.Patients and methods. 75 patients with depression, 101 with anxiety disorders (AD, and 86 control individuals were examined. EEG spectrum and coherence changes were estimated in the depression and AD groups versus the control group. Correlation analysis of EEG indices and blood serotonin concentrations was carried out.Results and discussion. The patients with depression and those with AD as compared to the controls were observed to have similar EEG spectral changes in the beta band. Coherence analysis in the beta-band showed that both disease groups versus the control group had oppositely directed changes: a reduction in intra- and interhemispheric coherence for depression and its increase for AD (p < 0.001. That in the theta and alpha bands revealed that both disease groups had unidirectional interhemispheric coherence changes: a decrease in integration in the alpha band and its increase in the theta and delta bands in the depression and AD groups (p < 0.05 and multidirectional changes in intrahemispheric coherence: its reduction in the depression group and an increase in the AD group (p < 0.05. Correlation analysis of EEG parameters and platelet serotonin concentrations showed opposite correlations of serotonin concentrations and EEG percentage power in the theta and beta bands. When there were higher serotonin concentrations in the patients with depression, EEG demonstrated a preponderance of a synchronization pattern; when these were in the patients with AD, there was a predominance

  17. Entropy of the electroencephalogram as applied in the M-Entropy S ...

    African Journals Online (AJOL)

    Background: It has been suggested that spectral entropy of the electroencephalogram as applied in the M-Entropy S/5TM Module (GE Healthcare) does not detect the effects of nitrous oxide (N2O). The aim of this study was to investigate the effect on entropy by graded increases in N2O concentrations in the presence of a ...

  18. EEG frequency PCA in EEG-ERP dynamics.

    Science.gov (United States)

    Barry, Robert J; De Blasio, Frances M

    2018-05-01

    Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

  19. A Wavelet-based Energetic Approach for the Analysis of Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Abul Hasan Siddiqi

    2012-12-01

    Full Text Available Electroencephalography (EEG is the recording of electrical activity along the scalp produced by the firing of neurons within the brain. The main application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study. EEG signals, like many biomedical signals, are highly non-stationary by their nature. Wavelet analysis has found a prominent position in the investigation of biomedical signals for its ability to analyze such signals, in particular EEG signals. Wavelet transform is capable of separating the signal energy among different frequency bands (i.e., different scales, achieving a good compromise between temporal and frequency resolution. The present study is an attempt at better understanding of the mechanism causing the epileptic disorder and accurate prediction of the occurrence of seizures. In the present paper we identify typical patterns of energy redistribution before and during a seizure using multi-resolution wavelet analysis.

  20. GABAA agonist reduces visual awareness : a masking-EEG experiment

    NARCIS (Netherlands)

    van Loon, Anouk M; Scholte, H Steven; van Gaal, Simon; van der Hoort, Björn J J; Lamme, Victor A F

    Consciousness can be manipulated in many ways. Here, we seek to understand whether two such ways, visual masking and pharmacological intervention, share a common pathway in manipulating visual consciousness. We recorded EEG from human participants who performed a backward-masking task in which they

  1. EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

    Science.gov (United States)

    Shishkin, Sergei L.; Nuzhdin, Yuri O.; Svirin, Evgeny P.; Trofimov, Alexander G.; Fedorova, Anastasia A.; Kozyrskiy, Bogdan L.; Velichkovsky, Boris M.

    2016-01-01

    We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG) marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs) were collected during game's control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant's averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200–500 ms relative to the fixation onset). For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower) results were obtained for the shrinkage Linear Discriminate Analysis (LDA) classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI) can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device

  2. EEG Negativity in Fixations Used for Gaze-Based Control: Toward Converting Intentions into Actions with an Eye-Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Sergei L. Shishkin

    2016-11-01

    Full Text Available We usually look at an object when we are going to manipulate it. Thus, eye tracking can be used to communicate intended actions. An effective human-machine interface, however, should be able to differentiate intentional and spontaneous eye movements. We report an electroencephalogram (EEG marker that differentiates gaze fixations used for control from spontaneous fixations involved in visual exploration. Eight healthy participants played a game with their eye movements only. Their gaze-synchronized EEG data (fixation-related potentials, FRPs were collected during game’s control-on and control-off conditions. A slow negative wave with a maximum in the parietooccipital region was present in each participant’s averaged FRPs in the control-on conditions and was absent or had much lower amplitude in the control-off condition. This wave was similar but not identical to stimulus-preceding negativity, a slow negative wave that can be observed during feedback expectation. Classification of intentional vs. spontaneous fixations was based on amplitude features from 13 EEG channels using 300 ms length segments free from electrooculogram contamination (200..500 ms relative to the fixation onset. For the first fixations in the fixation triplets required to make moves in the game, classified against control-off data, a committee of greedy classifiers provided 0.90 ± 0.07 specificity and 0.38 ± 0.14 sensitivity. Similar (slightly lower results were obtained for the shrinkage LDA classifier. The second and third fixations in the triplets were classified at lower rate. We expect that, with improved feature sets and classifiers, a hybrid dwell-based Eye-Brain-Computer Interface (EBCI can be built using the FRP difference between the intended and spontaneous fixations. If this direction of BCI development will be successful, such a multimodal interface may improve the fluency of interaction and can possibly become the basis for a new input device for

  3. Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

    Science.gov (United States)

    Dmochowski, Jacek P.; Sajda, Paul; Dias, Joao; Parra, Lucas C.

    2012-01-01

    Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity. PMID:22623915

  4. Correlated components of ongoing EEG point to emotionally laden attention - a possible marker of engagement?

    Science.gov (United States)

    Dmochowski, Jacek P; Sajda, Paul; Dias, Joao; Parra, Lucas C

    2012-01-01

    Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from multiple EEG records. The resulting components capture correlations down to a one-second time resolution, thus revealing that peak correlations of neural activity across viewings can occur in remarkable correspondence with arousing moments of the film. Moreover, a significant reduction in neural correlation occurs upon a second viewing of the film or when the narrative is disrupted by presenting its scenes scrambled in time. We also probe oscillatory brain activity during periods of heightened correlation, and observe during such times a significant increase in the theta band for a frontal component and reductions in the alpha and beta frequency bands for parietal and occipital components. Low-resolution EEG tomography of these components suggests that the correlated neural activity is consistent with sources in the cingulate and orbitofrontal cortices. Put together, these results suggest that the observed synchrony reflects attention- and emotion-modulated cortical processing which may be decoded with high temporal resolution by extracting maximally correlated components of neural activity.

  5. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals.

    Science.gov (United States)

    Zhuang, Ning; Zeng, Ying; Yang, Kai; Zhang, Chi; Tong, Li; Yan, Bin

    2018-03-12

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods.

  6. Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics.

    Science.gov (United States)

    Cuesta-Frau, David; Miró-Martínez, Pau; Jordán Núñez, Jorge; Oltra-Crespo, Sandra; Molina Picó, Antonio

    2017-08-01

    This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Investigating Patterns for Self-Induced Emotion Recognition from EEG Signals

    Science.gov (United States)

    Zeng, Ying; Yang, Kai; Tong, Li; Yan, Bin

    2018-01-01

    Most current approaches to emotion recognition are based on neural signals elicited by affective materials such as images, sounds and videos. However, the application of neural patterns in the recognition of self-induced emotions remains uninvestigated. In this study we inferred the patterns and neural signatures of self-induced emotions from electroencephalogram (EEG) signals. The EEG signals of 30 participants were recorded while they watched 18 Chinese movie clips which were intended to elicit six discrete emotions, including joy, neutrality, sadness, disgust, anger and fear. After watching each movie clip the participants were asked to self-induce emotions by recalling a specific scene from each movie. We analyzed the important features, electrode distribution and average neural patterns of different self-induced emotions. Results demonstrated that features related to high-frequency rhythm of EEG signals from electrodes distributed in the bilateral temporal, prefrontal and occipital lobes have outstanding performance in the discrimination of emotions. Moreover, the six discrete categories of self-induced emotion exhibit specific neural patterns and brain topography distributions. We achieved an average accuracy of 87.36% in the discrimination of positive from negative self-induced emotions and 54.52% in the classification of emotions into six discrete categories. Our research will help promote the development of comprehensive endogenous emotion recognition methods. PMID:29534515

  8. Pilot study of EEG in neonates born to mothers with gestational diabetes mellitus.

    Science.gov (United States)

    Léveillé-, Pauline; Hamel, Mathieu; Ardilouze, Jean-Luc; Pasquier, Jean-Charles; Deacon, Charles; Whittingstall, Kevin; Plourde, Mélanie

    2018-05-01

    The goal was to evaluate whether there was neurodevelopmental deficits in newborns born to mothers with gestational diabetes mellitus (GDM) compared to control newborns born to healthy mothers. Forty-six pregnant women (21 controls and 25 GDM) were recruited. Electroencephalogram (EEG) was recorded in the newborns within 48 h after birth. The EEG signal was quantitatively analyzed using power spectral density (PSD); coherence between hemispheres was calculated in paired channels of frontal, temporal, central and occipital regions. The left centro-occipital PSD in control newborns was 12% higher than in GDM newborns (p = 0.036) but was not significant after adjustment for gestational age. While coherence was higher in the frontal regions compared to the occipital regions (p gestational age and less by GDM status of the mothers. However, there is a need to confirm this result with a higher number of mother-newborns. Quantitative EEG in GDM newborns within 48 h after birth is feasible. This study emphasizes the importance of controlling blood glucose during GDM to protect infant brain development. Copyright © 2018 ISDN. Published by Elsevier Ltd. All rights reserved.

  9. An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

    Directory of Open Access Journals (Sweden)

    Hamza Djelouat

    2017-01-01

    Full Text Available The last decade has witnessed tremendous efforts to shape the Internet of things (IoT platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS. CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.

  10. Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.

    Science.gov (United States)

    Shin, Younghak; Lee, Seungchan; Ahn, Minkyu; Cho, Hohyun; Jun, Sung Chan; Lee, Heung-No

    2015-11-01

    One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

    Science.gov (United States)

    Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

    2016-01-01

    Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

  12. Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Mubin, Marizan; Saad, Ismail

    2016-01-01

    In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.

  13. A neural mass model of interconnected regions simulates rhythm propagation observed via TMS-EEG.

    Science.gov (United States)

    Cona, F; Zavaglia, M; Massimini, M; Rosanova, M; Ursino, M

    2011-08-01

    Knowledge of cortical rhythms represents an important aspect of modern neuroscience, to understand how the brain realizes its functions. Recent data suggest that different regions in the brain may exhibit distinct electroencephalogram (EEG) rhythms when perturbed by Transcranial Magnetic Stimulation (TMS) and that these rhythms can change due to the connectivity among regions. In this context, in silico simulations may help the validation of these hypotheses that would be difficult to be verified in vivo. Neural mass models can be very useful to simulate specific aspects of electrical brain activity and, above all, to analyze and identify the overall frequency content of EEG in a cortical region of interest (ROI). In this work we implemented a model of connectivity among cortical regions to fit the impulse responses in three ROIs recorded during a series of TMS/EEG experiments performed in five subjects and using three different impulse intensities. In particular we investigated Brodmann Area (BA) 19 (occipital lobe), BA 7 (parietal lobe) and BA 6 (frontal lobe). Results show that the model can reproduce the natural rhythms of the three regions quite well, acting on a few internal parameters. Moreover, the model can explain most rhythm changes induced by stimulation of another region, and inter-subject variability, by estimating just a few long-range connectivity parameters among ROIs. Copyright © 2011 Elsevier Inc. All rights reserved.

  14. Association of autonomic nervous system and EEG scalp potential during playing 2D Grand Turismo 5.

    Science.gov (United States)

    Subhani, Ahmad Rauf; Likun, Xia; Saeed Malik, Aamir

    2012-01-01

    Cerebral activation and autonomic nervous system have importance in studies such as mental stress. The aim of this study is to analyze variations in EEG scalp potential which may influence autonomic activation of heart while playing video games. Ten healthy participants were recruited in this study. Electroencephalogram (EEG) and electrocardiogram (ECG) signals were measured simultaneously during playing video game and rest conditions. Sympathetic and parasympathetic innervations of heart were evaluated from heart rate variability (HRV), derived from the ECG. Scalp potential was measured by the EEG. The results showed a significant upsurge in the value theta Fz/alpha Pz (p<0.001) while playing game. The results also showed tachycardia while playing video game as compared to rest condition (p<0.005). Normalized low frequency power and ratio of low frequency/high frequency power were significantly increased while playing video game and normalized high frequency power sank during video games. Results showed synchronized activity of cerebellum and sympathetic and parasympathetic innervation of heart.

  15. A Comparison Study on Multidomain EEG Features for Sleep Stage Classification

    Directory of Open Access Journals (Sweden)

    Yu Zhang

    2017-01-01

    Full Text Available Feature extraction from physiological signals of EEG (electroencephalogram is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

  16. Brain oscillations in sport: toward EEG biomakers of performance

    Directory of Open Access Journals (Sweden)

    Guy eCheron

    2016-02-01

    Full Text Available Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The noninvasive nature of high-density electroencephalography (EEG recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

  17. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

    Science.gov (United States)

    Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

  18. Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

    Science.gov (United States)

    Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

    2016-01-01

    Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

  19. Fusion of Electroencephalogram dynamics and musical contents for estimating emotional responses in music listening

    Directory of Open Access Journals (Sweden)

    Yuan-Pin eLin

    2014-05-01

    Full Text Available Electroencephalography (EEG-based emotion classification during music listening has gained increasing attention nowadays due to its promise of potential applications such as musical affective brain-computer interface (ABCI, neuromarketing, music therapy, and implicit multimedia tagging and triggering. However, music is an ecologically valid and complex stimulus that conveys certain emotions to listeners through compositions of musical elements. Using solely EEG signals to distinguish emotions remained challenging. This study aimed to assess the applicability of a multimodal approach by leveraging the EEG dynamics and acoustic characteristics of musical contents for the classification of emotional valence and arousal. To this end, this study adopted machine-learning methods to systematically elucidate the roles of the EEG and music modalities in the emotion modeling. The empirical results suggested that when whole-head EEG signals were available, the inclusion of musical contents did not improve the classification performance. The obtained performance of 74~76% using solely EEG modality was statistically comparable to that using the multimodality approach. However, if EEG dynamics were only available from a small set of electrodes (likely the case in real-life applications, the music modality would play a complementary role and augment the EEG results from around 61% to 67% in valence classification and from around 58% to 67% in arousal classification. The musical timbre appeared to replace less-discriminative EEG features and led to improvements in both valence and arousal classification, whereas musical loudness was contributed specifically to the arousal classification. The present study not only provided principles for constructing an EEG-based multimodal approach, but also revealed the fundamental insights into the interplay of the brain activity and musical contents in emotion modeling.

  20. On the applicability of brain reading for predictive human-machine interfaces in robotics.

    Science.gov (United States)

    Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

    2013-01-01

    The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  1. On the applicability of brain reading for predictive human-machine interfaces in robotics.

    Directory of Open Access Journals (Sweden)

    Elsa Andrea Kirchner

    Full Text Available The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR, a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  2. A dry EEG-system for scientific research and brain-computer interfaces

    Directory of Open Access Journals (Sweden)

    Thorsten Oliver Zander

    2011-05-01

    Full Text Available Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG still forms the method of choice in a wide variety of clinical and research applications. In the context of Brain-Computer Interfacing (BCI, EEG recently has become a tool to enhance Human-Machine Interaction (HMI. EEG could be employed in a wider range of environments, especially for the use of BCI systems in a clinical context or at the homes of patients. However, the application of EEG in these contexts is impeded by the cumbersome preparation of the electrodes with conductive gel that is necessary to lower the impedance between electrodes and scalp. Dry electrodes could provide a solution to this barrier and allow for EEG applications outside the laboratory. In addition, dry electrodes may reduce the time needed for neurological exams in clinical practice. This study evaluates a prototype of a three-channel dry electrode EEG system, comparing it to state-of-the-art conventional EEG electrodes. Two experimental paradigms were used: first, Event-Related Potentials (ERP were investigated with a variant of the oddball paradigm. Second, features of the frequency domain were compared by a paradigm inducing occipital alpha. Furthermore, both paradigms were used to evaluate BCI classification accuracies of both EEG systems. Amplitude and temporal structure of ERPs as well as features in the frequency domain did not differ significantly between the EEG systems. BCI classification accuracies were equally high in both systems when the frequency domain was considered. With respect to the oddball classification accuracy, there were slight differe