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Sample records for group eeg show

  1. Global Manufacturing Research: Experience Exchange Group (EEG) contributions

    DEFF Research Database (Denmark)

    Bruun, Peter

    1998-01-01

    of preliminary studies found interesting to set upan EEG composed of representatives from industry and a researcher. Inthe paper some general research methods pertinent to the areaindustrial management is discussed. The EEG concept is introduced andcharacterised in comparison with the other methods. EEG...... activities aredescribed and a tentative coupling to the phases in a research processis proposed. Following this is a discussion of methodological andquality requirements. It is considered how EEG activities couldpossible contribute to an industrial rooted research. The paper endsup looking at future research......The intention of this paper is to clarify if and how an ExperienceExchange Group (EEG) can be involved in a research process in the areaof industrial management. For exemplification of the topic an ongoingresearch in global manufacturing is referred to. In this research itwas after a series...

  2. Sparse EEG/MEG source estimation via a group lasso.

    Directory of Open Access Journals (Sweden)

    Michael Lim

    Full Text Available Non-invasive recordings of human brain activity through electroencephalography (EEG or magnetoencelphalography (MEG are of value for both basic science and clinical applications in sensory, cognitive, and affective neuroscience. Here we introduce a new approach to estimating the intra-cranial sources of EEG/MEG activity measured from extra-cranial sensors. The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful groups within a functionally-based common space. Detailed simulations using realistic source-geometries and data from a human Visual Evoked Potential experiment demonstrate that the group-lasso method has improved performance over traditional ℓ2 minimum-norm methods. In addition, we show that pooling source estimates across subjects over functionally defined regions of interest results in improvements in the accuracy of source estimates for both the group-lasso and minimum-norm approaches.

  3. 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 ...

  4. Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

    Directory of Open Access Journals (Sweden)

    Jin-Jia Wang

    2015-01-01

    Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

  5. 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.

  6. 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.

  7. Experience Exchange Group (EEG) Approach as a Means for Research to be rooted in Industry

    DEFF Research Database (Denmark)

    Bruun, Peter

    1997-01-01

    of preliminary studies found interesting to set up an EEG composed of representatives from industry and a researcher. In the paper some general research methods pertinent to the area industrial management are discussed. The EEG concept is introduced and characterised in comparison with the other methods. EEG...... activities are described and a tentative coupling to the phases in a research process is proposed. Following this is a discussion of methodological and quality requirements. It is considered how EEG activities could possibly contribute to an industrial rooted research. The paper ends up looking at future......The intention of this paper is to clarify if and how an Experience Exchange Group(EEG) can be involved in a research process in the area of industrial management. For exemplification of the topic an ongoing research in global manufacturing is referred to. In this research it was after a series...

  8. INDUCED EEG GAMMA OSCILLATION ALIGNMENT IMPROVES DIFFERENTIATION BETWEEN AUTISM AND ADHD GROUP RESPONSES IN A FACIAL CATEGORIZATION TASK.

    Science.gov (United States)

    Gross, Eric; El-Baz, Ayman S; Sokhadze, Guela E; Sears, Lonnie; Casanova, Manuel F; Sokhadze, Estate M

    2012-01-01

    INTRODUCTION: Children diagnosed with an autism spectrum disorder (ASD) often lack the ability to recognize and properly respond to emotional stimuli. Emotional deficits also characterize children with attention deficit/hyperactivity disorder (ADHD), in addition to exhibiting limited attention span. These abnormalities may effect a difference in the induced EEG gamma wave burst (35-45 Hz) peaked approximately 300-400 milliseconds following an emotional stimulus. Because induced gamma oscillations are not fixed at a definite point in time post-stimulus, analysis of averaged EEG data with traditional methods may result in an attenuated gamma burst power. METHODS: We used a data alignment technique to improve the averaged data, making it a better representation of the individual induced EEG gamma oscillations. A study was designed to test the response of a subject to emotional stimuli, presented in the form of emotional facial expression images. In a four part experiment, the subjects were instructed to identify gender in the first two blocks of the test, followed by differentiating between basic emotions in the final two blocks (i.e. anger vs. disgust). EEG data was collected from ASD (n=10), ADHD (n=9), and control (n=11) subjects via a 128 channel EGI system, and processed through a continuous wavelet transform and bandpass filter to isolate the gamma frequencies. A custom MATLAB code was used to align the data from individual trials between 200-600 ms post-stimulus, EEG site, and condition by maximizing the Pearson product-moment correlation coefficient between trials. The gamma power for the 400 ms window of maximum induced gamma burst was then calculated and compared between subject groups. RESULTS AND CONCLUSION: Condition (anger/disgust recognition, gender recognition) × Alignment × Group (ADHD, ASD, Controls) interaction was significant at most of parietal topographies (e.g., P3-P4, P7-P8). These interactions were better manifested in the aligned data set

  9. Brain order disorder 2nd group report of f-EEG

    Science.gov (United States)

    Lalonde, Francois; Gogtay, Nitin; Giedd, Jay; Vydelingum, Nadarajen; Brown, David; Tran, Binh Q.; Hsu, Charles; Hsu, Ming-Kai; Cha, Jae; Jenkins, Jeffrey; Ma, Lien; Willey, Jefferson; Wu, Jerry; Oh, Kenneth; Landa, Joseph; Lin, C. T.; Jung, T. P.; Makeig, Scott; Morabito, Carlo Francesco; Moon, Qyu; Yamakawa, Takeshi; Lee, Soo-Young; Lee, Jong-Hwan; Szu, Harold H.; Kaur, Balvinder; Byrd, Kenneth; Dang, Karen; Krzywicki, Alan; Familoni, Babajide O.; Larson, Louis; Harkrider, Susan; Krapels, Keith A.; Dai, Liyi

    2014-05-01

    Since the Brain Order Disorder (BOD) group reported on a high density Electroencephalogram (EEG) to capture the neuronal information using EEG to wirelessly interface with a Smartphone [1,2], a larger BOD group has been assembled, including the Obama BRAIN program, CUA Brain Computer Interface Lab and the UCSD Swartz Computational Neuroscience Center. We can implement the pair-electrodes correlation functions in order to operate in a real time daily environment, which is of the computation complexity of O(N3) for N=102~3 known as functional f-EEG. The daily monitoring requires two areas of focus. Area #(1) to quantify the neuronal information flow under arbitrary daily stimuli-response sources. Approach to #1: (i) We have asserted that the sources contained in the EEG signals may be discovered by an unsupervised learning neural network called blind sources separation (BSS) of independent entropy components, based on the irreversible Boltzmann cellular thermodynamics(ΔS function. (i) Although the entropy itself is not the information per se, but the concurrence of the entropy sources is the information flow as a functional-EEG, sketched in this 2nd BOD report. Area #(2) applying EEG bio-feedback will improve collective decision making (TBD). Approach to #2: We introduce a novel performance quality metrics, in terms of the throughput rate of faster (Δt) & more accurate (ΔA) decision making, which applies to individual, as well as team brain dynamics. Following Nobel Laureate Daniel Kahnmen's novel "Thinking fast and slow", through the brainwave biofeedback we can first identify an individual's "anchored cognitive bias sources". This is done in order to remove the biases by means of individually tailored pre-processing. Then the training effectiveness can be maximized by the collective product Δt * ΔA. For Area #1, we compute a spatiotemporally windowed EEG in vitro average using adaptive time-window sampling. The sampling rate depends on the type of neuronal

  10. Data-Driven Visualization and Group Analysis of Multichannel EEG Coherence with Functional Units

    NARCIS (Netherlands)

    Caat, Michael ten; Maurits, Natasha M.; Roerdink, Jos B.T.M.

    2008-01-01

    A typical data- driven visualization of electroencephalography ( EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter,

  11. Ovalbumin with Glycated Carboxyl Groups Shows Membrane-Damaging Activity

    Directory of Open Access Journals (Sweden)

    Ching-Chia Tang

    2017-02-01

    Full Text Available The aim of the present study was to investigate whether glycated ovalbumin (OVA showed novel activity at the lipid-water interface. Mannosylated OVA (Man-OVA was prepared by modification of the carboxyl groups with p-aminophenyl α-dextro (d-mannopyranoside. An increase in the number of modified carboxyl groups increased the membrane-damaging activity of Man-OVA on cell membrane-mimicking vesicles, whereas OVA did not induce membrane permeability in the tested phospholipid vesicles. The glycation of carboxyl groups caused a notable change in the gross conformation of OVA. Moreover, owing to their spatial positions, the Trp residues in Man-OVA were more exposed, unlike those in OVA. Fluorescence quenching studies suggested that the Trp residues in Man-OVA were located on the interface binds with the lipid vesicles, and their microenvironment was abundant in positively charged residues. Although OVA and Man-OVA showed a similar binding affinity for lipid vesicles, the lipid-interacting feature of Man-OVA was distinct from that of OVA. Chemical modification studies revealed that Lys and Arg residues, but not Trp residues, played a crucial role in the membrane-damaging activity of Man-OVA. Taken together, our data suggest that glycation of carboxyl groups causes changes in the structural properties and membrane-interacting features of OVA, generating OVA with membrane-perturbing activities at the lipid-water interface.

  12. Surface EEG Shows that Functional Segregation via Phase Coupling Contributes to the Neural Substrate of Mental Calculations

    Science.gov (United States)

    Dimitriadis, Stavros I.; Kanatsouli, Kassiani; Laskaris, Nikolaos A.; Tsirka, Vasso; Vourkas, Michael; Micheloyannis, Sifis

    2012-01-01

    Multichannel EEG traces from healthy subjects are used to investigate the brain's self-organisation tendencies during two different mental arithmetic tasks. By making a comparison with a control-state in the form of a classification problem, we can detect and quantify the changes in coordinated brain activity in terms of functional connectivity.…

  13. Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.

    Science.gov (United States)

    Ponomarev, Valery A; Mueller, Andreas; Candrian, Gian; Grin-Yatsenko, Vera A; Kropotov, Juri D

    2014-01-01

    To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  14. 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

  15. EEG correlates of virtual reality hypnosis.

    Science.gov (United States)

    White, David; Ciorciari, Joseph; Carbis, Colin; Liley, David

    2009-01-01

    The study investigated hypnosis-related electroencephalographic (EEG) coherence and power spectra changes in high and low hypnotizables (Stanford Hypnotic Clinical Scale) induced by a virtual reality hypnosis (VRH) induction system. In this study, the EEG from 17 participants (Mean age = 21.35, SD = 1.58) were compared based on their hypnotizability score. The EEG recording associated with a 2-minute, eyes-closed baseline state was compared to the EEG during a hypnosis-related state. This novel induction system was able to produce EEG findings consistent with previous hypnosis literature. Interactions of significance were found with EEG beta coherence. The high susceptibility group (n = 7) showed decreased coherence, while the low susceptibility group (n = 10) demonstrated an increase in coherence between medial frontal and lateral left prefrontal sites. Methodological and efficacy issues are discussed.

  16. OGJ group earnings show big gain for 1993

    International Nuclear Information System (INIS)

    Beck, R.J.; Sanders, V.

    1994-01-01

    Earnings for Oil and Gas Journal's group of 22 large US oil companies advanced sharply last year, increasing 70.9% from 1992. Group profits totaled $16.1 billion, with the gain stemming largely from lack of one time charges that depressed earnings in 1992. Adoption of new accounting rules associated with future postretirement costs caused the 1992 charges. US exploration and production earnings were mixed, tied in part to oil and gas production volumes. Higher gas prices and production helped boost earnings for a number of companies. But the earnings improvement from gas was offset by reduced oil production and prices. Results from non-U.S. E and P also were mixed. Average worldwide crude oil export prices were down from year earlier levels. However, for some companies this was offset by higher production levels and lower exploration costs. Earnings from refining and marketing were improved from 1992 levels, particularly for non-US operations. Lower feedstock costs, especially in the fourth quarter, helped boost earnings in this sector. Refining earnings gains also flowed from lower costs due to restructuring and an accompanying improvement in operating efficiency. The paper discusses world trends, operations and prices, US exploration and production, non-US E and P, US and non-US refining and marketing, petrochemicals, and the forecast for future demand

  17. 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

  18. Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies.

    Science.gov (United States)

    Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C

    2013-03-01

    Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.

  19. I won't tell: Young children show loyalty to their group by keeping group secrets.

    Science.gov (United States)

    Misch, Antonia; Over, Harriet; Carpenter, Malinda

    2016-02-01

    Group loyalty is highly valued. However, little is known about young children's loyal behavior. This study tested whether 4- and 5-year-olds (N=96) remain loyal to their group even when betraying it would be materially advantageous. Children and four puppets were allocated to novel groups. Two of these puppets (either in-group or out-group members) then told children a group secret and urged them not to disclose the secret. Another puppet (not assigned to either group) then bribed children with stickers to tell the secret. Across ages, children were significantly less likely to reveal the secret in the in-group condition than in the out-group condition. Thus, even young children are willing to pay a cost to be loyal to their group. Copyright © 2015 Elsevier Inc. All rights reserved.

  20. Comparison of background EEG activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster-based permutation statistical testing.

    Directory of Open Access Journals (Sweden)

    Jose Antonio Urigüen

    Full Text Available Idiopathic epilepsy is characterized by generalized seizures with no apparent cause. One of its main problems is the lack of biomarkers to monitor the evolution of patients. The only tools they can use are limited to inspecting the amount of seizures during previous periods of time and assessing the existence of interictal discharges. As a result, there is a need for improving the tools to assist the diagnosis and follow up of these patients. The goal of the present study is to compare and find a way to differentiate between two groups of patients suffering from idiopathic epilepsy, one group that could be followed-up by means of specific electroencephalographic (EEG signatures (intercritical activity present, and another one that could not due to the absence of these markers. To do that, we analyzed the background EEG activity of each in the absence of seizures and epileptic intercritical activity. We used the Shannon spectral entropy (SSE as a metric to discriminate between the two groups and performed permutation-based statistical tests to detect the set of frequencies that show significant differences. By constraining the spectral entropy estimation to the [6.25-12.89 Hz range, we detect statistical differences (at below 0.05 alpha-level between both types of epileptic patients at all available recording channels. Interestingly, entropy values follow a trend that is inversely related to the elapsed time from the last seizure. Indeed, this trend shows asymptotical convergence to the SSE values measured in a group of healthy subjects, which present SSE values lower than any of the two groups of patients. All these results suggest that the SSE, measured in a specific range of frequencies, could serve to follow up the evolution of patients suffering from idiopathic epilepsy. Future studies remain to be conducted in order to assess the predictive value of this approach for the anticipation of seizures.

  1. EEG activity during estral cycle in the rat.

    Science.gov (United States)

    Corsi-Cabrera, M; Juárez, J; Ponce-de-León, M; Ramos, J; Velázquez, P N

    1992-10-01

    EEG activity was recorded from right and left parietal cortex in adult female rats daily during 6 days. Immediately after EEG recording vaginal smears were taken and were microscopically analyzed to determine the estral stage. Absolute and relative powers and interhemispheric correlation of EEG activity were calculated and compared between estral stages. Interhemispheric correlation was significantly lower during diestrous as compared to proestrous and estrous. Absolute and relative powers did not show significant differences between estral stages. Absolute powers of alpha1, alpha2, beta1 and beta2 bands were significantly higher at the right parietal cortex. Comparisons of the same EEG records with estral stages randomly grouped showed no significant differences for any of the EEG parameters. EEG activity is a sensitive tool to study functional changes related to the estral cycle.

  2. 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.

  3. Memories of attachment hamper EEG cortical connectivity in dissociative patients.

    Science.gov (United States)

    Farina, Benedetto; Speranza, Anna Maria; Dittoni, Serena; Gnoni, Valentina; Trentini, Cristina; Vergano, Carola Maggiora; Liotti, Giovanni; Brunetti, Riccardo; Testani, Elisa; Della Marca, Giacomo

    2014-08-01

    In this study, we evaluated cortical connectivity modifications by electroencephalography (EEG) lagged coherence analysis, in subjects with dissociative disorders and in controls, after retrieval of attachment memories. We asked thirteen patients with dissociative disorders and thirteen age- and sex-matched healthy controls to retrieve personal attachment-related autobiographical memories through adult attachment interviews (AAI). EEG was recorded in the closed eyes resting state before and after the AAI. EEG lagged coherence before and after AAI was compared in all subjects. In the control group, memories of attachment promoted a widespread increase in EEG connectivity, in particular in the high-frequency EEG bands. Compared to controls, dissociative patients did not show an increase in EEG connectivity after the AAI. Conclusions: These results shed light on the neurophysiology of the disintegrative effect of retrieval of traumatic attachment memories in dissociative patients.

  4. EEG reveals an early influence of social conformity on visual processing in group pressure situations.

    Science.gov (United States)

    Trautmann-Lengsfeld, Sina Alexa; Herrmann, Christoph Siegfried

    2013-01-01

    Humans are social beings and often have to perceive and perform within groups. In conflict situations, this puts them under pressure to either adhere to the group opinion or to risk controversy with the group. Psychological experiments have demonstrated that study participants adapt to erroneous group opinions in visual perception tasks, which they can easily solve correctly when performing on their own. Until this point, however, it is unclear whether this phenomenon of social conformity influences early stages of perception that might not even reach awareness or later stages of conscious decision-making. Using electroencephalography, this study has revealed that social conformity to the wrong group opinion resulted in a decrease of the posterior-lateral P1 in line with a decrease of the later centro-parietal P3. These results suggest that group pressure situations impact early unconscious visual perceptual processing, which results in a later diminished stimulus discrimination and an adaptation even to the wrong group opinion. These findings might have important implications for understanding social behavior in group settings and are discussed within the framework of social influence on eyewitness testimony.

  5. Characterization of sleep need dissipation using EEG based slow-wave activity analysis in two age groups

    NARCIS (Netherlands)

    Garcia-Molina, G.; Baehr, K.; Steele, B.; Tsoneva, T.K.; Pfundtner, S.; Mahadevan, A.; Papas, N.; Riedner, B.; Tononi, G.; White, D.

    2017-01-01

    Introduction: In the two-process model of sleep regulation, slow-wave activity (SWA, EEG power in the 0.5–4 Hz band) is a direct indicator of sleep need. SWA builds up during NREM sleep, declines before the onset of REM sleep, remains low during REM and the level of increase in successive NREM

  6. Group-Level EEG-Processing Pipeline for Flexible Single Trial-Based Analyses Including Linear Mixed Models.

    Science.gov (United States)

    Frömer, Romy; Maier, Martin; Abdel Rahman, Rasha

    2018-01-01

    Here we present an application of an EEG processing pipeline customizing EEGLAB and FieldTrip functions, specifically optimized to flexibly analyze EEG data based on single trial information. The key component of our approach is to create a comprehensive 3-D EEG data structure including all trials and all participants maintaining the original order of recording. This allows straightforward access to subsets of the data based on any information available in a behavioral data structure matched with the EEG data (experimental conditions, but also performance indicators, such accuracy or RTs of single trials). In the present study we exploit this structure to compute linear mixed models (LMMs, using lmer in R) including random intercepts and slopes for items. This information can easily be read out from the matched behavioral data, whereas it might not be accessible in traditional ERP approaches without substantial effort. We further provide easily adaptable scripts for performing cluster-based permutation tests (as implemented in FieldTrip), as a more robust alternative to traditional omnibus ANOVAs. Our approach is particularly advantageous for data with parametric within-subject covariates (e.g., performance) and/or multiple complex stimuli (such as words, faces or objects) that vary in features affecting cognitive processes and ERPs (such as word frequency, salience or familiarity), which are sometimes hard to control experimentally or might themselves constitute variables of interest. The present dataset was recorded from 40 participants who performed a visual search task on previously unfamiliar objects, presented either visually intact or blurred. MATLAB as well as R scripts are provided that can be adapted to different datasets.

  7. Discovering EEG resting state alterations of semantic dementia.

    Science.gov (United States)

    Grieder, Matthias; Koenig, Thomas; Kinoshita, Toshihiko; Utsunomiya, Keita; Wahlund, Lars-Olof; Dierks, Thomas; Nishida, Keiichiro

    2016-05-01

    Diagnosis of semantic dementia relies on cost-intensive MRI or PET, although resting EEG markers of other dementias have been reported. Yet the view still holds that resting EEG in patients with semantic dementia is normal. However, studies using increasingly sophisticated EEG analysis methods have demonstrated that slightest alterations of functional brain states can be detected. We analyzed the common four resting EEG microstates (A, B, C, and D) of 8 patients with semantic dementia in comparison with 8 healthy controls and 8 patients with Alzheimer's disease. Topographical differences between the groups were found in microstate classes B and C, while microstate classes A and D were comparable. The data showed that the semantic dementia group had a peculiar microstate E, but the commonly found microstate C was lacking. Furthermore, the presence of microstate E was significantly correlated with lower MMSE and language scores. Alterations in resting EEG can be found in semantic dementia. Topographical shifts in microstate C might be related to semantic memory deficits. This is the first study that discovered resting state EEG abnormality in semantic dementia. The notion that resting EEG in this dementia subtype is normal has to be revised. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. 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 ...

  9. 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.

  10. Frontal EEG asymmetry in borderline personality disorder is associated with alexithymia.

    Science.gov (United States)

    Flasbeck, Vera; Popkirov, Stoyan; Brüne, Martin

    2017-01-01

    Frontal EEG asymmetry is a widely studied correlate of emotion processing and psychopathology. Recent research suggests that frontal EEG asymmetry during resting state is related to approach/withdrawal motivation and is also found in affective disorders such as major depressive disorder. Patients with borderline personality disorder (BPD) show aberrant behavior in relation to both approach and withdrawal motivation, which may arguably be associated with their difficulties in emotion processing. The occurrence and significance of frontal EEG asymmetry in BPD, however, has received little attention. Thirty-seven BPD patients and 39 controls underwent resting EEG and completed several psychometric questionnaires. While there were no between-group differences in frontal EEG asymmetry, in BPD frontal EEG asymmetry scores correlated significantly with alexithymia. That is, higher alexithymia scores were associated with relatively lower right-frontal activity. A subsequent analysis corroborated the significant interaction between frontal EEG asymmetry and alexithymia, which was moderated by group. Our findings reveal that lower right frontal EEG asymmetry is associated with alexithymia in patients with BPD. This finding is in accordance with neurophysiological models of alexithymia that implicate a right hemisphere impairment in emotion processing, and could suggest frontal EEG asymmetry as a potential biomarker of relevant psychopathology in these patients.

  11. Characteristics of late-onset epilepsy and EEG findings in children with autism spectrum disorders

    Directory of Open Access Journals (Sweden)

    Haneul Lee

    2011-01-01

    Full Text Available Purpose: To investigate the clinical characteristics of late-onset epilepsy combined with autism spectrum disorder (ASD, and the relationship between certain types of electroencephalography (EEG abnormalities in ASD and associated neuropsychological problems. Methods: Thirty patients diagnosed with ASD in early childhood and later developed clinical seizures were reviewed retrospectively. First, the clinical characteristics, language and behavioral regression, and EEG findings of these late-onset epilepsy patients with ASD were investigated. The patients were then classified into 2 groups according to the severity of the EEG abnormalities in the background rhythm and paroxysmal discharges. In the severe group, EEG showed persistent asymmetry, slow and disorganized background rhythms, and continuous sharp and slow waves during slow sleep (CSWS. Results: Between the two groups, there was no statistically significant difference in mean age (P=0.259, age of epilepsy diagnosis (P=0.237, associated family history (P=0.074, and positive abnormal magnetic resonance image (MRI findings (P=0.084. The severe EEG group tended to have more neuropsychological problems (P=0.074. The severe group statistically showed more electrographic seizures in EEG (P =0.000. Rett syndrome was correlated with more severe EEG abnormalities (P=0.002. Although formal cognitive function tests were not performed, the parents reported an improvement in neuropsychological function on the follow up checkup according to a parent’s questionnaire. Conclusion: Although some ASD patients with late-onset epilepsy showed severe EEG abnormalities, including CSWS, they generally showed an improvement in EEG and clinical symptoms in the longterm follow up. In addition, severe EEG abnormalities tended to be related to the neuropsychological function.

  12. Objective Audiometry using Ear-EEG

    DEFF Research Database (Denmark)

    Christensen, Christian Bech; Kidmose, Preben

    Recently, a novel electroencephalographic (EEG) method called ear-EEG [1], that enable recording of auditory evoked potentials (AEPs) from a personalized earpiece was introduced. Initial investigations show that well established AEPs, such as ASSR and P1-N1-P2 complex can be observed from ear-EEG...

  13. Prognostic value of EEG in different etiological types of coma.

    Science.gov (United States)

    Khaburzania, M; Beridze, M

    2013-06-01

    Study aimed at evaluation of prognostic value of standard EEG in different etiology of coma and the influence of etiological factor on the EEG patterns and coma outcome. Totally 175 coma patients were investigated. Patients were evaluated by Glasgow Coma Scale (GCS), clinically and by 16 channel electroencephalography. Auditory evoked potentials studied by EEG -regime for evoked potentials in patients with vegetative state (VS). Patients divided in 8 groups according to coma etiology. All patients were studied for photoreaction, brainstem reflexes, localization of sound and pain, length of coma state and outcome. Brain injury visualized by conventional CT. Outcome defined as death, VS, recovery with disability and without disability. Disability was rated by Disability Rating Scale (DRS). Recovered patients assessed by Mini Mental State Examination (MMSE) scale. Statistics performed by SPSS-11.0. From 175 coma patients 55 patients died, 23 patients found in VS, 97 patients recovered with and without disability. In all etiological groups of coma the background EEG patterns were established. Correspondence analysis of all investigated factors revealed that sound localization had the significant association with EEG delta and theta rhythms and with recovery from coma state (Chi-sqr. =31.10493; p= 0.000001). Among 23 VS patients 9 patients had the signs of MCS and showed the long latency waves (p300) after binaural stimulation. The high amplitude theta frequencies in frontal and temporal lobes significantly correlated with prolongation of latency of cognitive evoked potentials (r=+0.47; pEEG patterns' association with coma outcome only in hemorrhagic and traumatic coma (chi-sqr.=12.95; pEEG patterns and coma outcome. Low amplitude decreased power delta and theta frequencies correlated with SND in survived coma patients (r=+0.21; pEEG is the useful tool for elucidation of coma patients with a high probability to recover as well as those patients, who are at high risk of

  14. Modulation of EEG Theta Band Signal Complexity by Music Therapy

    Science.gov (United States)

    Bhattacharya, Joydeep; Lee, Eun-Jeong

    The primary goal of this study was to investigate the impact of monochord (MC) sounds, a type of archaic sounds used in music therapy, on the neural complexity of EEG signals obtained from patients undergoing chemotherapy. The secondary goal was to compare the EEG signal complexity values for monochords with those for progressive muscle relaxation (PMR), an alternative therapy for relaxation. Forty cancer patients were randomly allocated to one of the two relaxation groups, MC and PMR, over a period of six months; continuous EEG signals were recorded during the first and last sessions. EEG signals were analyzed by applying signal mode complexity, a measure of complexity of neuronal oscillations. Across sessions, both groups showed a modulation of complexity of beta-2 band (20-29Hz) at midfrontal regions, but only MC group showed a modulation of complexity of theta band (3.5-7.5Hz) at posterior regions. Therefore, the neuronal complexity patterns showed different changes in EEG frequency band specific complexity resulting in two different types of interventions. Moreover, the different neural responses to listening to monochords and PMR were observed after regular relaxation interventions over a short time span.

  15. 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.

  16. Group and Individual Variability in Mouse Pup Isolation Calls Recorded on the Same Day Show Stability

    Directory of Open Access Journals (Sweden)

    Terra D. Barnes

    2017-12-01

    Full Text Available Mice produce ultrasonic vocalizations (USVs in a variety of social situations, and USVs have been leveraged to study many neurological diseases including verbal dyspraxia, depression, autism and stuttering. Pups produce isolation calls, a common USV, spontaneously when they are isolated from their mother during the first 2 weeks of life. Several genetic manipulations affect (and often reduce pup isolation calls in mice. To facilitate the use of this assay as a means of testing whether significant functional differences in genotypes exist instead of contextual differences, we test the variability inherent in many commons measures of mouse vocalizations. Here we use biological consistency as a way of determining which are reproducible in mouse pup vocalizations. We present a comprehensive analysis of the normal variability of these vocalizations in groups of mice, individual mice and different strains of mice. To control for maturation effects, we recorded pup isolation calls in the same group of C57BL/6J 5 days old mice twice, with 1 h of rest in between recordings. In almost all cases, the group averages between the first and second recordings were the same. We also found that there were high correlations in some parameters in individual mice across recording while others were not well correlated. These findings could be replicated for the majority of features in a separate group of C57BL/6J mice and a group of 129/SvEvBrd-C57BL/6J mice. The averages of these mouse USV features are highly consistent and represent a robust assay to test the effects of genetic and other interventions in the experimental setting.

  17. Profits for OGJ group show big gain in 1993; revenues dip

    International Nuclear Information System (INIS)

    Beck, R.J.; Sanders, V.

    1994-01-01

    Earnings for Oil ampersand Gas Journal's group of 22 large US oil companies jumped sharply last year, increasing 78.6% from 1992. Profits totaled $16.2 billion in 1993, compared with $9.1 billion in 1992. This is in sharp contrast to performance in 1992, when group net income fell 47%. That was due mainly to costs related to restructuring, staff reductions, and adoption of new accounting rules. The new rules moved forward some charges stemming from future retirement benefits and caused a substantial slide in 1992 profits for a number of companies. The absence of similar charges last year was a major reason for the increase in earnings. The paper describes the earnings by sector, oil and gas prices, financial indicators, exploration and production activities, refining and marketing activities, earnings from petrochemicals, capital spending, and the outlook for 1994 performance

  18. Prediction of rhythmic and periodic EEG patterns and seizures on continuous EEG with early epileptiform discharges.

    Science.gov (United States)

    Koren, J; Herta, J; Draschtak, S; Pötzl, G; Pirker, S; Fürbass, F; Hartmann, M; Kluge, T; Baumgartner, C

    2015-08-01

    Continuous EEG (cEEG) is necessary to document nonconvulsive seizures (NCS), nonconvulsive status epilepticus (NCSE), as well as rhythmic and periodic EEG patterns of 'ictal-interictal uncertainty' (RPPIIU) including periodic discharges, rhythmic delta activity, and spike-and-wave complexes in neurological intensive care patients. However, cEEG is associated with significant recording and analysis efforts. Therefore, predictors from short-term routine EEG with a reasonably high yield are urgently needed in order to select patients for evaluation with cEEG. The aim of this study was to assess the prognostic significance of early epileptiform discharges (i.e., within the first 30 min of EEG recording) on the following: (1) incidence of ictal EEG patterns and RPPIIU on subsequent cEEG, (2) occurrence of acute convulsive seizures during the ICU stay, and (3) functional outcome after 6 months of follow-up. We conducted a separate analysis of the first 30 min and the remaining segments of prospective cEEG recordings according to the ACNS Standardized Critical Care EEG Terminology as well as NCS criteria and review of clinical data of 32 neurological critical care patients. In 17 patients with epileptiform discharges within the first 30 min of EEG (group 1), electrographic seizures were observed in 23.5% (n = 4), rhythmic or periodic EEG patterns of 'ictal-interictal uncertainty' in 64.7% (n = 11), and neither electrographic seizures nor RPPIIU in 11.8% (n = 2). In 15 patients with no epileptiform discharges in the first 30 min of EEG (group 2), no electrographic seizures were recorded on subsequent cEEG, RPPIIU were seen in 26.7% (n = 4), and neither electrographic seizures nor RPPIIU in 73.3% (n = 11). The incidence of EEG patterns on cEEG was significantly different between the two groups (p = 0.008). Patients with early epileptiform discharges developed acute seizures more frequently than patients without early epileptiform discharges (p = 0.009). Finally, functional

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. Singular spectrum analysis of sleep EEG in insomnia.

    Science.gov (United States)

    Aydın, Serap; Saraoǧlu, Hamdi Melih; Kara, Sadık

    2011-08-01

    In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.

  4. Temporal lobe deficits in murderers: EEG findings undetected by PET.

    Science.gov (United States)

    Gatzke-Kopp, L M; Raine, A; Buchsbaum, M; LaCasse, L

    2001-01-01

    This study evaluates electroencephalography (EEG) and positron emission tomography (PET) in the same subjects. Fourteen murderers were assessed by using both PET (while they were performing the continuous performance task) and EEG during a resting state. EEG revealed significant increases in slow-wave activity in the temporal, but not frontal, lobe in murderers, in contrast to prior PET findings that showed reduced prefrontal, but not temporal, glucose metabolism. Results suggest that resting EEG shows empirical utility distinct from PET activation findings.

  5. Test-retest reliability of cognitive EEG

    Science.gov (United States)

    McEvoy, L. K.; Smith, M. E.; Gevins, A.

    2000-01-01

    OBJECTIVE: Task-related EEG is sensitive to changes in cognitive state produced by increased task difficulty and by transient impairment. If task-related EEG has high test-retest reliability, it could be used as part of a clinical test to assess changes in cognitive function. The aim of this study was to determine the reliability of the EEG recorded during the performance of a working memory (WM) task and a psychomotor vigilance task (PVT). METHODS: EEG was recorded while subjects rested quietly and while they performed the tasks. Within session (test-retest interval of approximately 1 h) and between session (test-retest interval of approximately 7 days) reliability was calculated for four EEG components: frontal midline theta at Fz, posterior theta at Pz, and slow and fast alpha at Pz. RESULTS: Task-related EEG was highly reliable within and between sessions (r0.9 for all components in WM task, and r0.8 for all components in the PVT). Resting EEG also showed high reliability, although the magnitude of the correlation was somewhat smaller than that of the task-related EEG (r0.7 for all 4 components). CONCLUSIONS: These results suggest that under appropriate conditions, task-related EEG has sufficient retest reliability for use in assessing clinical changes in cognitive status.

  6. 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...

  7. [EEG changes in symptomatic headache caused by bruxism].

    Science.gov (United States)

    Wieselmann, G; Grabmair, W; Logar, C; Permann, R; Moser, F

    1987-02-20

    EEG recordings were carried out on 36 patients with the verified diagnosis of bruxism and unilateral headache. Occlusal splints were applied in the long-term management of these patients. Initial EEG recordings showed pathological changes in 56% of the patients. The EEG recordings were repeated two and six weeks later in these patients and following improvement in the clinical symptomatology pathological EEG patterns were detected in only 22% of all cases. This decrease is of statistical significance.

  8. 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

  9. Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

    Directory of Open Access Journals (Sweden)

    Galina V. Portnova

    2018-01-01

    Full Text Available Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

  10. 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.

  11. Concordance and discordance between PET images and foci of scalp EEG

    International Nuclear Information System (INIS)

    Iinuma, Kazuie; Haginoya, Kazuhiro; Yanai, Kazuhiko; Hatazawa, Jun; Ito, Masatoshi.

    1989-01-01

    Epileptic foci were determined by scalp EEG and positron emission tomography (PET) with fluorine 18 in 22 children with partial epilepsy (PE, n=13) and Lennoxy-Gastaut syndrome (LGS, n=9). The patients ranged in age from 6 to 18 years. The pattern of hypometabolism was classified into the following 4 categories: non-focal, localized, hemispheric, and diffuse. In the group of PE patients, 11 showed a relative agreement between the EEG foci and region of a low cerebral metabolic rate for glucose (CMRglc) determined by PET. A decreased CMRglc was matched with the EEG foci in 4 patients with LGS. A tendency of a higher relationship between the EEG foci and PET images was significant in PE than LGS. (N.K.)

  12. Preterm EEG: a multimodal neurophysiological protocol.

    Science.gov (United States)

    Stjerna, Susanna; Voipio, Juha; Metsäranta, Marjo; Kaila, Kai; Vanhatalo, Sampsa

    2012-02-18

    Since its introduction in early 1950s, electroencephalography (EEG) has been widely used in the neonatal intensive care units (NICU) for assessment and monitoring of brain function in preterm and term babies. Most common indications are the diagnosis of epileptic seizures, assessment of brain maturity, and recovery from hypoxic-ischemic events. EEG recording techniques and the understanding of neonatal EEG signals have dramatically improved, but these advances have been slow to penetrate through the clinical traditions. The aim of this presentation is to bring theory and practice of advanced EEG recording available for neonatal units. In the theoretical part, we will present animations to illustrate how a preterm brain gives rise to spontaneous and evoked EEG activities, both of which are unique to this developmental phase, as well as crucial for a proper brain maturation. Recent animal work has shown that the structural brain development is clearly reflected in early EEG activity. Most important structures in this regard are the growing long range connections and the transient cortical structure, subplate. Sensory stimuli in a preterm baby will generate responses that are seen at a single trial level, and they have underpinnings in the subplate-cortex interaction. This brings neonatal EEG readily into a multimodal study, where EEG is not only recording cortical function, but it also tests subplate function via different sensory modalities. Finally, introduction of clinically suitable dense array EEG caps, as well as amplifiers capable of recording low frequencies, have disclosed multitude of brain activities that have as yet been overlooked. In the practical part of this video, we show how a multimodal, dense array EEG study is performed in neonatal intensive care unit from a preterm baby in the incubator. The video demonstrates preparation of the baby and incubator, application of the EEG cap, and performance of the sensory stimulations.

  13. Engagement Assessment Using EEG Signals

    Science.gov (United States)

    Li, Feng; Li, Jiang; McKenzie, Frederic; Zhang, Guangfan; Wang, Wei; Pepe, Aaron; Xu, Roger; Schnell, Thomas; Anderson, Nick; Heitkamp, Dean

    2012-01-01

    In this paper, we present methods to analyze and improve an EEG-based engagement assessment approach, consisting of data preprocessing, feature extraction and engagement state classification. During data preprocessing, spikes, baseline drift and saturation caused by recording devices in EEG signals are identified and eliminated, and a wavelet based method is utilized to remove ocular and muscular artifacts in the EEG recordings. In feature extraction, power spectrum densities with 1 Hz bin are calculated as features, and these features are analyzed using the Fisher score and the one way ANOVA method. In the classification step, a committee classifier is trained based on the extracted features to assess engagement status. Finally, experiment results showed that there exist significant differences in the extracted features among different subjects, and we have implemented a feature normalization procedure to mitigate the differences and significantly improved the engagement assessment performance.

  14. Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses.

    Science.gov (United States)

    Cerquera, Alexander; Vollebregt, Madelon A; Arns, Martijn

    2018-03-01

    Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.

  15. EEG-vigilance differences between patients with borderline personality disorder, patients with obsessive-compulsive disorder and healthy controls.

    Science.gov (United States)

    Hegerl, Ulrich; Stein, Michael; Mulert, Christoph; Mergl, Roland; Olbrich, Sebastian; Dichgans, Eva; Rujescu, Dan; Pogarell, Oliver

    2008-04-01

    The regulation of brain activation, as assessed with the EEG, is a state modulated trait. A decline to lowered EEG-vigilance states has been found to be associated with emotional instability in older studies, but has not been systematically studied in patients with borderline personality disorder (BPD). Twenty unmedicated BPD patients were compared to 20 unmedicated patients with obsessive-compulsive disorder (OCD) as well as 20 healthy controls concerning their EEG-vigilance regulation over a 5-min period assessed with an algorithm classifying every artefact-free 2-s EEG segment into the EEG-vigilance state (A1-A3, B (=non-A)). If the alpha power was posterior more than 55% of the whole alpha power (anterior + posterior) in the artefact-free EEG-segments, that segment was marked as A1, if it was anterior more than 55% of the whole alpha power, as A3. For A2 the following rule was defined: Posterior or anterior alpha between 50 and 55% of the whole alpha power.BPD patients showed significantly lower rates of EEG-vigilance state A compared to OCD patients, indicating a lowered EEG-vigilance. All three groups showed a decrease in the rate of EEG-vigilance state A over the 5 min recording period in line with a lowering of vigilance. The study provides evidence for a less stable regulation of EEG-vigilance in BPD compared to OCD patients and is in line with concepts postulating that the behavioural pattern with sensation seeking and impulsivity in BPD has a compensatory and autoregulatory function to stabilize activation of the CNS.

  16. Hypnagogic EEG stages and polysomnogram

    OpenAIRE

    HAYASHI, Mitsuo; HIBINO, Kenji; HORI, Tadao

    1999-01-01

    The aim of this study is to show the polysomnogram of hypnagogic period. Sixteen subjects slept for two nights. Their EEGs (Fz, Cz, Pz, Oz), horizontal and vertical EOGs, submentalis EMG, thoracic and abdominal respiration were recorded. They pressed a button when pip tones (1000Hz, 50dB, max duration : 5s, ISI : 30-90s) were presented, and reported their psychological experiences, According to Hori et al. (1994), the hypnagogic EEGs just 5s before the pip tones were classified into 9 stages,...

  17. Gender differences in association between serotonin transporter gene polymorphism and resting-state EEG activity.

    Science.gov (United States)

    Volf, N V; Belousova, L V; Knyazev, G G; Kulikov, A V

    2015-01-22

    Human brain oscillations represent important features of information processing and are highly heritable. Gender has been observed to affect association between the 5-HTTLPR (serotonin-transporter-linked polymorphic region) polymorphism and various endophenotypes. This study aimed to investigate the effects of 5-HTTLPR on the spontaneous electroencephalography (EEG) activity in healthy male and female subjects. DNA samples extracted from buccal swabs and resting EEG recorded at 60 standard leads were collected from 210 (101 men and 109 women) volunteers. Spectral EEG power estimates and cortical sources of EEG activity were investigated. It was shown that effects of 5-HTTLPR polymorphism on electrical activity of the brain vary as a function of gender. Women with the S/L genotype had greater global EEG power compared to men with the same genotype. In men, current source density was markedly different among genotype groups in only alpha 2 and alpha 3 frequency ranges: S/S allele carriers had higher current source density estimates in the left inferior parietal lobule in comparison with the L/L group. In women, genotype difference in global power asymmetry was found in the central-temporal region. Contrasting L/L and S/L genotype carriers also yielded significant effects in the right hemisphere inferior parietal lobule and the right postcentral gyrus with L/L genotype carriers showing lower current source density estimates than S/L genotype carriers in all but gamma bands. So, in women, the effects of 5-HTTLPR polymorphism were associated with modulation of the EEG activity in a wide range of EEG frequencies. The significance of the results lies in the demonstration of gene by sex interaction with resting EEG that has implications for understanding sex-related differences in affective states, emotion and cognition. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

  18. A pilot randomized controlled trial using EEG-based brain-computer interface training for a Chinese-speaking group of healthy elderly.

    Science.gov (United States)

    Lee, Tih-Shih; Quek, Shin Yi; Goh, Siau Juinn Alexa; Phillips, Rachel; Guan, Cuntai; Cheung, Yin Bun; Feng, Lei; Wang, Chuan Chu; Chin, Zheng Yang; Zhang, Haihong; Lee, Jimmy; Ng, Tze Pin; Krishnan, K Ranga Rama

    2015-01-01

    There is growing evidence that cognitive training (CT) can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG)-based brain-computer interface (BCI) CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the acceptability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly. Thirty-nine elderly participants were randomized into intervention (n=21) and wait-list control (n=18) arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), which was translated and culturally adapted for the Chinese-speaking local population. Users were asked about any adverse events experienced after each session as a safety measure. The training was deemed easily usable and acceptable by senior users. The median difference in the change scores pre- and post-training of the modified RBANS total score was 8.0 (95% confidence interval [CI]: 0.0-16.0, P=0.042) higher in the intervention arm than waitlist control, while the mean difference was 9.0 (95% CI: 1.7-16.2, P=0.017). Ten (30.3%) participants reported a total of 16 adverse events - all of which were graded "mild" except for one graded "moderate". Our BCI training system shows potential in improving cognition in both English- and Chinese-speaking elderly, and deserves further evaluation in a Phase III trial. Overall, participants

  19. Workers on transformation of the shelter object of the Chernobyl nuclear power plant into an ecologically-safe system show qEEG abnormalities and cognitive dysfunctions: A follow-up study.

    Science.gov (United States)

    Loganovsky, Konstantyn; Perchuk, Iryna; Marazziti, Donatella

    2016-12-01

    The present study aimed at assessing bioelectric activity and cognitive functions in the workers on the conversion project of the "Shelter" object (SO) of the Chernobyl nuclear power plant into an environmentally safe system. A total of 196 men were included and examined before (t0) and after (t1) working on the SO in the period 2004-2008. They underwent a qEEG and a battery of neuropsychological and psychiatric assessments. At t1, the organized type of qEEG shifted towards the disorganized one. An increase of spectral δ-power in the left frontotemporal area, of θ- and α-power in the left temporal area, with redistribution of α-activity to the front and reduction of dominant frequency in the left temporal area, were registered. Further, neurocognitive tests revealed the presence of mild cognitive disorders at t1. Interestingly, those subjects previously exposed to radiation with no consequences, were more resistant to these detrimental effects. Taken together, the disturbances observed may be considered as cognitive symptoms of a chronic fatigue syndrome resulting from the exposure to ionizing radiation. Simple and non-invasive assessments, such as those performed by us, may be helpful to detect early brain changes caused by the presence of radiological risk factors.

  20. Attention-induced deactivations in very low frequency EEG oscillations: differential localisation according to ADHD symptom status.

    Directory of Open Access Journals (Sweden)

    Samantha J Broyd

    Full Text Available BACKGROUND: The default-mode network (DMN is characterised by coherent very low frequency (VLF brain oscillations. The cognitive significance of this VLF profile remains unclear, partly because of the temporally constrained nature of the blood oxygen-level dependent (BOLD signal. Previously we have identified a VLF EEG network of scalp locations that shares many features of the DMN. Here we explore the intracranial sources of VLF EEG and examine their overlap with the DMN in adults with high and low ADHD ratings. METHODOLOGY/PRINCIPAL FINDINGS: DC-EEG was recorded using an equidistant 66 channel electrode montage in 25 adult participants with high- and 25 participants with low-ratings of ADHD symptoms during a rest condition and an attention demanding Eriksen task. VLF EEG power was calculated in the VLF band (0.02 to 0.2 Hz for the rest and task condition and compared for high and low ADHD participants. sLORETA was used to identify brain sources associated with the attention-induced deactivation of VLF EEG power, and to examine these sources in relation to ADHD symptoms. There was significant deactivation of VLF EEG power between the rest and task condition for the whole sample. Using s-LORETA the sources of this deactivation were localised to medial prefrontal regions, posterior cingulate cortex/precuneus and temporal regions. However, deactivation sources were different for high and low ADHD groups: In the low ADHD group attention-induced VLF EEG deactivation was most significant in medial prefrontal regions while for the high ADHD group this deactivation was predominantly localised to the temporal lobes. CONCLUSIONS/SIGNIFICANCE: Attention-induced VLF EEG deactivations have intracranial sources that appear to overlap with those of the DMN. Furthermore, these seem to be related to ADHD symptom status, with high ADHD adults failing to significantly deactivate medial prefrontal regions while at the same time showing significant attenuation of

  1. Polymorphism of CRISPR shows separated natural groupings of Shigella subtypes and evidence of horizontal transfer of CRISPR

    Science.gov (United States)

    Yang, Chaojie; Li, Peng; Su, Wenli; Li, Hao; Liu, Hongbo; Yang, Guang; Xie, Jing; Yi, Shengjie; Wang, Jian; Cui, Xianyan; Wu, Zhihao; Wang, Ligui; Hao, Rongzhang; Jia, Leili; Qiu, Shaofu; Song, Hongbin

    2015-01-01

    Clustered, regularly interspaced, short palindromic repeats (CRISPR) act as an adaptive RNA-mediated immune mechanism in bacteria. They can also be used for identification and evolutionary studies based on polymorphisms within the CRISPR locus. We amplified and analyzed 6 CRISPR loci from 237 Shigella strains belonging to the 4 species groups, as well as 13 Escherichia coli strains. The CRISPR-associated (cas) gene sequence arrays of these strains were screened and compared. The CRISPR sequences from Shigella were conserved among subtypes, suggesting that CRISPR may represent a new identification tool for the detection and discrimination of Shigella species. Secondary structure analysis showed a different stem-loop structure at the terminal repeat, suggesting a distinct recognition mechanism in the formation of crRNA. In addition, the presence of “self-target” spacers and polymorphisms within CRISPR in Shigella indicated a selective pressure for inhibition of this system, which has the potential to damage “self DNA.” Homology analysis of spacers showed that CRISPR might be involved in the regulation of virulence transmission. Phylogenetic analysis based on CRISPR sequences from Shigella and E. coli indicated that although phenotypic properties maintain convergent evolution, the 4 Shigella species do not represent natural groupings. Surprisingly, comparative analysis of Shigella repeats with other species provided new evidence for CRISPR horizontal transfer. Our results suggested that CRISPR analysis is applicable for the detection of Shigella species and for investigation of evolutionary relationships. PMID:26327282

  2. Polymorphism of CRISPR shows separated natural groupings of Shigella subtypes and evidence of horizontal transfer of CRISPR.

    Science.gov (United States)

    Yang, Chaojie; Li, Peng; Su, Wenli; Li, Hao; Liu, Hongbo; Yang, Guang; Xie, Jing; Yi, Shengjie; Wang, Jian; Cui, Xianyan; Wu, Zhihao; Wang, Ligui; Hao, Rongzhang; Jia, Leili; Qiu, Shaofu; Song, Hongbin

    2015-01-01

    Clustered, regularly interspaced, short palindromic repeats (CRISPR) act as an adaptive RNA-mediated immune mechanism in bacteria. They can also be used for identification and evolutionary studies based on polymorphisms within the CRISPR locus. We amplified and analyzed 6 CRISPR loci from 237 Shigella strains belonging to the 4 species groups, as well as 13 Escherichia coli strains. The CRISPR-associated (cas) gene sequence arrays of these strains were screened and compared. The CRISPR sequences from Shigella were conserved among subtypes, suggesting that CRISPR may represent a new identification tool for the detection and discrimination of Shigella species. Secondary structure analysis showed a different stem-loop structure at the terminal repeat, suggesting a distinct recognition mechanism in the formation of crRNA. In addition, the presence of "self-target" spacers and polymorphisms within CRISPR in Shigella indicated a selective pressure for inhibition of this system, which has the potential to damage "self DNA." Homology analysis of spacers showed that CRISPR might be involved in the regulation of virulence transmission. Phylogenetic analysis based on CRISPR sequences from Shigella and E. coli indicated that although phenotypic properties maintain convergent evolution, the 4 Shigella species do not represent natural groupings. Surprisingly, comparative analysis of Shigella repeats with other species provided new evidence for CRISPR horizontal transfer. Our results suggested that CRISPR analysis is applicable for the detection of Shigella species and for investigation of evolutionary relationships.

  3. Thyroid-specific questions on work ability showed known-groups validity among Danes with thyroid diseases.

    Science.gov (United States)

    Nexo, Mette Andersen; Watt, Torquil; Bonnema, Steen Joop; Hegedüs, Laszlo; Rasmussen, Åse Krogh; Feldt-Rasmussen, Ulla; Bjorner, Jakob Bue

    2015-07-01

    We aimed to identify the best approach to work ability assessment in patients with thyroid disease by evaluating the factor structure, measurement equivalence, known-groups validity, and predictive validity of a broad set of work ability items. Based on the literature and interviews with thyroid patients, 24 work ability items were selected from previous questionnaires, revised, or developed anew. Items were tested among 632 patients with thyroid disease (non-toxic goiter, toxic nodular goiter, Graves' disease (with or without orbitopathy), autoimmune hypothyroidism, and other thyroid diseases), 391 of which had participated in a study 5 years previously. Responses to select items were compared to general population data. We used confirmatory factor analyses for categorical data, logistic regression analyses and tests of differential item function, and head-to-head comparisons of relative validity in distinguishing known groups. Although all work ability items loaded on a common factor, the optimal factor solution included five factors: role physical, role emotional, thyroid-specific limitations, work limitations (without disease attribution), and work performance. The scale on thyroid-specific limitations showed the most power in distinguishing clinical groups and time since diagnosis. A global single item proved useful for comparisons with the general population, and a thyroid-specific item predicted labor market exclusion within the next 5 years (OR 5.0, 95 % CI 2.7-9.1). Items on work limitations with attribution to thyroid disease were most effective in detecting impact on work ability and showed good predictive validity. Generic work ability items remain useful for general population comparisons.

  4. 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.

  5. EEG entropy measures indicate decrease of cortical information processing in Disorders of Consciousness.

    Science.gov (United States)

    Thul, Alexander; Lechinger, Julia; Donis, Johann; Michitsch, Gabriele; Pichler, Gerald; Kochs, Eberhard F; Jordan, Denis; Ilg, Rüdiger; Schabus, Manuel

    2016-02-01

    Clinical assessments that rely on behavioral responses to differentiate Disorders of Consciousness are at times inapt because of some patients' motor disabilities. To objectify patients' conditions of reduced consciousness the present study evaluated the use of electroencephalography to measure residual brain activity. We analyzed entropy values of 18 scalp EEG channels of 15 severely brain-damaged patients with clinically diagnosed Minimally-Conscious-State (MCS) or Unresponsive-Wakefulness-Syndrome (UWS) and compared the results to a sample of 24 control subjects. Permutation entropy (PeEn) and symbolic transfer entropy (STEn), reflecting information processes in the EEG, were calculated for all subjects. Participants were tested on a modified active own-name paradigm to identify correlates of active instruction following. PeEn showed reduced local information content in the EEG in patients, that was most pronounced in UWS. STEn analysis revealed altered directed information flow in the EEG of patients, indicating impaired feed-backward connectivity. Responses to auditory stimulation yielded differences in entropy measures, indicating reduced information processing in MCS and UWS. Local EEG information content and information flow are affected in Disorders of Consciousness. This suggests local cortical information capacity and feedback information transfer as neural correlates of consciousness. The utilized EEG entropy analyses were able to relate to patient groups with different Disorders of Consciousness. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  6. 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.

  7. [EEG markers of spontaneous recovery of vertical posture in patients with consequences of severe traumatic brain injury].

    Science.gov (United States)

    Zhavoronkova, L A; Zharikova, A V; Maksakova, O A

    2014-01-01

    9 patients (mean age 23.6 +/- 3.15 y.o.) with severe traumatic brain injury (TBI) and impairment of vertical posture were included in complex clinical and EEG study during spontaneous recovery of vertical posture (VP). Patients were included in three different groups according to severity of deficit according to MPAI, FIM and MMSE scales. EEG data have been compared to those of 10 healthy volunteers (mean age 22.8 +/- 0.67 yo.). In patients with moderate brain impairment and fast recovery of VP (over 2 weeks) change of posture from sitting to standup has been accompanied by EEG-signs similar to those of healthy people. These included predominant increase of coherence in right hemisphere for majority of frequency bands, although in more complex conditions EEG of these patients showed pathological signs. In patients with more severe deficit spontaneous recovery of VP has been accompanied by "hyper-reactive" change of EEG for all frequency bands without local specificity. This finding didn't depend on side ofbrain impairment and could be considered as marker of positive dynamics of VP restoration. In patients with most severe brain impairment and deficit of functions VP didn't recover after 3 month of observation. EEG-investigation has revealed absence of reactive change of EEG during passive verticalisation. This finding can be used as marker of negative prognosis.

  8. Following Musical Shows: A Study with Focal Groups on Satisfaction of Musical Concerts Regular Visitors and Socialization between Them

    Directory of Open Access Journals (Sweden)

    Lúmia Massa Garcia Pires

    2017-06-01

    Full Text Available This article aimed to identify which attributes impact more significantly on the satisfaction of concerts’ regular visitors and socialization between them when inserted in these kinds of events. Thus, we used a qualitative methodology, performing focus groups. Among the main results of this study, we found, regarding satisfaction of concerts’ visitors, the attributes that most influence the public are related to services - especially for beverage supply, cleaning of bathrooms and lines formed inside the event - organization, show infrastructure and performance artists. Furthermore, considering the socialization of the visitors, we found that most respondents often go to concerts together with other people, but some did not exclude the possibility to attend the concerts alone when it comes to a familiar artist.

  9. Contribution of EEG in transient neurological deficits.

    Science.gov (United States)

    Lozeron, Pierre; Tcheumeni, Nadine Carole; Turki, Sahar; Amiel, Hélène; Meppiel, Elodie; Masmoudi, Sana; Roos, Caroline; Crassard, Isabelle; Plaisance, Patrick; Benbetka, Houria; Guichard, Jean-Pierre; Houdart, Emmanuel; Baudoin, Hélène; Kubis, Nathalie

    2018-01-01

    Identification of stroke mimics and 'chameleons' among transient neurological deficits (TND) is critical. Diagnostic workup consists of a brain imaging study, for a vascular disease or a brain tumour and EEG, for epileptiform discharges. The precise role of EEG in this diagnostic workup has, however, never been clearly delineated. However, this could be crucial in cases of atypical or incomplete presentation with consequences on disease management and treatment. We analysed the EEG patterns on 95 consecutive patients referred for an EEG within 7 days of a TND with diagnostic uncertainty. Patients were classified at the discharge or the 3-month follow-up visit as: 'ischemic origin', 'migraine aura', 'focal seizure', and 'other'. All patients had a brain imaging study. EEG characteristics were correlated to the TND symptoms, imaging study, and final diagnosis. Sixty four (67%) were of acute onset. Median symptom duration was 45 min. Thirty two % were 'ischemic', 14% 'migraine aura', 19% 'focal seizure', and 36% 'other' cause. EEGs were recorded with a median delay of 1.6 day after symptoms onset. Forty EEGs (42%) were abnormal. Focal slow waves were the most common finding (43%), also in the ischemic group (43%), whether patients had a typical presentation or not. Epileptiform discharges were found in three patients, one with focal seizure and two with migraine aura. Non-specific EEG focal slowing is commonly found in TND, and may last several days. We found no difference in EEG presentation between stroke mimics and stroke chameleons, and between other diagnoses.

  10. A pilot randomized controlled trial using EEG-based brain–computer interface training for a Chinese-speaking group of healthy elderly

    Directory of Open Access Journals (Sweden)

    Lee TS

    2015-01-01

    Full Text Available Tih-Shih Lee,1 Shin Yi Quek,1 Siau Juinn Alexa Goh,1 Rachel Phillips,2 Cuntai Guan,3 Yin Bun Cheung,4 Lei Feng,5 Chuan Chu Wang,3 Zheng Yang Chin,3 Haihong Zhang,3 Jimmy Lee,6 Tze Pin Ng,5 K Ranga Rama Krishnan1 1Department of Neuroscience and Behavioral Disorders, Duke-NUS Graduate Medical School, Singapore; 2Singapore Clinical Research Institute, Singapore; 3Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore; 4Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore; 5Department of Psychological Medicine, National University of Singapore, Singapore; 6Department of General Psychiatry/Department of Research, Institute of Mental Health, Singapore Background: There is growing evidence that cognitive training (CT can improve the cognitive functioning of the elderly. CT may be influenced by cultural and linguistic factors, but research examining CT programs has mostly been conducted on Western populations. We have developed an innovative electroencephalography (EEG-based brain–computer interface (BCI CT program that has shown preliminary efficacy in improving cognition in 32 healthy English-speaking elderly adults in Singapore. In this second pilot trial, we examine the acceptability, safety, and preliminary efficacy of our BCI CT program in healthy Chinese-speaking Singaporean elderly.Methods: Thirty-nine elderly participants were randomized into intervention (n=21 and waitlist control (n=18 arms. Intervention consisted of 24 half-hour sessions with our BCI-based CT training system to be completed in 8 weeks; the control arm received the same intervention after an initial 8-week waiting period. At the end of the training, a usability and acceptability questionnaire was administered. Efficacy was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS, which was translated and culturally adapted for the Chinese-speaking local population. Users were asked

  11. 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

  12. 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

  13. 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.

  14. Assessing quantitative EEG spectrograms to identify non-epileptic events.

    Science.gov (United States)

    Goenka, Ajay; Boro, Alexis; Yozawitz, Elissa

    2017-09-01

    To evaluate the sensitivity and specificity of quantitative EEG (QEEG) spectrograms in order to distinguish epileptic from non-epileptic events. Seventeen patients with paroxysmal non-epileptic events, captured during EEG monitoring, were retrospectively assessed using QEEG spectrograms. These patients were compared to a control group of 13 consecutive patients (ages 25-60 years) with epileptic seizures of similar semiology. Assessment of raw EEG was employed as the gold standard against which epileptic and non-epileptic events were validated. QEEG spectrograms, available using Persyst 12 EEG system integration software, were each assessed with respect to their usefulness to distinguish epileptic from non-epileptic seizures. The given spectrogram was interpreted as indicating a seizure if, at the time of the clinically identified event, it showed a visually significant change from baseline. Eighty-two clinically identified paroxysmal events were analysed (46 non-epileptic and 36 epileptic). The "seizure detector trend analysis" spectrogram correctly classified 33/46 (71%) non-epileptic events (no seizure indicated during a clinically identified event) vs. 29/36 (81%) epileptic seizures (seizure indicated during a clinically identified event) (p=0.013). Similarly, "rhythmicity spectrogram", FFT spectrogram, "asymmetry relative spectrogram", and integrated-amplitude EEG spectrogram detected 28/46 (61%), 30/46 (65%), 22/46 (48%) and 27/46 (59%) non-epileptic events vs. 27/36 (75%), 25/36 (69%), 25/36 (69%) and 27/36 (75%) epileptic events, respectively. High sensitivities and specificities for QEEG seizure detection analyses suggest that QEEG may have a role at the bedside to facilitate early differentiation between epileptic seizures and non-epileptic events in order to avoid unnecessary administration of antiepileptic drugs and possible iatrogenic consequences.

  15. 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

  16. Driving behavior recognition using EEG data from a simulated car-following experiment.

    Science.gov (United States)

    Yang, Liu; Ma, Rui; Zhang, H Michael; Guan, Wei; Jiang, Shixiong

    2017-11-23

    Driving behavior recognition is the foundation of driver assistance systems, with potential applications in automated driving systems. Most prevailing studies have used subjective questionnaire data and objective driving data to classify driving behaviors, while few studies have used physiological signals such as electroencephalography (EEG) to gather data. To bridge this gap, this paper proposes a two-layer learning method for driving behavior recognition using EEG data. A simulated car-following driving experiment was designed and conducted to simultaneously collect data on the driving behaviors and EEG data of drivers. The proposed learning method consists of two layers. In Layer I, two-dimensional driving behavior features representing driving style and stability were selected and extracted from raw driving behavior data using K-means and support vector machine recursive feature elimination. Five groups of driving behaviors were classified based on these two-dimensional driving behavior features. In Layer II, the classification results from Layer I were utilized as inputs to generate a k-Nearest-Neighbor classifier identifying driving behavior groups using EEG data. Using independent component analysis, a fast Fourier transformation, and linear discriminant analysis sequentially, the raw EEG signals were processed to extract two core EEG features. Classifier performance was enhanced using the adaptive synthetic sampling approach. A leave-one-subject-out cross validation was conducted. The results showed that the average classification accuracy for all tested traffic states was 69.5% and the highest accuracy reached 83.5%, suggesting a significant correlation between EEG patterns and car-following behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Group Cognitive Behavioural Therapy Program Shows Potential in Reducing Symptoms of Depression and Stress among Young People with ASD

    Science.gov (United States)

    McGillivray, J. A.; Evert, H. T.

    2014-01-01

    We examined the efficacy of cognitive behavioural therapy (CBT) delivered in groups on the reduction of symptoms of depression, anxiety and stress in young people on the autism spectrum. Utilising a quasi-experimental design, comparisons were made between individuals allocated to a group intervention program and individuals allocated to a…

  18. Calibrating EEG-based motor imagery brain-computer interface from passive movement.

    Science.gov (United States)

    Ang, Kai Keng; Guan, Cuntai; Wang, Chuanchu; Phua, Kok Soon; Tan, Adrian Hock Guan; Chin, Zheng Yang

    2011-01-01

    EEG data from performing motor imagery are usually collected to calibrate a subject-specific model for classifying the EEG data during the evaluation phase of motor imagery Brain-Computer Interface (BCI). However, there is no direct objective measure to determine if a subject is performing motor imagery correctly for proper calibration. Studies have shown that passive movement, which is directly observable, induces Event-Related Synchronization patterns that are similar to those induced from motor imagery. Hence, this paper investigates the feasibility of calibrating EEG-based motor imagery BCI from passive movement. EEG data of 12 healthy subjects were collected during motor imagery and passive movement of the hand by a haptic knob robot. The calibration models using the Filter Bank Common Spatial Pattern algorithm on the EEG data from motor imagery were compared against using the EEG data from passive movement. The performances were compared based on the 10×10-fold cross-validation accuracies of the calibration data, and off-line session-to-session transfer kappa values to other sessions of motor imagery performed on another day. The results showed that the calibration performed using passive movement yielded higher model accuracy and off-line session-to-session transfer (73.6% and 0.354) than the calibration performed using motor imagery (71.3% and 0.311), and no significant differences were observed between the two groups (p=0.20, 0.23). Hence, this study shows that it is feasible to calibrate EEG-based motor imagery BCI from passive movement.

  19. The EEG 2017 in the overview

    International Nuclear Information System (INIS)

    Altrock, Martin; Vollprecht, Jens

    2016-01-01

    On 08.07.2016, the German Bundestag, the German Renewable Energies Act (EEG) in 2017 passed together with the wind-at-sea law. At the same time, the legislature changed 22 other energy legislation, inter alia, also the EnWG. Here, the law de facto a law amending the EEG 2014 is: The EEG is thus not total re-promulgated. Rather essentially part 3 (''Payment of market premium and feed in rate'') of the EEG 2014 renewed, notably Section 3 supplemented by regulations on the newly introduced procurements. But beyond the framework of support is further developed in various details, like the definition of a plant, the promotion of storage facilities and of course, in the again very ambitious and complicated transitional arrangements. Other notable individual changes concern the introduction of regional evidence of directly marketed electricity from renewable sources, the increase of liability for balancing group deviations in paragraph 60 para. 1 EEG 2017 or readjustments in the special equalization scheme, paragraph 64 para. 2 no. 2 EEG. [de

  20. Prognostic and diagnostic value of EEG signal coupling measures in coma.

    Science.gov (United States)

    Zubler, Frederic; Koenig, Christa; Steimer, Andreas; Jakob, Stephan M; Schindler, Kaspar A; Gast, Heidemarie

    2016-08-01

    Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  1. QUANTITATIVE EEG COMPARATIVE ANALYSIS BETWEEN AUTISM SPECTRUM DISORDER (ASD AND ATTENTION DEFICIT HYPERACTIVITY DISORDER (ADHD

    Directory of Open Access Journals (Sweden)

    Plamen D. Dimitrov

    2017-01-01

    Full Text Available Background: Autism is a mental developmental disorder, manifested in the early childhood. Attention deficit hyperactivity disorder is another psychiatric condition of the neurodevelopmental type. Both disorders affect information processing in the nervous system, altering the mechanisms which control how neurons and their synapses are connected and organized. Purpose: To examine if quantitative EEG assessment is sensitive and simple enough to differentiate autism from attention deficit hyperactivity disorder and neurologically typical children. Material and methods: Quantitative EEG is a type of electrophysiological assessment that uses computerized mathematical analysis to convert the raw waveform data into different frequency ranges. Each frequency range is averaged across a sample of data and quantified into mean amplitude (voltage in microvolts mV. We performed quantitative EEG analysis and compared 4 cohorts of children (aged from 3 to 7 years: with autism (high [n=27] and low [n=52] functioning, with attention deficit hyperactivity disorder [n=34], and with typical behavior [n75]. Results: Our preliminary results show that there are significant qEEG differences between the groups of patients and the control cohort. The changes affect the potential levels of delta-, theta-, alpha-, and beta- frequency spectrums. Conclusion: The present study shows some significant quantitative EEG findings in autistic patients. This is a step forward in our efforts, aimed at defining specific neurophysiologic changes, in order to develop and refine strategies for early diagnosis of autism spectrum disorders, differentiation from other development conditions in childhood, detection of specific biomarkers and early initiation of treatment.

  2. Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis.

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    Francisco J Fraga

    Full Text Available Changes in electroencephalography (EEG amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD. Existing tools available to perform such analysis (e.g., detrended fluctuation analysis, however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD showed significant differences in amplitude modulations between the three groups. Most notably, i delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD, ii delta modulation of the theta band appeared with an increase in severity, and iii delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.

  3. One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

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    Yehudit Meir-Hasson

    Full Text Available Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

  4. Continuous EEG-SEP monitoring in severe brain injury.

    Science.gov (United States)

    Amantini, A; Fossi, S; Grippo, A; Innocenti, P; Amadori, A; Bucciardini, L; Cossu, C; Nardini, C; Scarpelli, S; Roma, V; Pinto, F

    2009-04-01

    To monitor acute brain injury in the neurological intensive care unit (NICU), we used EEG and somatosensory evoked potentials (SEP) in combination to achieve more accuracy in detecting brain function deterioration. Sixty-eight patients (head trauma and intracranial hemorrhage; GCSSEP and intracranial pressure monitoring (ICP). Fifty-five patients were considered "stable" or improving, considering the GCS and CT scan: in this group, SEP didn't show significant changes. Thirteen patients showed neurological deteriorations and, in all patients, cortical SEP showed significant alterations (amplitude decrease>50% often till complete disappearance). SEP deterioration anticipated ICP increase in 30%, was contemporary in 38%, and followed ICP increase in 23%. Considering SEP and ICP in relation to clinical course, all patients but one with ICP less than 20 mmHg were stable, while the three patients with ICP greater than 40 mmHg all died. Among the 26 patients with ICP of 20-40 mmHg, 17 were stable, while nine showed clinical and neurophysiological deterioration. Thus, there is a range of ICP values (20-40 mmHg) were ICP is scarcely indicative of clinical deterioration, rather it is the SEP changes that identify brain function deterioration. Therefore, SEP have a twofold interest with respect to ICP: their changes can precede an ICP increase and they can constitute a complementary tool to interpret ICP trends. It has been very important to associate SEP and EEG: about 60% of our patients were deeply sedated and, because of their relative insensitivity to anesthetics, only SEP allowed us to monitor brain damage evolution when EEG was scarcely valuable. We observed 3% of nonconvulsive status epilepticus compared to 18% of neurological deterioration. If the aim of neurophysiological monitoring is to "detect and protect", it may not be limited to detecting seizures, rather it should be able to identify brain deterioration, so we propose the combined monitoring of EEG with SEP.

  5. Data-driven forward model inference for EEG brain imaging

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hauberg, Søren; Hansen, Lars Kai

    2016-01-01

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain......-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models....... Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging...

  6. 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.

  7. 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.

  8. 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.

  9. Usefulness of a simple sleep-deprived EEG protocol for epilepsy diagnosis in de novo subjects.

    Science.gov (United States)

    Giorgi, Filippo S; Perini, Daria; Maestri, Michelangelo; Guida, Melania; Pizzanelli, Chiara; Caserta, Anna; Iudice, Alfonso; Bonanni, Enrica

    2013-11-01

    In case series concerning the role of EEG after sleep deprivation (SD-EEG) in epilepsy, patients' features and protocols vary dramatically from one report to another. In this study, we assessed the usefulness of a simple SD-EEG method in well characterized patients. Among the 963 adult subjects submitted to SD-EEG at our Center, in the period 2003-2010, we retrospectively selected for analysis only those: (1) evaluated for suspected epileptic seizures; (2) with a normal/non-specific baseline EEG; (3) still drug-free at the time of SD-EEG; (4) with an MRI analysis; (5) with at least 1 year follow-up. SD-EEG consisted in SD from 2:00 AM and laboratory EEG from 8:00 AM to 10:30 AM. We analyzed epileptic interictal abnormalities (IIAs) and their correlations with patients' features. Epilepsy was confirmed in 131 patients. SD-EEG showed IIAs in 41.2% of all patients with epilepsy, and a 91.1% specificity for epilepsy diagnosis; IIAs types observed during SD-EEG are different in generalized versus focal epilepsies; for focal epilepsies, the IIAs yield in SD-EEG is higher than in second routine EEG. This simple SD-EEG protocol is very useful in de novo patients with suspected seizures. This study sheds new light on the role of SD-EEG in specific epilepsy populations. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  10. Transfer function between EEG and BOLD signals of epileptic activity

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    Marco eLeite

    2013-01-01

    Full Text Available Simultaneous EEG-fMRI recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a heamodynamic response function to model the associated BOLD changes. Although more flexible approaches may allow a higher degree of complexity for the haemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis (ICA of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.

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

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    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.

  12. Parametric and Nonparametric EEG Analysis for the Evaluation of EEG Activity in Young Children with Controlled Epilepsy

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    Vangelis Sakkalis

    2008-01-01

    Full Text Available There is an important evidence of differences in the EEG frequency spectrum of control subjects as compared to epileptic subjects. In particular, the study of children presents difficulties due to the early stages of brain development and the various forms of epilepsy indications. In this study, we consider children that developed epileptic crises in the past but without any other clinical, psychological, or visible neurophysiological findings. The aim of the paper is to develop reliable techniques for testing if such controlled epilepsy induces related spectral differences in the EEG. Spectral features extracted by using nonparametric, signal representation techniques (Fourier and wavelet transform and a parametric, signal modeling technique (ARMA are compared and their effect on the classification of the two groups is analyzed. The subjects performed two different tasks: a control (rest task and a relatively difficult math task. The results show that spectral features extracted by modeling the EEG signals recorded from individual channels by an ARMA model give a higher discrimination between the two subject groups for the control task, where classification scores of up to 100% were obtained with a linear discriminant classifier.

  13. 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.

  14. EEG indices correlate with sustained attention performance in patients affected by diffuse axonal injury.

    Science.gov (United States)

    Coelli, Stefania; Barbieri, Riccardo; Reni, Gianluigi; Zucca, Claudio; Bianchi, Anna Maria

    2018-06-01

    The aim of this study is to assess the ability of EEG-based indices in providing relevant information about cognitive engagement level during the execution of a clinical sustained attention (SA) test in healthy volunteers and DAI (diffused axonal injury)-affected patients. We computed three continuous power-based engagement indices (P β /P α , 1/P α , and P β / (P α + P θ )) from EEG recordings in a control group (n = 7) and seven DAI-affected patients executing a 10-min Conners' "not-X" continuous performance test (CPT). A correlation analysis was performed in order to investigate the existence of relations between the EEG metrics and behavioral parameters in both the populations. P β /P α and 1/P α indices were found to be correlated with reaction times in both groups while P β / (P α + P θ ) and P β /P α also correlated with the errors rate for DAI patients. In line with previous studies, time course fluctuations revealed a first strong decrease of attention after 2 min from the beginning of the test and a final fading at the end. Our results provide evidence that EEG-derived indices extraction and evaluation during SA tasks are helpful in the assessment of attention level in healthy subjects and DAI patients, offering motivations for including EEG monitoring in cognitive rehabilitation practice. Graphical abstract Three EEG-derived indices were computed from four electrodes montages in a population of seven healthy volunteers and a group of seven DAI-affected patients. Results show a significant correlation between the time course of the indices and behavioral parameters, thus demonstrating their usefulness in monitoring mental engagement level during a sustained attention task.

  15. Source-Modeling Auditory Processes of EEG Data Using EEGLAB and Brainstorm

    Directory of Open Access Journals (Sweden)

    Maren Stropahl

    2018-05-01

    physiologically plausible EEG source estimates. Finally, we show how to perform group level analysis in the time domain on anatomically defined regions of interest (auditory scout. The proposed pipeline needs to be tailored to the specific datasets and paradigms. However, the straightforward combination of EEGLAB and Brainstorm analysis tools may be of interest to others performing EEG source localization.

  16. Standardized Computer-based Organized Reporting of EEG: SCORE

    Science.gov (United States)

    Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C; Fuglsang-Frederiksen, Anders; Martins-da-Silva, António; Trinka, Eugen; Visser, Gerhard; Rubboli, Guido; Hjalgrim, Helle; Stefan, Hermann; Rosén, Ingmar; Zarubova, Jana; Dobesberger, Judith; Alving, Jørgen; Andersen, Kjeld V; Fabricius, Martin; Atkins, Mary D; Neufeld, Miri; Plouin, Perrine; Marusic, Petr; Pressler, Ronit; Mameniskiene, Ruta; Hopfengärtner, Rüdiger; Emde Boas, Walter; Wolf, Peter

    2013-01-01

    The electroencephalography (EEG) signal has a high complexity, and the process of extracting clinically relevant features is achieved by visual analysis of the recordings. The interobserver agreement in EEG interpretation is only moderate. This is partly due to the method of reporting the findings in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings). A working group of EEG experts took part in consensus workshops in Dianalund, Denmark, in 2010 and 2011. The faculty was approved by the Commission on European Affairs of the International League Against Epilepsy (ILAE). The working group produced a consensus proposal that went through a pan-European review process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice. The main elements of SCORE are the following: personal data of the patient, referral data, recording conditions, modulators, background activity, drowsiness and sleep, interictal findings, “episodes” (clinical or subclinical events), physiologic patterns, patterns of uncertain significance, artifacts, polygraphic channels, and diagnostic significance. The following specific aspects of the neonatal EEGs are scored: alertness, temporal organization, and spatial organization. For each EEG finding, relevant features are scored using predefined terms. Definitions are provided for all EEG terms and features. SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make

  17. How Long Should Routine EEG Be Recorded to Get Relevant Information?

    Science.gov (United States)

    Doudoux, Hannah; Skaare, Kristina; Geay, Thomas; Kahane, Philippe; Bosson, Jean L; Sabourdy, Cécile; Vercueil, Laurent

    2017-03-01

    The optimal duration of routine EEG (rEEG) has not been determined on a clinical basis. This study aims to determine the time required to obtain relevant information during rEEG with respect to the clinical request. All rEEGs performed over 3 months in unselected patients older than 14 years in an academic hospital were analyzed retrospectively. The latency required to obtain relevant information was determined for each rEEG by 2 independent readers blinded to the clinical data. EEG final diagnoses and latencies were analyzed with respect to the main clinical requests: subacute cognitive impairment, spells, transient focal neurologic manifestation or patients referred by epileptologists. From 430 rEEGs performed in the targeted period, 364 were analyzed: 92% of the pathological rEEGs were provided within the first 10 minutes of recording. Slowing background activity was diagnosed from the beginning, whereas interictal epileptiform discharges were recorded over time. Moreover, the time elapsed to demonstrate a pattern differed significantly in the clinical groups: in patients with subacute cognitive impairment, EEG abnormalities appeared within the first 10 minutes, whereas in the other groups, data could be provided over time. Patients with subacute cognitive impairment differed from those in the other groups significantly in the elapsed time required to obtain relevant information during rEEG, suggesting that 10-minute EEG recordings could be sufficient, arguing in favor of individualized rEEG. However, this conclusion does not apply to intensive care unit patients.

  18. EEG Correlates of Ten Positive Emotions.

    Science.gov (United States)

    Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan

    2017-01-01

    Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants' ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as 'encouragement' for awe, gratitude, hope, inspiration, pride, 'playfulness' for amusement, joy, interest, and 'harmony' for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.

  19. Drug Treated Schizophrenia, Schizoaffective and Bipolar Disorder Patients Evaluated by qEEG Absolute Spectral Power and Mean Frequency Analysis.

    Science.gov (United States)

    Wix-Ramos, Richard; Moreno, Xiomara; Capote, Eduardo; González, Gilbert; Uribe, Ezequiel; Eblen-Zajjur, Antonio

    2014-04-01

    Research of electroencephalograph (EEG) power spectrum and mean frequency has shown inconsistent results in patients with schizophrenic, schizoaffective and bipolar disorders during medication when compared to normal subjects thus; the characterization of these parameters is an important task. We applied quantitative EEG (qEEG) to investigate 38 control, 15 schizophrenic, 7 schizoaffective and 11 bipolar disorder subjects which remaine under the administration of psychotropic drugs (except control group). Absolute spectral power (ASP), mean frequency and hemispheric electrical asymmetry were measured by 19 derivation qEEG. Group mean values were compared with non parametrical Mann-Whitney test and spectral EEG maps with z-score method at p Schizoaffective patients received neuroleptic+benzodiazepine (71.4%) and for bipolar disorder patients neuroleptic+antiepileptic (81.8%). Schizophrenic (at all derivations except for Fp1, Fp2, F8 and T6) and schizoaffective (only at C3) show higher values of ASP (+57.7% and +86.1% respectively) compared to control group. ASP of bipolar disorder patients did not show differences against control group. The mean frequency was higher at Fp1 (+14.2%) and Fp2 (+17.4%) in bipolar disorder patients than control group, but no differences were found in frequencies between schizophrenic or schizoaffective patients against the control group. Majority of spectral differences were found at the left hemisphere in schizophrenic and schizoaffective but not in bipolar disorder subjects. The present report contributes to characterize quantitatively the qEEG in drug treated schizophrenic, schizoaffective or bipolar disorder patients.

  20. 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.

  1. Behavioural Inhibition System (BIS) sensitivity differentiates EEG theta responses during goal conflict in a continuous monitoring task.

    Science.gov (United States)

    Moore, Roger A; Mills, Matthew; Marshman, Paul; Corr, Philip J

    2012-08-01

    Previous research has revealed that EEG theta oscillations are affected during goal conflict processing. This is consistent with the behavioural inhibition system (BIS) theory of anxiety (Gray & McNaughton, 2000). However, studies have not attempted to relate these BIS-related theta effects to BIS personality measures. Confirmation of such an association would provide further support for BIS theory, especially as it relates to trait differences. EEG was measured (32 electrodes) from extreme groups (low/high trait BIS) engaged in a target detection task. Goal conflicts were introduced throughout the task. Results show that the two groups did not differ in behavioural performance. The major EEG result was that a stepwise discriminant analysis indicated discrimination by 6 variables derived from coherence and power, with 5 of the 6 in the theta range as predicted by BIS theory and one in the beta range. Also, across the whole sample, EEG theta coherence increased at a variety of regions during primary goal conflict and showed a general increase during response execution; EEG theta power, in contrast, was primarily reactive to response execution. This is the first study to reveal a three-way relationship between the induction of goal conflict, the induction of theta power and coherence, and differentiation by psychometrically-defined low/high BIS status. Copyright © 2012 Elsevier B.V. All rights reserved.

  2. 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.

  3. 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.

  4. Clinical and EEG features of ischemic stroke patients with abnormal discharges

    Directory of Open Access Journals (Sweden)

    Jia-lei YANG

    2016-05-01

    Full Text Available Objective To investigate the clinical and EEG features of ischemic stroke patients with abnormal discharges.  Methods Clinical data and 24-hour EEG monitoring of 162 ischemic stroke patients were analyzed retrospectively. One-year follow-up was carried out and post-ischemic epilepsy was diagnosed.  Results Among 162 ischemic stroke patients, 24-hour EEG was abnormal in 87 cases (53.70%. According to the correspondence of site of infarcts and abnormal discharges, these 87 cases were classified into 2 groups: matched group (N = 24, 27.59% and unmatched group (N = 63, 72.41%. There was no significant difference between 2 groups in terms of Oxfordshire Community Stroke Project (OCSP and TOAST classification (P = 0.792, 0.111, while there was significant difference between 2 groups on the site of infarcts (P = 0.000. In matched group, the infarcts were mainly located in cortex (N = 23, 95.83%. However, in unmatched group, the infarcts were mainly located in cortex and basal ganglia (N = 27, 42.86%, or in basal ganglia only (N = 24, 38.10%. In matched group, 24-hour EEG showed slowing of background activities, and sharp waves and sharp and slow wave complex which were corresponding to the infarct sites. The abnormal discharges could only be recorded around the infarct unilaterally. In unmatched group, the epileptiform discharges were recorded in both contralateral and ipsilateral ischemic hemispheres, usually with widespread slow waves and asymmetric background. The infarcts were limited, but abnormal discharges were widespread. For example, the infarct was located in deep brain, while scalp abnormal discharges were recorded. Although there was no significant difference in terms of epilepsy incidence between 2 groups (P = 0.908, the types of epilepsy were statistically different between 2 groups (P = 0.000. In matched group, the main type was partial seizure. But in unmatched group, the main types of epilepsy were secondary generalized seizure and

  5. Portable wireless neurofeedback system of EEG alpha rhythm enhances memory.

    Science.gov (United States)

    Wei, Ting-Ying; Chang, Da-Wei; Liu, You-De; Liu, Chen-Wei; Young, Chung-Ping; Liang, Sheng-Fu; Shaw, Fu-Zen

    2017-11-13

    Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

  6. 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.

  7. Analysis of absence seizure generation using EEG spatial-temporal regularity measures.

    Science.gov (United States)

    Mammone, Nadia; Labate, Domenico; Lay-Ekuakille, Aime; Morabito, Francesco C

    2012-12-01

    Epileptic seizures are thought to be generated and to evolve through an underlying anomaly of synchronization in the activity of groups of neuronal populations. The related dynamic scenario of state transitions is revealed by detecting changes in the dynamical properties of Electroencephalography (EEG) signals. The recruitment procedure ending with the crisis can be explored through a spatial-temporal plot from which to extract suitable descriptors that are able to monitor and quantify the evolving synchronization level from the EEG tracings. In this paper, a spatial-temporal analysis of EEG recordings based on the concept of permutation entropy (PE) is proposed. The performance of PE are tested on a database of 24 patients affected by absence (generalized) seizures. The results achieved are compared to the dynamical behavior of the EEG of 40 healthy subjects. Being PE a feature which is dependent on two parameters, an extensive study of the sensitivity of the performance of PE with respect to the parameters' setting was carried out on scalp EEG. Once the optimal PE configuration was determined, its ability to detect the different brain states was evaluated. According to the results here presented, it seems that the widely accepted model of "jump" transition to absence seizure should be in some cases coupled (or substituted) by a gradual transition model characteristic of self-organizing networks. Indeed, it appears that the transition to the epileptic status is heralded before the preictal state, ever since the interictal stages. As a matter of fact, within the limits of the analyzed database, the frontal-temporal scalp areas appear constantly associated to PE levels higher compared to the remaining electrodes, whereas the parieto-occipital areas appear associated to lower PE values. The EEG of healthy subjects neither shows any similar dynamic behavior nor exhibits any recurrent portrait in PE topography.

  8. Widespread EEG changes precede focal seizures.

    Directory of Open Access Journals (Sweden)

    Piero Perucca

    Full Text Available The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal, and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline. Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma and high-frequency bands (ripples and fast ripples. At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development, but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.

  9. EEG entropy measures in anesthesia

    Directory of Open Access Journals (Sweden)

    Zhenhu eLiang

    2015-02-01

    Full Text Available Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA and detecting the burst suppression pattern (BSP, in anesthesia induced by GA-BAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE and State entropy (SE, three wavelet entropy (WE measures (Shannon WE (SWE, Tsallis WE (TWE and Renyi WE (RWE, Hilbert-Huang spectral entropy (HHSE, approximate entropy (ApEn, sample entropy (SampEn, Fuzzy entropy, and three permutation entropy (PE measures (Shannon PE (SPE, Tsallis PE (TPE and Renyi PE (RPE. Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure.Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

  10. The urban brain: analysing outdoor physical activity with mobile EEG.

    Science.gov (United States)

    Aspinall, Peter; Mavros, Panagiotis; Coyne, Richard; Roe, Jenny

    2015-02-01

    Researchers in environmental psychology, health studies and urban design are interested in the relationship between the environment, behaviour settings and emotions. In particular, happiness, or the presence of positive emotional mindsets, broadens an individual's thought-action repertoire with positive benefits to physical and intellectual activities, and to social and psychological resources. This occurs through play, exploration or similar activities. In addition, a body of restorative literature focuses on the potential benefits to emotional recovery from stress offered by green space and 'soft fascination'. However, access to the cortical correlates of emotional states of a person actively engaged within an environment has not been possible until recently. This study investigates the use of mobile electroencephalography (EEG) as a method to record and analyse the emotional experience of a group of walkers in three types of urban environment including a green space setting. Using Emotiv EPOC, a low-cost mobile EEG recorder, participants took part in a 25 min walk through three different areas of Edinburgh. The areas (of approximately equal length) were labelled zone 1 (urban shopping street), zone 2 (path through green space) and zone 3 (street in a busy commercial district). The equipment provided continuous recordings from five channels, labelled excitement (short-term), frustration, engagement, long-term excitement (or arousal) and meditation. A new form of high-dimensional correlated component logistic regression analysis showed evidence of lower frustration, engagement and arousal, and higher meditation when moving into the green space zone; and higher engagement when moving out of it. Systematic differences in EEG recordings were found between three urban areas in line with restoration theory. This has implications for promoting urban green space as a mood-enhancing environment for walking or for other forms of physical or reflective activity. Published

  11. Efficacy of Alfa EEG wave biofeedback in the management of anxiety

    Directory of Open Access Journals (Sweden)

    Pookala Bhat

    2010-01-01

    Full Text Available Background: Biofeedback is a technique in which people are trained to improve their health by learning to control certain internal bodily processes that normally occur involuntarily. Various studies in the past have shown usefulness of Alfa electroencephalographic (EEG biofeedback in the alleviation of anxiety symptoms. Though most of the psychiatric centers in the armed forces have this facility, not much work has been done in our setup to assess its efficacy in the management of anxiety. Hence this study was undertaken. Materials and Methods: This study was carried out in a multispecialty Command Hospital by enrolling 100 patients with psychiatric diagnosis from both inpatient and outpatient services. The anxiety level was assessed clinically and by using Hamilton Anxiety Scale and Taylor′s Manifest Anxiety Scale. One group of 50 patients was treated with Alfa EEG biofeedback sessions only, 5 times in a week for 8 weeks, along with specific pharmacotherapy. The other group was treated with appropriate dose of anxiolytics. The anxiety level was reassessed after 4 weeks and 8 weeks. Results: The response was better for mixed anxiety and depressive disorder with pharmacotherapy than with the biofeedback, but female patients showed better response with EEG biofeedback. Conclusion: In the short term, Alfa EEG biofeedback therapy is almost as efficacious as pharmacological intervention in the management of anxiety symptoms, and relatively more useful in females.

  12. 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.

  13. Discriminant Multitaper Component Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Sajda, Paul

    the method for predicting the handedness of a subject’s button press given multivariate EEG data. We show that our method learns multitapers sensitive to oscillatory activity in the 8–12 Hz range with spatial filters selective for lateralized motor cortex. This finding is consistent with the well-known mu...

  14. EEG entropy measures in anesthesia

    Science.gov (United States)

    Liang, Zhenhu; Wang, Yinghua; Sun, Xue; Li, Duan; Voss, Logan J.; Sleigh, Jamie W.; Hagihira, Satoshi; Li, Xiaoli

    2015-01-01

    Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression. Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents. Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared. Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation

  15. 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. ...

  16. 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.

  17. 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.

  18. Causality within the Epileptic Network: An EEG-fMRI Study Validated by Intracranial EEG.

    Science.gov (United States)

    Vaudano, Anna Elisabetta; Avanzini, Pietro; Tassi, Laura; Ruggieri, Andrea; Cantalupo, Gaetano; Benuzzi, Francesca; Nichelli, Paolo; Lemieux, Louis; Meletti, Stefano

    2013-01-01

    Accurate localization of the Seizure Onset Zone (SOZ) is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI) has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical work-up. However, fMRI maps related to interictal epileptiform activities (IED) often show multiple regions of signal change, or "networks," rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modeling (DCM) applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here, we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of blood oxygenation level dependent (BOLD) signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favored a model corresponding to the left dorso-lateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a) the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI), and "psycho-physiological interaction" analysis; (b) the failure of a first surgical intervention limited to the fronto-polar region; (c) the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

  19. Causality within the epileptic network: an EEG-fMRI study validated by intracranial EEG.

    Directory of Open Access Journals (Sweden)

    Anna Elisabetta eVaudano

    2013-11-01

    Full Text Available Accurate localization of the Seizure Onset Zone (SOZ is crucial in patients with drug-resistance focal epilepsy. EEG with fMRI recording (EEG-fMRI has been proposed as a complementary non-invasive tool, which can give useful additional information in the pre-surgical workup. However, fMRI maps related to interictal epileptiform activities (IED often show multiple regions of signal change, or networks, rather than highly focal ones. Effective connectivity approaches like Dynamic Causal Modelling (DCM applied to fMRI data potentially offers a framework to address which brain regions drives the generation of seizures and IED within an epileptic network. Here we present a first attempt to validate DCM on EEG-fMRI data in one patient affected by frontal lobe epilepsy. Pre-surgical EEG-fMRI demonstrated two distinct clusters of BOLD signal increases linked to IED, one located in the left frontal pole and the other in the ipsilateral dorso-lateral frontal cortex. DCM of the IED-related BOLD signal favoured a model corresponding to the left dorsolateral frontal cortex as driver of changes in the fronto-polar region. The validity of DCM was supported by: (a the results of two different non-invasive analysis obtained on the same dataset: EEG source imaging (ESI, and psychophysiological interaction analysis (PPI; (b the failure of a first surgical intervention limited to the fronto-polar region; (c the results of the intracranial EEG monitoring performed after the first surgical intervention confirming a SOZ located over the dorso-lateral frontal cortex. These results add evidence that EEG-fMRI together with advanced methods of BOLD signal analysis is a promising tool that can give relevant information within the epilepsy surgery diagnostic work-up.

  20. 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.

  1. EEG source imaging during two Qigong meditations.

    Science.gov (United States)

    Faber, Pascal L; Lehmann, Dietrich; Tei, Shisei; Tsujiuchi, Takuya; Kumano, Hiroaki; Pascual-Marqui, Roberto D; Kochi, Kieko

    2012-08-01

    Experienced Qigong meditators who regularly perform the exercises "Thinking of Nothing" and "Qigong" were studied with multichannel EEG source imaging during their meditations. The intracerebral localization of brain electric activity during the two meditation conditions was compared using sLORETA functional EEG tomography. Differences between conditions were assessed using t statistics (corrected for multiple testing) on the normalized and log-transformed current density values of the sLORETA images. In the EEG alpha-2 frequency, 125 voxels differed significantly; all were more active during "Qigong" than "Thinking of Nothing," forming a single cluster in parietal Brodmann areas 5, 7, 31, and 40, all in the right hemisphere. In the EEG beta-1 frequency, 37 voxels differed significantly; all were more active during "Thinking of Nothing" than "Qigong," forming a single cluster in prefrontal Brodmann areas 6, 8, and 9, all in the left hemisphere. Compared to combined initial-final no-task resting, "Qigong" showed activation in posterior areas whereas "Thinking of Nothing" showed activation in anterior areas. The stronger activity of posterior (right) parietal areas during "Qigong" and anterior (left) prefrontal areas during "Thinking of Nothing" may reflect a predominance of self-reference, attention and input-centered processing in the "Qigong" meditation, and of control-centered processing in the "Thinking of Nothing" meditation.

  2. Standardized computer-based organized reporting of EEG

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C.

    2017-01-01

    Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the se......Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted...... in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE....... In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE...

  3. Donepezil impairs memory in healthy older subjects: behavioural, EEG and simultaneous EEG/fMRI biomarkers.

    Directory of Open Access Journals (Sweden)

    Joshua H Balsters

    Full Text Available Rising life expectancies coupled with an increasing awareness of age-related cognitive decline have led to the unwarranted use of psychopharmaceuticals, including acetylcholinesterase inhibitors (AChEIs, by significant numbers of healthy older individuals. This trend has developed despite very limited data regarding the effectiveness of such drugs on non-clinical groups and recent work indicates that AChEIs can have negative cognitive effects in healthy populations. For the first time, we use a combination of EEG and simultaneous EEG/fMRI to examine the effects of a commonly prescribed AChEI (donepezil on cognition in healthy older participants. The short- and long-term impact of donepezil was assessed using two double-blind, placebo-controlled trials. In both cases, we utilised cognitive (paired associates learning (CPAL and electrophysiological measures (resting EEG power that have demonstrated high-sensitivity to age-related cognitive decline. Experiment 1 tested the effects of 5 mg/per day dosage on cognitive and EEG markers at 6-hour, 2-week and 4-week follow-ups. In experiment 2, the same markers were further scrutinised using simultaneous EEG/fMRI after a single 5 mg dose. Experiment 1 found significant negative effects of donepezil on CPAL and resting Alpha and Beta band power. Experiment 2 replicated these results and found additional drug-related increases in the Delta band. EEG/fMRI analyses revealed that these oscillatory differences were associated with activity differences in the left hippocampus (Delta, right frontal-parietal network (Alpha, and default-mode network (Beta. We demonstrate the utility of simple cognitive and EEG measures in evaluating drug responses after acute and chronic donepezil administration. The presentation of previously established markers of age-related cognitive decline indicates that AChEIs can impair cognitive function in healthy older individuals. To our knowledge this is the first study to identify

  4. Idiopathic normal pressure hydrocephalus, quantitative EEG findings, and the cerebrospinal fluid tap test: a pilot study.

    Science.gov (United States)

    Seo, Jong-Geun; Kang, Kyunghun; Jung, Ji-Young; Park, Sung-Pa; Lee, Maan-Gee; Lee, Ho-Won

    2014-12-01

    In this pilot study, we analyzed relationships between quantitative EEG measurements and clinical parameters in idiopathic normal pressure hydrocephalus patients, along with differences in these quantitative EEG markers between cerebrospinal fluid tap test responders and nonresponders. Twenty-six idiopathic normal pressure hydrocephalus patients (9 cerebrospinal fluid tap test responders and 17 cerebrospinal fluid tap test nonresponders) constituted the final group for analysis. The resting EEG was recorded and relative powers were computed for seven frequency bands. Cerebrospinal fluid tap test nonresponders, when compared with responders, showed a statistically significant increase in alpha2 band power at the right frontal and centrotemporal regions. Higher delta2 band powers in the frontal, central, parietal, and occipital regions and lower alpha1 band powers in the right temporal region significantly correlated with poorer cognitive performance. Higher theta1 band powers in the left parietal and occipital regions significantly correlated with gait dysfunction. And higher delta1 band powers in the right frontal regions significantly correlated with urinary disturbance. Our findings may encourage further research using quantitative EEG in patients with ventriculomegaly as a potential electrophysiological marker for predicting cerebrospinal fluid tap test responders. This study additionally suggests that the delta, theta, and alpha bands are statistically correlated with the severity of symptoms in idiopathic normal pressure hydrocephalus patients.

  5. Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from EEG signal.

    Science.gov (United States)

    Hosseinifard, Behshad; Moradi, Mohammad Hassan; Rostami, Reza

    2013-03-01

    Diagnosing depression in the early curable stages is very important and may even save the life of a patient. In this paper, we study nonlinear analysis of EEG signal for discriminating depression patients and normal controls. Forty-five unmedicated depressed patients and 45 normal subjects were participated in this study. Power of four EEG bands and four nonlinear features including detrended fluctuation analysis (DFA), higuchi fractal, correlation dimension and lyapunov exponent were extracted from EEG signal. For discriminating the two groups, k-nearest neighbor, linear discriminant analysis and logistic regression as the classifiers are then used. Highest classification accuracy of 83.3% is obtained by correlation dimension and LR classifier among other nonlinear features. For further improvement, all nonlinear features are combined and applied to classifiers. A classification accuracy of 90% is achieved by all nonlinear features and LR classifier. In all experiments, genetic algorithm is employed to select the most important features. The proposed technique is compared and contrasted with the other reported methods and it is demonstrated that by combining nonlinear features, the performance is enhanced. This study shows that nonlinear analysis of EEG can be a useful method for discriminating depressed patients and normal subjects. It is suggested that this analysis may be a complementary tool to help psychiatrists for diagnosing depressed patients. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  6. Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

    Science.gov (United States)

    Grin-Yatsenko, Vera A; Ponomarev, Valery A; Pronina, Marina V; Poliakov, Yury I; Plotnikova, Irina V; Kropotov, Juri D

    2017-09-01

    We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

  7. EEG in the classroom: Synchronised neural recordings during video presentation

    DEFF Research Database (Denmark)

    Poulsen, Andreas Trier; Kamronn, Simon Due; Dmochowski, Jacek

    2017-01-01

    We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional......-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom....

  8. Association between increased EEG signal complexity and cannabis dependence.

    Science.gov (United States)

    Laprevote, Vincent; Bon, Laura; Krieg, Julien; Schwitzer, Thomas; Bourion-Bedes, Stéphanie; Maillard, Louis; Schwan, Raymund

    2017-12-01

    Both acute and regular cannabis use affects the functioning of the brain. While several studies have demonstrated that regular cannabis use can impair the capacity to synchronize neural assemblies during specific tasks, less is known about spontaneous brain activity. This can be explored by measuring EEG complexity, which reflects the spontaneous variability of human brain activity. A recent study has shown that acute cannabis use can affect that complexity. Since the characteristics of cannabis use can affect the impact on brain functioning, this study sets out to measure EEG complexity in regular cannabis users with or without dependence, in comparison with healthy controls. We recruited 26 healthy controls, 25 cannabis users without cannabis dependence and 14 cannabis users with cannabis dependence, based on DSM IV TR criteria. The EEG signal was extracted from at least 250 epochs of the 500ms pre-stimulation phase during a visual evoked potential paradigm. Brain complexity was estimated using Lempel-Ziv Complexity (LZC), which was compared across groups by non-parametric Kruskall-Wallis ANOVA. The analysis revealed a significant difference between the groups, with higher LZC in participants with cannabis dependence than in non-dependent cannabis users. There was no specific localization of this effect across electrodes. We showed that cannabis dependence is associated to an increased spontaneous brain complexity in regular users. This result is in line with previous results in acute cannabis users. It may reflect increased randomness of neural activity in cannabis dependence. Future studies should explore whether this effect is permanent or diminishes with cannabis cessation. Copyright © 2017 Elsevier B.V. and ECNP. All rights reserved.

  9. Impact of age on both BIS values and EEG bispectrum during anaesthesia with sevoflurane in children.

    Science.gov (United States)

    Wodey, E; Tirel, O; Bansard, J Y; Terrier, A; Chanavaz, C; Harris, R; Ecoffey, C; Senhadji, L

    2005-06-01

    The aim of this study was to evaluate the potential relationship between age, BIS (Aspect), and the EEG bispectrum during anaesthesia with sevoflurane. BIS and raw EEG were recorded at a steady state of 1 MAC in 100 children, and during a decrease from 2 to 0.5 MAC in a sub-group of 29 children. The bispectrum of the EEG was estimated using MATLAB software. For analysis, the bispectrum was divided into 36 frequencies of coupling (P(i))--the MatBis. A multiple correspondence analysis (MCA) was used to establish an underlying structure of the pattern of each individual's MatBis at 1 MAC. Clustering of children into homogeneous groups was conducted by a hierarchical ascending classification (HAC). The level of statistical significance was set at 0.05. At 1 MAC, the BIS values for all children ranged from 20 to 74 (median 40). Projection of both age and BIS value recorded at 1 MAC onto the structured model of the MCA showed them to be distributed along the same axis, demonstrating that the different values of BIS obtained in younger or older children are mainly dependent on their MatBis. At 1 MAC, six homogeneous groups of children were obtained through the HAC. Groups 5 (30 months; range 23-49) and 6 (18 months; range 6-180) were the younger children and Group 1 (97 months; range 46-162) the older. Groups 5 and 6 had the highest median values of BIS (54; range 50-59) (55; range 26-74) and Group 1 the lowest values (29; range 22-37). The EEG bispectrum, as well as the BIS appeared to be strongly related to the age of children at 1 MAC sevoflurane.

  10. Tracking EEG changes in response to alpha and beta binaural beats.

    Science.gov (United States)

    Vernon, D; Peryer, G; Louch, J; Shaw, M

    2014-07-01

    A binaural beat can be produced by presenting two tones of a differing frequency, one to each ear. Such auditory stimulation has been suggested to influence behaviour and cognition via the process of cortical entrainment. However, research so far has only shown the frequency following responses in the traditional EEG frequency ranges of delta, theta and gamma. Hence a primary aim of this research was to ascertain whether it would be possible to produce clear changes in the EEG in either the alpha or beta frequency ranges. Such changes, if possible, would have a number of important implications as well as potential applications. A secondary goal was to track any observable changes in the EEG throughout the entrainment epoch to gain some insight into the nature of the entrainment effects on any changes in an effort to identify more effective entrainment regimes. Twenty two healthy participants were recruited and randomly allocated to one of two groups, each of which was exposed to a distinct binaural beat frequency for ten 1-minute epochs. The first group listened to an alpha binaural beat of 10 Hz and the second to a beta binaural beat of 20 Hz. EEG was recorded from the left and right temporal regions during pre-exposure baselines, stimulus exposure epochs and post-exposure baselines. Analysis of changes in broad-band and narrow-band amplitudes, and frequency showed no effect of binaural beat frequency eliciting a frequency following effect in the EEG. Possible mediating factors are discussed and a number of recommendations are made regarding future studies, exploring entrainment effects from a binaural beat presentation. Copyright © 2012 Elsevier B.V. All rights reserved.

  11. 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.

  12. EEG Clearing Office strengthened by EEG 2012. Alternative dispute resolution in the renewable energies industry; Aufwertung der Clearingstelle EEG durch das EEG 2012. Alternative Dispute Resolution im Bereich der Erneuerbaren Energien

    Energy Technology Data Exchange (ETDEWEB)

    Chatzinerantzis, Alexandros; Fach, Martin [Linklaters LLP, Frankfurt am Main (Germany). Praxisgruppe Litigation and Arbitration

    2012-11-14

    The EEG Clearing Office is a special arbitration forum for the purpose of facilitating quick and inexpensive out-of-court dispute resolutions and resolving cases of legal uncertainty in connection with the regulations of the EEG (Renewable Energy Law). The Clearing Office has developed dynamically over the past years, as the numbers of newly registered potential and ongoing procedures impressively show. In the 2012 amendment to the EEG the legislature has fundamentally revised and substantially widened the legal basis for the work of the Clearing Office. This provides the motivation for presenting the Clearing Office and its procedural rules in the following article.

  13. A STUDY ON EEG ABNORMALITIES IN CHILDREN WITH MIGRAINE

    Directory of Open Access Journals (Sweden)

    Subinay Mandal

    2017-04-01

    Full Text Available BACKGROUND Migraine is one of the common causes of headache in children. Migraine and epilepsy are both common episodic neurological disorders. The comorbidity of these two conditions is well known. Many researcher have pointed out that neuronal hyperexcitability is the initiating event for occurrence of migraine attack. The aim of the paper was to evaluate the EEG in children with migraine. MATERIALS AND METHODS We retrospectively analysed records of children who attended our paediatric outpatient department with diagnoses as suffering from migraine based on International Headache Society (IHS diagnostic criteria. Apart from detailed clinical history, EEG of every patient was collected and analysed. EEG was performed interictally at least 24 hours after the last episode of headache attack in all the cases. RESULTS 56 children (age range, 4-14 years constituted our study group. 64.3% children had migraine without aura (common type and in 23.2% cases had migraine with aura (classic type other were with migraine variants. Abnormal EEG was reported in 30.3% children. 17% of children with migraine without history of seizure had abnormal EEG. Sixty one percent of patients with aura had abnormal EEG. History of either febrile fits or afebrile fits was present in total 17.1% of cases. The type of paroxysmal discharges we came across was- a Sharp waves, b Spikes and c Spike and slow wave complexes. Abnormal paroxysmal sharp and spike-wave complexes (also called spike-and-slow-wave complexes were the most common EEG abnormality. CONCLUSION EEG abnormality was found in significant number of children with migraine both with and without history of seizure in our study. This indicates neuronal hyperexcitability during episodes of migraine. So, EEG should be considered in patients with clinical diagnoses of migraine to exclude association of any seizure activity.

  14. Differences in the perceived music pleasantness between monolateral cochlear implanted and normal hearing children assessed by EEG.

    Science.gov (United States)

    Vecchiato, G; Maglione, A G; Scorpecci, A; Malerba, P; Graziani, I; Cherubino, P; Astolfi, L; Marsella, P; Colosimo, A; Babiloni, Fabio

    2013-01-01

    The perception of the music in cochlear implanted (CI) patients is an important aspect of their quality of life. In fact, the pleasantness of the music perception by such CI patients can be analyzed through a particular analysis of EEG rhythms. Studies on healthy subjects show that exists a particular frontal asymmetry of the EEG alpha rhythm which can be correlated with pleasantness of the perceived stimuli (approach-withdrawal theory). In particular, here we describe differences between EEG activities estimated in the alpha frequency band for a monolateral CI group of children and a normal hearing one during the fruition of a musical cartoon. The results of the present analysis showed that the alpha EEG asymmetry patterns related to the normal hearing group refers to a higher pleasantness perception when compared to the cerebral activity of the monolateral CI patients. In fact, the present results support the statement that a monolateral CI group could perceive the music in a less pleasant way when compared to normal hearing children.

  15. On the Keyhole Hypothesis: High Mutual Information between Ear and Scalp EEG

    Directory of Open Access Journals (Sweden)

    Kaare B. Mikkelsen

    2017-06-01

    Full Text Available We propose and test the keyhole hypothesis—that measurements from low dimensional EEG, such as ear-EEG reflect a broadly distributed set of neural processes. We formulate the keyhole hypothesis in information theoretical terms. The experimental investigation is based on legacy data consisting of 10 subjects exposed to a battery of stimuli, including alpha-attenuation, auditory onset, and mismatch-negativity responses and a new medium-long EEG experiment involving data acquisition during 13 h. Linear models were estimated to lower bound the scalp-to-ear capacity, i.e., predicting ear-EEG data from simultaneously recorded scalp EEG. A cross-validation procedure was employed to ensure unbiased estimates. We present several pieces of evidence in support of the keyhole hypothesis: There is a high mutual information between data acquired at scalp electrodes and through the ear-EEG “keyhole,” furthermore we show that the view—represented as a linear mapping—is stable across both time and mental states. Specifically, we find that ear-EEG data can be predicted reliably from scalp EEG. We also address the reverse view, and demonstrate that large portions of the scalp EEG can be predicted from ear-EEG, with the highest predictability achieved in the temporal regions and when using ear-EEG electrodes with a common reference electrode.

  16. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

    Science.gov (United States)

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-09-01

    Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. 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.

  18. Similar or disparate brain patterns? The intra-personal EEG variability of three women with multiple personality disorder.

    Science.gov (United States)

    Lapointe, A R; Crayton, J W; DeVito, R; Fichtner, C G; Konopka, L M

    2006-07-01

    Quantitative EEG was used to assess the intra-personal variability of brain electrical activity for 3 women diagnosed with Multiple Personality Disorder (MPD). Two separate control groups (within-subject and between-subject) were used to test the hypothesis that the intra-personal EEG variability between 2 alters would be less than the interpersonal EEG variability between 2 controls, and similar to the intra-personal EEG variability of a single personality. This hypothesis was partially supported. In general, the 2 EEG records of a MPD subject (alter 1 vs. alter 2) were more different from one another than the 2 EEG records of a single control, but less different from one another than the EEG records of 2 separate controls. Most of the EEG variability between alters involved beta activity in the frontal and temporal lobes.

  19. EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients

    Science.gov (United States)

    Ramirez, Rafael; Planas, Josep; Escude, Nuria; Mercade, Jordi; Farriols, Cristina

    2018-01-01

    Music is known to have the power to induce strong emotions. The present study assessed, based on Electroencephalography (EEG) data, the emotional response of terminally ill cancer patients to a music therapy intervention in a randomized controlled trial. A sample of 40 participants from the palliative care unit in the Hospital del Mar in Barcelona was randomly assigned to two groups of 20. The first group [experimental group (EG)] participated in a session of music therapy (MT), and the second group [control group (CG)] was provided with company. Based on our previous work on EEG-based emotion detection, instantaneous emotional indicators in the form of a coordinate in the arousal-valence plane were extracted from the participants’ EEG data. The emotional indicators were analyzed in order to quantify (1) the overall emotional effect of MT on the patients compared to controls, and (2) the relative effect of the different MT techniques applied during each session. During each MT session, five conditions were considered: I (initial patient’s state before MT starts), C1 (passive listening), C2 (active listening), R (relaxation), and F (final patient’s state). EEG data analysis showed a significant increase in valence (p = 0.0004) and arousal (p = 0.003) between I and F in the EG. No significant changes were found in the CG. This results can be interpreted as a positive emotional effect of MT in advanced cancer patients. In addition, according to pre- and post-intervention questionnaire responses, participants in the EG also showed a significant decrease in tiredness, anxiety and breathing difficulties, as well as an increase in levels of well-being. No equivalent changes were observed in the CG. PMID:29551984

  20. EEG-Based Analysis of the Emotional Effect of Music Therapy on Palliative Care Cancer Patients

    Directory of Open Access Journals (Sweden)

    Rafael Ramirez

    2018-03-01

    Full Text Available Music is known to have the power to induce strong emotions. The present study assessed, based on Electroencephalography (EEG data, the emotional response of terminally ill cancer patients to a music therapy intervention in a randomized controlled trial. A sample of 40 participants from the palliative care unit in the Hospital del Mar in Barcelona was randomly assigned to two groups of 20. The first group [experimental group (EG] participated in a session of music therapy (MT, and the second group [control group (CG] was provided with company. Based on our previous work on EEG-based emotion detection, instantaneous emotional indicators in the form of a coordinate in the arousal-valence plane were extracted from the participants’ EEG data. The emotional indicators were analyzed in order to quantify (1 the overall emotional effect of MT on the patients compared to controls, and (2 the relative effect of the different MT techniques applied during each session. During each MT session, five conditions were considered: I (initial patient’s state before MT starts, C1 (passive listening, C2 (active listening, R (relaxation, and F (final patient’s state. EEG data analysis showed a significant increase in valence (p = 0.0004 and arousal (p = 0.003 between I and F in the EG. No significant changes were found in the CG. This results can be interpreted as a positive emotional effect of MT in advanced cancer patients. In addition, according to pre- and post-intervention questionnaire responses, participants in the EG also showed a significant decrease in tiredness, anxiety and breathing difficulties, as well as an increase in levels of well-being. No equivalent changes were observed in the CG.

  1. Combining Cryptography with EEG Biometrics.

    Science.gov (United States)

    Damaševičius, Robertas; Maskeliūnas, Rytis; Kazanavičius, Egidijus; Woźniak, Marcin

    2018-01-01

    Cryptographic frameworks depend on key sharing for ensuring security of data. While the keys in cryptographic frameworks must be correctly reproducible and not unequivocally connected to the identity of a user, in biometric frameworks this is different. Joining cryptography techniques with biometrics can solve these issues. We present a biometric authentication method based on the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem (BCH) codes, perform its security analysis, and demonstrate its security characteristics. We evaluate a biometric cryptosystem using our own dataset of electroencephalography (EEG) data collected from 42 subjects. The experimental results show that the described biometric user authentication system is effective, achieving an Equal Error Rate (ERR) of 0.024.

  2. An Investigation of Stimulant Effects on the EEG of Children With Attention-Deficit/Hyperactivity Disorder.

    Science.gov (United States)

    Clarke, Adam R; Barry, Robert J; Baker, Iris E; McCarthy, Rory; Selikowitz, Mark

    2017-07-01

    Stimulant medications are the most commonly prescribed treatment for Attention-Deficit/Hyperactivity Disorder (AD/HD). These medications result in a normalization of the EEG. However, past research has found that complete normalization of the EEG is not always achieved. One reason for this may be that studies have used different medications interchangeably, or groups of subjects on different stimulants. This study investigated whether methylphenidate and dexamphetamine produce different levels of normalization of the EEG in children with AD/HD. Three groups of 20 boys participated in this study. There were 2 groups with a diagnosis of AD/HD; one group, good responders to methylphenidate, and the second, good responders to dexamphetamine. The third group was a normal control group. Baseline EEGs were recorded using an eyes-closed resting condition, and analyzed for total power and relative delta, theta, alpha, and beta. Subjects were placed on a 6-month trial of methylphenidate or dexamphetamine, after which a second EEG was recorded. At baseline, the children with AD/HD had elevated relative theta, less relative alpha and beta compared with controls. Baseline differences were found between the two medication groups, with the dexamphetamine group having greater EEG abnormalities than the methylphenidate group. The results indicate that good responders to methylphenidate and dexamphetamine have different EEG profiles when assessed before medication, and these differences may represent different underlying central nervous system deficits. The 2 medications were found to result in substantial normalization of the EEG, with no significant differences in EEG changes occurring between the 2 medications. This indicates that the degree of pretreatment EEG abnormality was the major factor contributing to the degree of normalization of the EEG. As good responders to the 2 medications appear to have different central nervous system abnormalities, it is recommended that

  3. 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 ...

  4. Cortical sources of resting state EEG rhythms are related to brain hypometabolism in subjects with Alzheimer's disease: an EEG-PET study.

    Science.gov (United States)

    Babiloni, Claudio; Del Percio, Claudio; Caroli, Anna; Salvatore, Elena; Nicolai, Emanuele; Marzano, Nicola; Lizio, Roberta; Cavedo, Enrica; Landau, Susan; Chen, Kewei; Jagust, William; Reiman, Eric; Tedeschi, Gioacchino; Montella, Patrizia; De Stefano, Manuela; Gesualdo, Loreto; Frisoni, Giovanni B; Soricelli, Andrea

    2016-12-01

    Cortical sources of resting state electroencephalographic (EEG) delta (2-4 Hz) and low-frequency alpha (8-10.5 Hz) rhythms show abnormal activity (i.e., current density) in patients with dementia due to Alzheimer's disease (AD). Here, we hypothesized that abnormality of this activity is related to relevant disease processes as revealed by cortical hypometabolism typically observed in AD patients by fluorodeoxyglucose positron emission tomography. Resting state eyes-closed EEG data were recorded in 19 AD patients with dementia and 40 healthy elderly (Nold) subjects. EEG frequency bands of interest were delta and low-frequency alpha. EEG sources were estimated in these bands by low-resolution brain electromagnetic tomography (LORETA). Fluorodeoxyglucose positron emission tomography images were recorded only in the AD patients, and cortical hypometabolism was indexed by the so-called Alzheimer's discrimination analysis tool (PALZ) in the frontal association, ventromedial frontal, temporoparietal association, posterior cingulate, and precuneus areas. Results showed that compared with the Nold group, the AD group pointed to higher activity of delta sources and lower activity of low-frequency alpha sources in a cortical region of interest formed by all cortical areas of the PALZ score. In the AD patients, there was a positive correlation between the PALZ score and the activity of delta sources in the cortical region of interest (p < 0.05). These results suggest a relationship between resting state cortical hypometabolism and synchronization of cortical neurons at delta rhythms in AD patients with dementia. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal

    Directory of Open Access Journals (Sweden)

    Roberta eSclocco

    2014-04-01

    Full Text Available In the last decade, an increasing interest has arisen in investigating the relationship between the electrophysiological and hemodynamic measurements of brain activity, such as EEG and (BOLD fMRI. In particular, changes in BOLD have been shown to be associated with changes in the spectral profile of neural activity, rather than with absolute power. Concurrently, recent findings showed that different EEG rhythms are independently related to changes in the BOLD signal: therefore, it would be important to distinguish between the contributions of the different EEG rhythms to BOLD fluctuations when modeling the relationship between the two signals. Here we propose a method to perform EEG-informed fMRI analysis, in which the EEG regressors take into account both the changes in the spectral profile and the rhythms distinction. We applied it to EEG-fMRI data during a hand grip task in healthy subjects, and compared the results with those obtained by two existing models found in literature. Our results showed that the proposed method better captures the correlations between BOLD signal and EEG rhythms modulations, identifying task-related, well localized activated volumes. Furthermore, we showed that including among the regressors also EEG rhythms not primarily involved in the task enhances the performance of the analysis, even when only correlations with BOLD signal and specific EEG rhythms are explored.

  6. 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.

  7. Hippocampal EEG and behaviour in dog. I. Hippocampal EEG correlates of gross motor behaviour

    NARCIS (Netherlands)

    Arnolds, D.E.A.T.; Lopes da Silva, F.H.; Aitink, J.W.; Kamp, A.

    It was shown that rewarding spectral shifts (i.e. increase in amplitude or peak frequency of the hippocampal EEG) causes a solitary dog to show increased motor behaviour. Rewarded spectral shifts concurred with a variety of behavioural transitions. It was found that statistically significant

  8. 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.

  9. 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.

  10. Generalized periodic EEG activity in two cases of neurosyphilis

    Directory of Open Access Journals (Sweden)

    Anghinah Renato

    2006-01-01

    Full Text Available Neurosyphilis is a recognized cause of epileptic seizures and cognitive impairment, but is not usually associated with the finding of generalized periodic activity in the EEG. We report two similar cases characterized by progressive cognitive impairment followed by partial complex seizures, in whom the EEG showed generalized periodic activity. Both cerebrospinal fluid and the response to penicillin therapy confirmed the diagnoses of neurosyphilis in the two cases. The finding of EEG generalized periodic activity in patients with cognitive or behavioral disorders is usually associated with Creutzfeldt-Jakob disease, although there are other conditions, some of them potentially reversible, which may also present this EEG abnormality. Neurosyphilis has tended not to be included among them, and our present findings support the importance of first ruling out neurosyphilis in those patients with cognitive or behavioral disorders associated with generalized periodic epileptiform discharges.

  11. 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

  12. 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.

  13. A Pilot Study of EEG Source Analysis Based Repetitive Transcranial Magnetic Stimulation for the Treatment of Tinnitus.

    Directory of Open Access Journals (Sweden)

    Hui Wang

    Full Text Available Repetitive Transcranial Magnetic Stimulation (rTMS is a novel therapeutic tool to induce a suppression of tinnitus. However, the optimal target sites are unknown. We aimed to determine whether low-frequency rTMS induced lasting suppression of tinnitus by decreasing neural activity in the cortex, navigated by high-density electroencephalogram (EEG source analysis, and the utility of EEG for targeting treatment.In this controlled three-armed trial, seven normal hearing patients with tonal tinnitus received a 10-day course of 1-Hz rTMS to the cortex, navigated by high-density EEG source analysis, to the left temporoparietal cortex region, and to the left temporoparietal with sham stimulation. The Tinnitus handicap inventory (THI and a visual analog scale (VAS were used to assess tinnitus severity and loudness. Measurements were taken before, and immediately, 2 weeks, and 4 weeks after the end of the interventions.Low-frequency rTMS decreased tinnitus significantly after active, but not sham, treatment. Responders in the EEG source analysis-based rTMS group, 71.4% (5/7 patients, experienced a significant reduction in tinnitus loudness, as evidenced by VAS scores. The target site of neuronal generators most consistently associated with a positive response was the frontal lobe in the right hemisphere, sourced using high-density EEG equipment, in the tinnitus patients. After left temporoparietal rTMS stimulation, 42.8% (3/7 patients experienced a decrease in tinnitus loudness.Active EEG source analysis based rTMS resulted in significant suppression in tinnitus loudness, showing the superiority of neuronavigation-guided coil positioning in dealing with tinnitus. Non-auditory areas should be considered in the pathophysiology of tinnitus. This knowledge in turn can contribute to investigate the pathophysiology of tinnitus.

  14. Added clinical value of the inferior temporal EEG electrode chain.

    Science.gov (United States)

    Bach Justesen, Anders; Eskelund Johansen, Ann Berit; Martinussen, Noomi Ida; Wasserman, Danielle; Terney, Daniella; Meritam, Pirgit; Gardella, Elena; Beniczky, Sándor

    2018-01-01

    To investigate the diagnostic added value of supplementing the 10-20 EEG array with six electrodes in the inferior temporal chain. EEGs were recorded with 25 electrodes: 19 positions of the 10-20 system, and six additional electrodes in the inferior temporal chain (F9/10, T9/10, P9/10). Five-hundred consecutive standard and sleep EEG recordings were reviewed using the 10-20 array and the extended array. We identified the recordings with EEG abnormalities that had peak negativities at the inferior temporal electrodes, and those that only were visible at the inferior temporal electrodes. From the 286 abnormal recordings, the peak negativity was at the inferior temporal electrodes in 81 cases (28.3%) and only visible at the inferior temporal electrodes in eight cases (2.8%). In the sub-group of patients with temporal abnormalities (n = 134), these represented 59% (peak in the inferior chain) and 6% (only seen at the inferior chain). Adding six electrodes in the inferior temporal electrode chain to the 10-20 array improves the localization and identification of EEG abnormalities, especially those located in the temporal region. Our results suggest that inferior temporal electrodes should be added to the EEG array, to increase the diagnostic yield of the recordings. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  15. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

    Directory of Open Access Journals (Sweden)

    Duffy Frank H

    2012-06-01

    Full Text Available Abstract Background The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Methods Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD; 571 children were neuro-typical controls (C. After artifact management, principal components analysis (PCA identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984. Discriminant function analysis (DFA determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Results Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P Conclusions Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range

  16. 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

  17. Classifying Drivers' Cognitive Load Using EEG Signals.

    Science.gov (United States)

    Barua, Shaibal; Ahmed, Mobyen Uddin; Begum, Shahina

    2017-01-01

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy.

  18. EEG- and CT-findings in comatous patients after cerebral trauma

    International Nuclear Information System (INIS)

    Krueger, J.

    1983-01-01

    It is reported about a prospectively planned series of tests on 67 patients after cerebral trauma. During the first three days and every third day afterwards the patients were examined cliniconeurologically and by means of EEG, frequency analysis of EEG and - as far as possible - CCT. The EEG shows the cerebral function, in the CCT we see, like in a pathological-anatomic section, intracranial morphological changes. The results are discussed in detail. (orig.) [de

  19. 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 ...

  20. Experiments on Classification of Electroencephalography (EEG Signals in Imagination of Direction using Stacked Autoencoder

    Directory of Open Access Journals (Sweden)

    Kenta Tomonaga

    2017-08-01

    Full Text Available This paper presents classification methods for electroencephalography (EEG signals in imagination of direction measured by a portable EEG headset. In the authorsr previous studies, principal component analysis extracted significant features from EEG signals to construct neural network classifiers. To improve the performance, the authors have implemented a Stacked Autoencoder (SAE for the classification. The SAE carries out feature extraction and classification in a form of multi-layered neural network. Experimental results showed that the SAE outperformed the previous classifiers.

  1. Single-channel in-ear-EEG detects the focus of auditory attention to concurrent tone streams and mixed speech

    Science.gov (United States)

    Fiedler, Lorenz; Wöstmann, Malte; Graversen, Carina; Brandmeyer, Alex; Lunner, Thomas; Obleser, Jonas

    2017-06-01

    Objective. Conventional, multi-channel scalp electroencephalography (EEG) allows the identification of the attended speaker in concurrent-listening (‘cocktail party’) scenarios. This implies that EEG might provide valuable information to complement hearing aids with some form of EEG and to install a level of neuro-feedback. Approach. To investigate whether a listener’s attentional focus can be detected from single-channel hearing-aid-compatible EEG configurations, we recorded EEG from three electrodes inside the ear canal (‘in-Ear-EEG’) and additionally from 64 electrodes on the scalp. In two different, concurrent listening tasks, participants (n  =  7) were fitted with individualized in-Ear-EEG pieces and were either asked to attend to one of two dichotically-presented, concurrent tone streams or to one of two diotically-presented, concurrent audiobooks. A forward encoding model was trained to predict the EEG response at single EEG channels. Main results. Each individual participants’ attentional focus could be detected from single-channel EEG response recorded from short-distance configurations consisting only of a single in-Ear-EEG electrode and an adjacent scalp-EEG electrode. The differences in neural responses to attended and ignored stimuli were consistent in morphology (i.e. polarity and latency of components) across subjects. Significance. In sum, our findings show that the EEG response from a single-channel, hearing-aid-compatible configuration provides valuable information to identify a listener’s focus of attention.

  2. Deviant dynamics of EEG resting state pattern in 22q11.2 deletion syndrome adolescents: A vulnerability marker of schizophrenia?

    Science.gov (United States)

    Tomescu, Miralena I; Rihs, Tonia A; Becker, Robert; Britz, Juliane; Custo, Anna; Grouiller, Frédéric; Schneider, Maude; Debbané, Martin; Eliez, Stephan; Michel, Christoph M

    2014-08-01

    Previous studies have repeatedly found altered temporal characteristics of EEG microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG microstates. If this is the case, temporal alterations of EEG microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG-fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS. Copyright © 2014 Elsevier B.V. All rights reserved.

  3. Topographical characteristics and principal component structure of the hypnagogic EEG.

    Science.gov (United States)

    Tanaka, H; Hayashi, M; Hori, T

    1997-07-01

    The purpose of the present study was to identify the dominant topographic components of electroencephalographs (EEG) and their behavior during the waking-sleeping transition period. Somnography of nocturnal sleep was recorded on 10 male subjects. Each recording, from "lights-off" to 5 minutes after the appearance of the first sleep spindle, was analyzed. The typical EEG patterns during hypnagogic period were classified into nine EEG stages. Topographic maps demonstrated that the dominant areas of alpha-band activity moved from the posterior areas to anterior areas along the midline of the scalp. In delta-, theta-, and sigma-band activities, the differences of EEG amplitude between the focus areas (the dominant areas) and the surrounding areas increased as a function of EEG stage. To identify the dominant topographic components, a principal component analysis was carried out on a 12-channel EEG data set for each of six frequency bands. The dominant areas of alpha 2- (9.6-11.4 Hz) and alpha 3- (11.6-13.4 Hz) band activities moved from the posterior to anterior areas, respectively. The distribution of alpha 2-band activity on the scalp clearly changed just after EEG stage 3 (alpha intermittent, < 50%). On the other hand, alpha 3-band activity became dominant in anterior areas after the appearance of vertex sharp-wave bursts (EEG stage 7). For the sigma band, the amplitude of extensive areas from the frontal pole to the parietal showed a rapid rise after the onset of stage 7 (the appearance of vertex sharp-wave bursts). Based on the results, sleep onset process probably started before the onset of sleep stage 1 in standard criteria. On the other hand, the basic sleep process may start before the onset of sleep stage 2 or the manually scored spindles.

  4. Correlation of EEG with neuropsychological status in children with epilepsy.

    Science.gov (United States)

    Hsu, David A; Rayer, Katherine; Jackson, Daren C; Stafstrom, Carl E; Hsu, Murielle; Ferrazzano, Peter A; Dabbs, Kevin; Worrell, Gregory A; Jones, Jana E; Hermann, Bruce P

    2016-02-01

    To determine correlations of the EEG frequency spectrum with neuropsychological status in children with idiopathic epilepsy. Forty-six children ages 8-18 years old with idiopathic epilepsy were retrospectively identified and analyzed for correlations between EEG spectra and neuropsychological status using multivariate linear regression. In addition, the theta/beta ratio, which has been suggested as a clinically useful EEG marker of attention-deficit hyperactivity disorder (ADHD), and an EEG spike count were calculated for each subject. Neuropsychological status was highly correlated with posterior alpha (8-15 Hz) EEG activity in a complex way, with both positive and negative correlations at lower and higher alpha frequency sub-bands for each cognitive task in a pattern that depends on the specific cognitive task. In addition, the theta/beta ratio was a specific but insensitive indicator of ADHD status in children with epilepsy; most children both with and without epilepsy have normal theta/beta ratios. The spike count showed no correlations with neuropsychological status. (1) The alpha rhythm may have at least two sub-bands which serve different purposes. (2) The theta/beta ratio is not a sensitive indicator of ADHD status in children with epilepsy. (3) The EEG frequency spectrum correlates more robustly with neuropsychological status than spike count analysis in children with idiopathic epilepsy. (1) The role of posterior alpha rhythms in cognition is complex and can be overlooked if EEG spectral resolution is too coarse or if neuropsychological status is assessed too narrowly. (2) ADHD in children with idiopathic epilepsy may involve different mechanisms from those in children without epilepsy. (3) Reliable correlations with neuropsychological status require longer EEG samples when using spike count analysis than when using frequency spectra. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights

  5. [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.

  6. Corrected Four-Sphere Head Model for EEG Signals.

    Science.gov (United States)

    Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn V; Dale, Anders M; Einevoll, Gaute T; Wójcik, Daniel K

    2017-01-01

    The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

  7. Corrected Four-Sphere Head Model for EEG Signals

    Directory of Open Access Journals (Sweden)

    Solveig Næss

    2017-10-01

    Full Text Available The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF, skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM. We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

  8. 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.

  9. Mutual information measures applied to EEG signals for sleepiness characterization.

    Science.gov (United States)

    Melia, Umberto; Guaita, Marc; Vallverdú, Montserrat; Embid, Cristina; Vilaseca, Isabel; Salamero, Manel; Santamaria, Joan

    2015-03-01

    Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

  10. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study.

    Science.gov (United States)

    Duffy, Frank H; Als, Heidelise

    2012-06-26

    The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact. Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls. Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.

  11. Thalamic involvement in the regulation of alpha EEG activity in psychiatric patients

    International Nuclear Information System (INIS)

    Shirazi, S.P.; Pakula, J.; Young, I.J.; Crayton, J.W.; Konopka, L.M.; Rybak, M.

    2002-01-01

    Aim: The thalamus is considered to be an important sub-cortical system involved in modulation of cortical activities. A relationship between thalamic activity and surface EEG was recently reported. In this study we evaluated a group of patients with psychiatric disorders who presented with asymmetric perfusion of the thalamus based on brain SPECT HMPAO studies. We predicted that asymmetrical activity of the thalamus would have asymmetrically distributed surface qEEG activity patterns. Materials and Methods: Twenty-three male psychiatric patients (age 54±14) with a primary diagnosis of depression and co-morbid substance abuse (83%) were studied with qEEG and HMPAO brain SPECT. The HMPAO ligand was administered while the EEG activity was being recorded. The SPECT analysis was conducted by means of ROI and SPM. ROI regions were determined based on the Talairach atlas coordinate system. ROI locations were verified by the automated utility, Talairach Demon. QEEG data was analyzed by a standardized protocol involving the NxLink database. Correlations between SPECT findings and qEEG absolute power were calculated. Results: Patients were divided into two groups based on thalamic perfusion patterns. Group 1 (Gr 1) had decreased perfusion to the right thalamus whereas Group 2 (Gr 2) had decreased perfusion to the left thalamus. SPM comparison of the patient groups to normal control subjects indicated significant findings. Comparison of Gr 1 to controls showed increased activity in the left temporal lobe and vermis. Decreased activity was observed in the left and right medial frontal lobes (right Brodmann 9;left Brodmann 6) as well as the left (Brodmann 30) and right (Brodmann 24) cingulate. Gr 2 comparison showed increased activity in the right middle frontal gyrus (Brodmann 10) and left inferior parietal lobe. Decreased activity was found in the left inferior frontal lobe (Brodmann 47). A positive correlation between alpha power and thalamic perfusion was identified in Gr

  12. Coherence of EEG frequency components during manual movements executed by the subdominant hand in women

    Directory of Open Access Journals (Sweden)

    O. V. Korzhyk

    2017-02-01

    Full Text Available The academic community is paying more and more attention to the question of the individual characteristics of the brain processes which ensure the manual motor programming of movements performed not only by the leading, but also by the subdominant hand. Researchers do not exclude the existence of the particular parameters of the human brain correlating with manual motor activities. This study involved 136 women at the age of 19–21 years. The testees were divided into two groups according to high and low values of the EEG modal α-frequency determined individually and in a motionless state. We evaluated the coherence status of the EEG frequency components in the motionless state and during movements performed by fingers of the subdominant (left hand in response to rhythmic sound signals. The testing stages involved the sequential execution of motor tasks including clamping and unclamping performed by the fingers of the subdominant hand (such as grasping movements without effort. The testees also performed fingering (a manual response to each stimulus at in different times and not by all the fingers of the hand simultaneously, but separately, one by one, in a given sequence. Clamping and unclamping was executed by the fingers subject to power loading the (additional load on the fingers being 10H. Execution of manual movements by means of the subdominant hand in response to the sensory signals was accompanied by an increase in coherence in the EEG frequency components, especially in the frontal, temporal, and parietal cortexes of the central areas. Women with a low individual α-rate of such a regularity had significantly increased scores at the high (α3-, β- frequencies of the EEG spectrum. At the same time, women in both groups mainly showed a decrease in the coherence coefficients of θ-, α1- and α3-activity in the frontal cortex leads in terms of the execution of the sequential finger movements and movements under power loading. The

  13. Maturation of EEG Power Spectra in Early Adolescence: A Longitudinal Study

    Science.gov (United States)

    Cragg, Lucy; Kovacevic, Natasa; McIntosh, Anthony Randal; Poulsen, Catherine; Martinu, Kristina; Leonard, Gabriel; Paus, Tomas

    2011-01-01

    This study investigated the fine-grained development of the EEG power spectra in early adolescence, and the extent to which it is reflected in changes in peak frequency. It also sought to determine whether sex differences in the EEG power spectra reflect differential patterns of maturation. A group of 56 adolescents were tested at age 10 years and…

  14. Novel Method To Identify Source-Associated Phylogenetic Clustering Shows that Listeria monocytogenes Includes Niche-Adapted Clonal Groups with Distinct Ecological Preferences

    DEFF Research Database (Denmark)

    Nightingale, K. K.; Lyles, K.; Ayodele, M.

    2006-01-01

    population are identified (TreeStats test). Analysis of sequence data for 120 L. monocytogenes isolates revealed evidence of clustering between isolates from the same source, based on the phylogenies inferred from actA and inlA (P = 0.02 and P = 0.07, respectively; SourceCluster test). Overall, the Tree...... are biologically valid. Overall, our data show that (i) the SourceCluster and TreeStats tests can identify biologically meaningful source-associated phylogenetic clusters and (ii) L. monocytogenes includes clonal groups that have adapted to infect specific host species or colonize nonhost environments......., including humans, animals, and food. If the null hypothesis that the genetic distances for isolates within and between source populations are identical can be rejected (SourceCluster test), then particular clades in the phylogenetic tree with significant overrepresentation of sequences from a given source...

  15. Family Nurture Intervention in preterm infants alters frontal cortical functional connectivity assessed by EEG coherence.

    Science.gov (United States)

    Myers, M M; Grieve, P G; Stark, R I; Isler, J R; Hofer, M A; Yang, J; Ludwig, R J; Welch, M G

    2015-07-01

    To assess the impact of Family Nurture Intervention (FNI) on cortical function in preterm infants at term age. Family Nurture Intervention is a NICU-based intervention designed to establish emotional connection between mothers and preterm infants. Infants born at 26-34 weeks postmenstrual age (PMA) were divided into two groups, standard care (SC, N = 49) and FNI (FNI, N = 56). Infants had EEG recordings of ~one hour duration with 124 lead nets between 37 and 44 weeks PMA. Coherence was measured between all pairs of electrodes in ten frequency bands. Data were summarised both within and between 12 regions during two sleep states (active, quiet). Coherence levels were negatively correlated with PMA age in both groups. As compared to SC infants, FNI infants showed significantly lower levels of EEG coherence (1-18 Hz) largely within and between frontal regions. Coherence in FNI infants was decreased in regions where we previously found robust increases in EEG power. As coherence decreases with age, results suggest that FNI may accelerate brain maturation particularly in frontal brain regions, which have been shown in research by others to be involved in regulation of attention, cognition and emotion regulation; domains deficient in preterm infants. ©2015 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

  16. 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.

  17. EEG spectral analysis and its clinical significance for patients with non-occupationalchronic mercury poisoning

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    Bin-bin SUN

    2015-03-01

    Full Text Available Objective To evaluate the features of EEG spectrum and its clinical significance for patients with non-occupational chronic mercury poisoning.  Methods Eighteen patients with chronic mercury poisoning were collected continuously as poisoning group at Affiliated Hospital of Academy of Military Medical Sciences from March 2012 to September 2013. At the same time, 12 age- and sex-matched healthy people were selected as control group. All patients underwent video EEG, and EEGLAB in Matlab 2013 software was used to analyze their EEG data. Relevant spectrum data of the 2 groups were compared and analyzed.  Results The frequency-energy curves of 12 normal subjects were similar to sine curve, with obvious energy peak at α band. The frequency-energy curves of 18 patients showed as follows: 5 cases had the peak at slow δ wave, and the energy curve decreased since δ band appeared, with α band peak disappearing. The curve of 10 cases had 2 peaks respectively at α and δ band, and δ peak was higher than α peak. The spectrum in other 3 cases was normal. The quantitative analysis of EEG revealed the proportion of δ band for the total energy. The proportion of δ band for total energy of the poisoning group in right middle temporal (P = 0.018 and left posterior temporal (P = 0.039 channel was significantly higher than that of the normal group, while the proportion of δ band in middle frontal (P = 0.003, right frontal (P = 0.016 and right anterior temporal (P = 0.024, left middle temporal (P = 0.036 and right posterior temporal (P = 0.031 was lower than that of the normal group. Conclusions EEG examination plays an important role in assessing the severity of brain injury for patients with non-occupational chronic mercury poisoning. Spectrum analysis is an intuitive and simple method, and can provide some help for clinical diagnosis and treatment. DOI: 10.3969/j.issn.1672-6731.2015.02.013

  18. 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.

  19. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures.

    Science.gov (United States)

    Wang, Lei; Long, Xi; Arends, Johan B A M; Aarts, Ronald M

    2017-10-01

    The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. A single-channel EEG signal can be mapped into visibility graphs (VGS), including basic visibility graph (VG), horizontal VG (HVG), and difference VG (DVG). These graphs were used to characterize different EEG seizure patterns. To demonstrate its effectiveness in identifying EEG seizure patterns and detecting generalized seizures, EEG recordings of 615h on one EEG channel from 29 epileptic patients with ID were analyzed. A novel feature set with discriminative power for seizure detection was obtained by using the VGS method. The degree distributions (DDs) of DVG can clearly distinguish EEG of each seizure pattern. The degree entropy and power-law degree power in DVG were proposed here for the first time, and they show significant difference between seizure and non-seizure EEG. The connecting structure measured by HVG can better distinguish seizure EEG from background than those by VG and DVG. A traditional EEG feature set based on frequency analysis was used here as a benchmark feature set. With a support vector machine (SVM) classifier, the seizure detection performance of the benchmark feature set (sensitivity of 24%, FD t /h of 1.8s) can be improved by combining our proposed VGS features extracted from one EEG channel (sensitivity of 38%, FD t /h of 1.4s). The proposed VGS-based features can help improve seizure detection for ID patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. EEG Oscillatory States: Universality, Uniqueness and Specificity across Healthy-Normal, Altered and Pathological Brain Conditions

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

    For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations’ functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal. PMID:24505292

  1. EEG Signal Quality of a Subcutaneous Recording System Compared to Standard Surface Electrodes

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    Jonas Duun-Henriksen

    2015-01-01

    Full Text Available Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology. Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis and qualitative (blinded subjective scoring by neurophysiologists analysis. Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed. This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well. Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.

  2. Resting EEG deficits in accused murderers with schizophrenia.

    Science.gov (United States)

    Schug, Robert A; Yang, Yaling; Raine, Adrian; Han, Chenbo; Liu, Jianghong; Li, Liejia

    2011-10-31

    Empirical evidence continues to suggest a biologically distinct violent subtype of schizophrenia. The present study examined whether murderers with schizophrenia would demonstrate resting EEG deficits distinguishing them from both non-violent schizophrenia patients and murderers without schizophrenia. Resting EEG data were collected from five diagnostic groups (normal controls, non-murderers with schizophrenia, murderers with schizophrenia, murderers without schizophrenia, and murderers with psychiatric conditions other than schizophrenia) at a brain hospital in Nanjing, China. Murderers with schizophrenia were characterized by increased left-hemispheric fast-wave EEG activity relative to non-violent schizophrenia patients, while non-violent schizophrenia patients instead demonstrated increased diffuse slow-wave activity compared to all other groups. Results are discussed within the framework of a proposed left-hemispheric over-processing hypothesis specific to violent individuals with schizophrenia, involving left hemispheric hyperarousal deficits, which may lead to a homicidally violent schizophrenia outcome. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  3. Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding

    Science.gov (United States)

    Cheung, Mei-chun; Chan, Agnes S.; Liu, Ying; Law, Derry; Wong, Christina W. Y.

    2017-01-01

    Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG) study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group) and 30 of whom had never received music training (the NMT group). The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation. PMID:28358852

  4. Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding.

    Directory of Open Access Journals (Sweden)

    Mei-Chun Cheung

    Full Text Available Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group and 30 of whom had never received music training (the NMT group. The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation.

  5. Music training is associated with cortical synchronization reflected in EEG coherence during verbal memory encoding.

    Science.gov (United States)

    Cheung, Mei-Chun; Chan, Agnes S; Liu, Ying; Law, Derry; Wong, Christina W Y

    2017-01-01

    Music training can improve cognitive functions. Previous studies have shown that children and adults with music training demonstrate better verbal learning and memory performance than those without such training. Although prior studies have shown an association between music training and changes in the structural and functional organization of the brain, there is no concrete evidence of the underlying neural correlates of the verbal memory encoding phase involved in such enhanced memory performance. Therefore, we carried out an electroencephalography (EEG) study to investigate how music training was associated with brain activity during the verbal memory encoding phase. Sixty participants were recruited, 30 of whom had received music training for at least one year (the MT group) and 30 of whom had never received music training (the NMT group). The participants in the two groups were matched for age, education, gender distribution, and cognitive capability. Their verbal and visual memory functions were assessed using standardized neuropsychological tests and EEG was used to record their brain activity during the verbal memory encoding phase. Consistent with previous studies, the MT group demonstrated better verbal memory than the NMT group during both the learning and the delayed recall trials in the paper-and-pencil tests. The MT group also exhibited greater learning capacity during the learning trials. Compared with the NMT group, the MT group showed an increase in long-range left and right intrahemispheric EEG coherence in the theta frequency band during the verbal memory encoding phase. In addition, their event-related left intrahemispheric theta coherence was positively associated with subsequent verbal memory performance as measured by discrimination scores. These results suggest that music training may modulate the cortical synchronization of the neural networks involved in verbal memory formation.

  6. 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.

  7. 'You don't show everyone your weakness': Older adults' views on using Family Group Conferencing to regain control and autonomy.

    Science.gov (United States)

    Metze, Rosalie N; Kwekkeboom, Rick H; Abma, Tineke A

    2015-08-01

    Family Group Conferencing (FGC), a model in which a person and his or her social network make their own 'care' plan, is used in youth care and might also be useful in elderly care to support older persons living at home. In Amsterdam, the Netherlands, FGC was implemented for older adults but they showed resistance. Reasons for this resistance have been researched and are described in this article. We examine existing views and attitudes of older adults concerning the use of FGC, and report on how older adults see the possibility to regain control over their lives using FGC. To do this, focus group sessions, duo interviews and individual interviews were held with older adults with varying characteristics: living at home, in sheltered housing, or in a home for the elderly; and living in urban, suburban or rural areas. Themes were: views on and contentment with the control and autonomy that they experience in their lives, and the willingness to use FGC to improve this. The main reasons for our respondents to resist FGC were: expecting people to be there for them without a FGC, not feeling ready yet for a FGC, feeling embarrassed when asking for help, being reluctant to open up about their problems, and having the fear of losing control when organizing a FGC. We conclude that, for this generation of older adults, FGC means losing control and autonomy rather than gaining it. To be appealing to older adults, a relational empowerment strengthening model should most likely be focused on reciprocity, peer-to-peer support, and solutions instead of problems. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Slower EEG alpha generation, synchronization and "flow"-possible biomarkers of cognitive impairment and neuropathology of minor stroke.

    Science.gov (United States)

    Petrovic, Jelena; Milosevic, Vuk; Zivkovic, Miroslava; Stojanov, Dragan; Milojkovic, Olga; Kalauzi, Aleksandar; Saponjic, Jasna

    2017-01-01

    We investigated EEG rhythms, particularly alpha activity, and their relationship to post-stroke neuropathology and cognitive functions in the subacute and chronic stages of minor strokes. We included 10 patients with right middle cerebral artery (MCA) ischemic strokes and 11 healthy controls. All the assessments of stroke patients were done both in the subacute and chronic stages. Neurological impairment was measured using the National Institute of Health Stroke Scale (NIHSS), whereas cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA) and MoCA memory index (MoCA-MIS). The EEG was recorded using a 19 channel EEG system with standard EEG electrode placement. In particular, we analyzed the EEGs derived from the four lateral frontal (F3, F7, F4, F8), and corresponding lateral posterior (P3, P4, T5, T6) electrodes. Quantitative EEG analysis included: the group FFT spectra, the weighted average of alpha frequency (αAVG), the group probability density distributions of all conventional EEG frequency band relative amplitudes (EEG microstructure), the inter- and intra-hemispheric coherences, and the topographic distribution of alpha carrier frequency phase potentials (PPs). Statistical analysis was done using a Kruskal-Wallis ANOVA with a post-hoc Mann-Whitney U two-tailed test, and Spearman's correlation. We demonstrated transient cognitive impairment alongside a slower alpha frequency ( α AVG) in the subacute right MCA stroke patients vs. the controls. This slower alpha frequency showed no amplitude change, but was highly synchronized intra-hemispherically, overlying the ipsi-lesional hemisphere, and inter-hemispherically, overlying the frontal cortex. In addition, the disturbances in EEG alpha activity in subacute stroke patients were expressed as a decrease in alpha PPs over the frontal cortex and an altered "alpha flow", indicating the sustained augmentation of inter-hemispheric interactions. Although the stroke induced slower alpha was a

  9. Slower EEG alpha generation, synchronization and “flow”—possible biomarkers of cognitive impairment and neuropathology of minor stroke

    Directory of Open Access Journals (Sweden)

    Jelena Petrovic

    2017-09-01

    Full Text Available Background We investigated EEG rhythms, particularly alpha activity, and their relationship to post-stroke neuropathology and cognitive functions in the subacute and chronic stages of minor strokes. Methods We included 10 patients with right middle cerebral artery (MCA ischemic strokes and 11 healthy controls. All the assessments of stroke patients were done both in the subacute and chronic stages. Neurological impairment was measured using the National Institute of Health Stroke Scale (NIHSS, whereas cognitive functions were assessed using the Montreal Cognitive Assessment (MoCA and MoCA memory index (MoCA-MIS. The EEG was recorded using a 19 channel EEG system with standard EEG electrode placement. In particular, we analyzed the EEGs derived from the four lateral frontal (F3, F7, F4, F8, and corresponding lateral posterior (P3, P4, T5, T6 electrodes. Quantitative EEG analysis included: the group FFT spectra, the weighted average of alpha frequency (αAVG, the group probability density distributions of all conventional EEG frequency band relative amplitudes (EEG microstructure, the inter- and intra-hemispheric coherences, and the topographic distribution of alpha carrier frequency phase potentials (PPs. Statistical analysis was done using a Kruskal–Wallis ANOVA with a post-hoc Mann–Whitney U two-tailed test, and Spearman’s correlation. Results We demonstrated transient cognitive impairment alongside a slower alpha frequency (αAVG in the subacute right MCA stroke patients vs. the controls. This slower alpha frequency showed no amplitude change, but was highly synchronized intra-hemispherically, overlying the ipsi-lesional hemisphere, and inter-hemispherically, overlying the frontal cortex. In addition, the disturbances in EEG alpha activity in subacute stroke patients were expressed as a decrease in alpha PPs over the frontal cortex and an altered “alpha flow”, indicating the sustained augmentation of inter-hemispheric interactions

  10. Surface plasmon resonance analysis shows an IgG-isotype-specific defect in ABO blood group antibody formation in patients with common variable immunodeficiency

    Directory of Open Access Journals (Sweden)

    Michael Bernhard Fischer

    2015-05-01

    Full Text Available Background: Common variable immunodeficiency (CVID is the most common clinically severe primary immunodeficiency and comprises a heterogeneous group of patients with recurrent severe bacterial infections due to the failure to produce IgG antibodies after exposure to infectious agents and immunization. Diagnostic recommendations for antibody failure include assessment of isoagglutinins. We have readdressed this four decades old but still accepted recommendation with up to date methodology.Methods: Anti-A/B IgM- and IgG-antibodies were measured by Diamed-ID Micro Typing, surface plasmon resonance (SPR using the Biacore® device and flow cytometry.Results: When Diamed-ID Micro Typing was used, CVID patients (n=34 showed IgG- and IgM-isoagglutinins that were comparable to healthy volunteers (n=28, while all XLA patients (n=8 had none. Anti-A/B IgM-antibodies were present in more than 2/3 of the CVID patients and showed binding kinetics comparable to anti-A/B IgM-antibodies from healthy individuals. A correlation could be found in CVID patients between levels of anti-A/B IgM-antibodies and levels of serum IgM and PnP-IgM-antibodies. In contrast in CVID patients as a group ABO antibodies were significantly decreased when assessed by SPR, which correlated with levels of switched memory, non-switched memory and naïve B cells, but all CVID patients had low/undetectable anti-A/B IgG-antibodies.Conclusion: These results indicate that conventional isoagglutinin assessment and assessment of anti-A/B IgM antibodies are not suited for the diagnosis of impaired antibody production in CVID. Examination of anti-A/B IgG antibodies by SPR provides a useful method for the diagnosis of IgG antibody failure in all CVID patients studied, thus indicating an important additional rationale to start immunoglobulin replacement therapy early in these patients, before post-infectious sequelae develop.

  11. 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.

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

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    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.

  13. Magnetic resonance imaging in relation to EEG epileptic foci in tuberous sclerosis

    International Nuclear Information System (INIS)

    Tamaki, Kyoko; Okuno, Takehiko; Ito, Masatoshi; Asato, Reinin; Konishi, Junji; Mikawa, Haruki

    1990-01-01

    In 20 patients with tuberous sclerosis (TS), who were sequentially treated for epilepsy at our clinic, the high signal lesions in the cerebral cortex and subcortex detected on T2 weighted magnetic resonance imaging (MRI) were compared with the interictal EEG findings. In four cases who showed a unilateral distribution of the MRI lesions, there was a good correlation between the laterality of the affected lobes and the localization of the EEG epileptic foci. Thirteen cases with more than four affected lobes in both hemispheres also showed bilateral epileptic foci on EEG. The MRI lesions in the occipital lobes showed the best correlation with the EEG epileptic foci, while the worst correlation was seen in the frontal lobes. In addition, the cases with four or more affected lobes without laterality on MRI are more likely to show bilateral synchronization on EEG. The prognosis of epilepsy in these cases was found to be rather poor. (author)

  14. Optimizing microsurgical skills with EEG neurofeedback

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    Benjamin Larry

    2009-07-01

    Full Text Available Abstract Background By enabling individuals to self-regulate their brainwave activity in the field of optimal performance in healthy individuals, neurofeedback has been found to improve cognitive and artistic performance. Here we assessed whether two distinct EEG neurofeedback protocols could develop surgical skill, given the important role this skill plays in medicine. Results National Health Service trainee ophthalmic microsurgeons (N = 20 were randomly assigned to either Sensory Motor Rhythm-Theta (SMR or Alpha-Theta (AT groups, a randomized subset of which were also part of a wait-list 'no-treatment' control group (N = 8. Neurofeedback groups received eight 30-minute sessions of EEG training. Pre-post assessment included a skills lab surgical procedure with timed measures and expert ratings from video-recordings by consultant surgeons, together with state/trait anxiety self-reports. SMR training demonstrated advantages absent in the control group, with improvements in surgical skill according to 1 the expert ratings: overall technique (d = 0.6, p Conclusion SMR-Theta neurofeedback training provided significant improvement in surgical technique whilst considerably reducing time on task by 26%. There was also evidence that AT training marginally reduced total surgery time, despite suboptimal training efficacies. Overall, the data set provides encouraging evidence of optimised learning of a complex medical specialty via neurofeedback training.

  15. Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis.

    Science.gov (United States)

    Somers, Ben; Bertrand, Alexander

    2016-12-01

    Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. We apply a distributed canonical correlation analysis (CCA-)based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

  16. Classification of single normal and Alzheimer’s disease individuals from cortical sources of resting state EEG rhythms

    Directory of Open Access Journals (Sweden)

    Claudio eBabiloni

    2016-02-01

    Full Text Available Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG rhythms in groups of Alzheimer’s disease (AD compared to healthy elderly (Nold subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz, theta (4-8 Hz, alpha 1 (8-10.5 Hz, alpha 2 (10.5-13 Hz, beta 1 (13-20 Hz, beta 2 (20-30 Hz, and gamma (30-40 Hz were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC higher than 0.7 as a threshold for a moderate classification rate (i.e. 70%. Results showed that the following EEG markers overcame this threshold: (i central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii frontal theta/alpha 1 current density; (iv occipital delta/alpha 1 inter-hemispherical connectivity; (v occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%. These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

  17. CHANGES IN THE RAT EEG SPECTRA AND CORE TEMPERATURE AFTER EXPOSURE TO DIFFERENT DOSES OF CHLORPYRIFOS.

    Science.gov (United States)

    Our previous study showed that single exposure to 25 mg/kg (p.o.) of organophsphate pesticide chlorpyrifos (CHP) led to significant alterations in all EEG frequency bands within 0.1-50 Hz range, reduction in core temperature (Tc) and motor activity (MA). The alterations in EEG pe...

  18. Subdural to subgaleal EEG signal transmission: The role of distance, leakage and insulating affectors

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels Wesenberg; Madsen, Rasmus Elsborg

    2013-01-01

    Objective To estimate the area of cortex affecting the extracranial EEG signal. MethodsThe coherence between intra- and extracranial EEG channels were evaluated on at least 10min of spontaneous, awake data from seven patients admitted for epilepsy surgery work up. Results Cortical electrodes show...

  19. Hypoglycemia-Associated EEG Changes in Prepubertal Children With Type 1 Diabetes

    DEFF Research Database (Denmark)

    Hansen, Grith Lærkholm; Foli-Andersen, Pia; Fredheim, Siri

    2016-01-01

    were calculated. A within-patient analysis was conducted comparing hypoglycemia versus euglycemia changes in the qEEG. The nonparametric Wilcoxon signed rank test was performed. A real-time analyzing algorithm developed for adults was applied. RESULTS: The qEEG showed significant differences...

  20. EEG analysis of seizure patterns using visibility graphs for detection of generalized seizures

    NARCIS (Netherlands)

    Wang, Lei; Long, Xi; Arends, J.B.A.M.; Aarts, R.M.

    2017-01-01

    Background The traditional EEG features in the time and frequency domain show limited seizure detection performance in the epileptic population with intellectual disability (ID). In addition, the influence of EEG seizure patterns on detection performance was less studied. New method A single-channel

  1. Computerized spectral analyses of EEG in chronic schizophrenic patients

    International Nuclear Information System (INIS)

    Fujita, Haruhiro

    1985-01-01

    This study was aimed at clarifying the EEG difference between chronic schizophrenic patients and normal controls by using the EEG method of spectral analyses. Twelve comparatively homogenous chronic schizophrenic patients and the 10 healthy controls were subjected to EEG investigations. 1) The EEG of schizophrenic patients had a slowing tendency of the frequency in the frontal pole, anterior temporal and central regions of the scalp compared with control subjects. 2) There was a decrease of mutual relation among the five electrodes' peak frequency in the schizophrenic patients. 3) The EEG of schizophrenic patients had more fast waves of β 1 and β 2 band than that of control subjects. 4) A slowing tendency of the frequency in the first half regions of the scalp was not found in 3 chronic schizophrenic patients which showed defective functions in the frontal area by positron emission tomography. 5) When mental arithmetic was given, the schizophrenic patients showed an increase of fast wave in the central, posterior temporal and occipital regions of the scalp. 6) When they opened their eyes, attenuation in the α band was not so marked in the schizophrenic patients. (author)

  2. Scale-specific effects: A report on multiscale analysis of acupunctured EEG in entropy and power

    Science.gov (United States)

    Song, Zhenxi; Deng, Bin; Wei, Xile; Cai, Lihui; Yu, Haitao; Wang, Jiang; Wang, Ruofan; Chen, Yingyuan

    2018-02-01

    Investigating acupuncture effects contributes to improving clinical application and understanding neuronal dynamics under external stimulation. In this report, we recorded electroencephalography (EEG) signals evoked by acupuncture at ST36 acupoint with three stimulus frequencies of 50, 100 and 200 times per minutes, and selected non-acupuncture EEGs as the control group. Multiscale analyses were introduced to investigate the possible acupuncture effects on complexity and power in multiscale level. Using multiscale weighted-permutation entropy, we found the significant effects on increased complexity degree in EEG signals induced by acupuncture. The comparison of three stimulation manipulations showed that 100 times/min generated most obvious effects, and affected most cortical regions. By estimating average power spectral density, we found decreased power induced by acupuncture. The joint distribution of entropy and power indicated an inverse correlation, and this relationship was weakened by acupuncture effects, especially under the manipulation of 100 times/min frequency. Above findings are more evident and stable in large scales than small scales, which suggests that multiscale analysis allows evaluating significant effects in specific scale and enables to probe the inherent characteristics underlying physiological signals.

  3. Novel screen printed electrode set for routine EEG recordings in patients with altered mental status.

    Science.gov (United States)

    Myllymaa, Sami; Lepola, Pasi; Hukkanen, Taina; Oun, Andre; Mervaala, Esa; Toyras, Juha; Lappalainen, Reijo; Myllymaa, Katja

    2013-01-01

    There is a growing need for an easy to use screening tool for the assessment of brain's electrical function in patients with altered mental status (AMS). The purpose of this study is to give a brief overview of the state-of-the-art in electrode technology, and to present a novel sub-hairline electrode set developed in our research group. Screen-printing technology was utilized to construct the electrode set consisting of ten electroencephalography (EEG) electrodes, two electrooculography (EOG) electrodes, two ground electrodes and two reference electrodes. Electrical characteristics of hydrogel-coated silver ink electrodes were found adequate for clinical EEG recordings as assessed by electrical impedance spectroscopy (EIS). The skin-electrode impedances remain stable and low enough at least two days enabling high-quality long-term recordings. Due to the proper material selection, thin ink layers and detachable zero insertion force (ZIF) - connector, electrode was observed to be CT- and MRI-compatible allowing imaging without removing the electrodes. Pilot EEG recordings gave very promising results and an on-going clinical trial with larger number of patients will show the true feasibility of this approach.

  4. Single-trial EEG-informed fMRI reveals spatial dependency of BOLD signal on early and late IC-ERP amplitudes during face recognition.

    Science.gov (United States)

    Wirsich, Jonathan; Bénar, Christian; Ranjeva, Jean-Philippe; Descoins, Médéric; Soulier, Elisabeth; Le Troter, Arnaud; Confort-Gouny, Sylviane; Liégeois-Chauvel, Catherine; Guye, Maxime

    2014-10-15

    Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital-temporal-frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. The EEG response to the repromulgated standard and compliance process

    International Nuclear Information System (INIS)

    Neill, R.H.; Chaturvedi, L.; Lee, W.W.L.

    1993-01-01

    Since 1978, the New Mexico Environmental Evaluation Group (EEG) has provided an independent technical evaluation of the Waste Isolation Pilot Plant (WIPP) to ensure protection of the public health and safety of New Mexicans and protection of the environment in New Mexico. Through its technical competence and continuity, the EEG has had a major influence on the course of the WIPP. This paper summarizes our views on the 1993 repromulgation of the general environmental standards for high-level and transuranic waste disposal and the certification for compliance with the standard

  6. Using of the interictal EEGs for epilepsy diagnosing

    International Nuclear Information System (INIS)

    Panischev, O Yu; Demin, S A; Zinatullin, E M

    2015-01-01

    In this work we apply a new method to determine the differences in characteristics of the cortical electroencephalographic (EEG) activity, measured during interictal stage (i.e., period between seizures), between healthy subjects and patients with epilepsy. To analyze the dynamical and spectral properties of bioelectric activity we use power spectra and phase portraits which are introduced on the basis of the Memory Function Formalism (MFF). We discover the significant differences in the types of power spectra of the EEG for healthy subjects and patients. We reveal the cerebral cortex areas for which the EEG activity of considered groups of subjects has a different structure of the phase portraits. The proposed approach can be used as an additional method for diagnosis of epilepsy during interictal stage. (paper)

  7. Brain perfusion SPECT and EEG findings in Rett syndrome

    International Nuclear Information System (INIS)

    Lappalainen, R.; Liewendahl, K.; Nikkinen, P.; Sainio, K.; Riikonen, R.S.

    1997-01-01

    Thirteen patients (mean age 8.4 + 5.3 years) with Rett syndrome (RS) were studied with EEG and 99m Tc-HMPAO SPECT. Eleven patients had background abnormalities and 10 patients paroxysmal activity in EEG. Hypoperfusion of varying severity was detected in 11 patients, 7 patients having multiple lesions. Bifrontal hypoperfusion, observed in 6 patients, was the most distinctive finding. Hypoperfusion was observed also in other cortical regions, except for the occipital lobes. There was no correlation between severity of the background abnormality or presence of paroxysmal activity in EEG and grade of hypoperfusion. There was, however, an association between the severity of hypoperfusion and early manifestation of symptoms in patients with RS. Whether this early-onset group of patients represents a different disease entity or only reflects disease variability the basic pathology being the same, is a possibility that deserves further clarification. (au) 37 refs

  8. EEG controls for detecting the recurrence of supratentorial gliomas

    International Nuclear Information System (INIS)

    Leblhuber, F.; Olschowski, A.; Deisenhammer, E.; Hammer, B.; Knauer, W.

    1984-01-01

    The purpose of this study was to find out the value of postoperative EEG controls in the early detection of recurrence of supratentorial gliomas (the majority being astrocytomas, stage II to IV). 29 cases with verified tumour recurrence were examined and in all but one the EEG showed a reactivation of the focus in accordance with the development of the glioma. At least one of the following parameters had to be established: 1. a further spreading of the focal changes, 2. a reduction in frequency, 3. an increase in amplitudes and 4. focal depression and amplitudes. At least 3 postoperative EEG controls were made in each case. The duration of tumour treatment was 3 to 59 months. In 3 cases temporary focus activation was found without evidence of tumour recurrence; in one of these cases the activation was preceded by an epileptic seizure. Epileptic seizures, thus, seem to have a focus activating effect. Focus activation as a result of radiotherapy or cytostatic treatment was not observed. On the basis of our findings it appears that regularly conducted postoperative EEG controls seem to be highly suited as a non-invasive and economical method for the early detection of recurrence of this type of tumour. In the case of malignant types of gliomas involving rapid growth EEG controls should be made monthly. (Author)

  9. Quantitative change of EEG and respiration signals during mindfulness meditation

    Science.gov (United States)

    2014-01-01

    Background This study investigates measures of mindfulness meditation (MM) as a mental practice, in which a resting but alert state of mind is maintained. A population of older people with high stress level participated in this study, while electroencephalographic (EEG) and respiration signals were recorded during a MM intervention. The physiological signals during meditation and control conditions were analyzed with signal processing. Methods EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis, phase analysis and classification to evaluate an objective marker for meditation. Results Different frequency bands showed differences in meditation and control conditions. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy (85%) at discriminating between meditation and control conditions than a classifier using the EEG signal only (78%). Conclusion Support vector machine (SVM) classifier with EEG and respiration feature vector is a viable objective marker for meditation ability. This classifier should be able to quantify different levels of meditation depth and meditation experience in future studies. PMID:24939519

  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. Beamforming applied to surface EEG improves ripple visibility.

    Science.gov (United States)

    van Klink, Nicole; Mol, Arjen; Ferrier, Cyrille; Hillebrand, Arjan; Huiskamp, Geertjan; Zijlmans, Maeike

    2018-01-01

    Surface EEG can show epileptiform ripples in people with focal epilepsy, but identification is impeded by the low signal-to-noise ratio of the electrode recordings. We used beamformer-based virtual electrodes to improve ripple identification. We analyzed ten minutes of interictal EEG of nine patients with refractory focal epilepsy. EEGs with more than 60 channels and 20 spikes were included. We computed ∼79 virtual electrodes using a scalar beamformer and marked ripples (80-250 Hz) co-occurring with spikes in physical and virtual electrodes. Ripple numbers in physical and virtual electrodes were compared, and sensitivity and specificity of ripples for the region of interest (ROI; based on clinical information) were determined. Five patients had ripples in the physical electrodes and eight in the virtual electrodes, with more ripples in virtual than in physical electrodes (101 vs. 57, p = .007). Ripples in virtual electrodes predicted the ROI better than physical electrodes (AUC 0.65 vs. 0.56, p = .03). Beamforming increased ripple visibility in surface EEG. Virtual ripples predicted the ROI better than physical ripples, although sensitivity was still poor. Beamforming can facilitate ripple identification in EEG. Ripple localization needs to be improved to enable its use for presurgical evaluation in people with epilepsy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  12. Posture Used in fMRI-PET Elicits Reduced Cortical Activity and Altered Hemispheric Asymmetry with Respect to Sitting Position: An EEG Resting State Study

    Directory of Open Access Journals (Sweden)

    Chiara Spironelli

    2017-12-01

    Full Text Available Horizontal body position is a posture typically adopted for sleeping or during brain imaging recording in both neuroscience experiments and diagnostic situations. Recent literature showed how this position and similar ones with head down are associated to reduced plasticity, impaired pain and emotional responses. The present study aimed at further understanding the decrease of cortical activity associated with horizontal body position by measuring high-frequency EEG bands – typically associated with high-level cognitive activation – in a resting state experimental condition. To this end, two groups of 16 female students were randomly assigned to either sitting control (SC or 2-h horizontal Bed Rest condition (hBR while EEG was recorded from 38 scalp recording sites. The hBR group underwent several body transitions, from sitting to supine, and from supine to sitting. Results revealed a clear effect of horizontal posture: the hBR group showed, compared to its baseline and to SC, reduced High-Beta and Gamma EEG band amplitudes throughout the 2-h of hBR condition. In addition, before and after the supine condition, hBR group as well as SC exhibited a greater left vs. right frontal activation in both EEG bands while, on the contrary, the supine position induced a bilateral and reduced activation in hBR participants. The cortical sources significantly more active in SC compared with hBR participants included the left Inferior Frontal Gyrus and left Insula. Results are discussed in relation to the differences among neuroimaging methods (e.g., fMRI, EEG, NIRS, which can be partially explained by posture-induced neural network changes.

  13. Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

    Science.gov (United States)

    Lahmiri, Salim

    2018-01-01

    The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.

  14. Evaluation of artifact-corrected electroencephalographic (EEG) training: a pilot study.

    Science.gov (United States)

    La Marca, Jeffry P; Cruz, Daniel; Fandino, Jennifer; Cacciaguerra, Fabiana R; Fresco, Joseph J; Guerra, Austin T

    2018-07-01

    This double-blind study examined the effect of electromyographical (EMG) artifacts, which contaminate electroencephalographical (EEG) signals, by comparing artifact-corrected (AC) and non-artifact-corrected (NAC) neurofeedback (NF) training procedures. 14 unmedicated college students were randomly assigned to two groups: AC (n = 7) or NAC (n = 7). Both groups received 12 sessions of NF and were trained using identical NF treatment protocols to reduce their theta/beta power ratios (TBPR). Outcomes on a continuous performance test revealed that the AC group had statistically significant increases across measures of auditory and visual attention. The NAC group showed smaller gains that only reached statistical significance on measures of visual attention. Only the AC group reduced their TBPR, the NAC group did not. AC NF appears to play an important role during training that leads to improvements in both auditory and visual attention. Additional research is required to confirm the results of this study.

  15. Analysis of correlation between white matter changes and functional responses in thalamic stroke: a DTI & EEG study.

    Science.gov (United States)

    Duru, Adil Deniz; Duru, Dilek Göksel; Yumerhodzha, Sami; Bebek, Nerses

    2016-06-01

    Diffusion tensor imaging (DTI) allows in vivo structural brain mapping and detection of microstructural disruption of white matter (WM). One of the commonly used parameters for grading the anisotropic diffusivity in WM is fractional anisotropy (FA). FA value helps to quantify the directionality of the local tract bundle. Therefore, FA images are being used in voxelwise statistical analyses (VSA). The present study used Tract-Based Spatial Statistics (TBSS) of FA images across subjects, and computes the mean skeleton map to detect voxelwise knowledge of the tracts yielding to groupwise comparison. The skeleton image illustrates WM structure and shows any changes caused by brain damage. The microstructure of WM in thalamic stroke is investigated, and the VSA results of healthy control and thalamic stroke patients are reported. It has been shown that several skeleton regions were affected subject to the presence of thalamic stroke (FWE, p EEG (qEEG) scores and neurophysiological tests with the FA skeleton for the entire test group is also investigated. We compared measurements that are related to the same fibers across subjects, and discussed implications for VSA of WM in thalamic stroke cases, for the relationship between behavioral tests and FA skeletons, and for the correlation between the FA maps and qEEG scores.Results obtained through the regression analyses did not exceed the corrected statistical threshold values for multiple comparisons (uncorrected, p EEG, cingulum bundle and corpus callosum were found to be related. These areas are parts of the Default Mode Network (DMN) where DMN is known to be involved in resting state EEG theta activity. The relation between the EEG alpha band power values and FA values of the skeleton was found to support the cortico-thalamocortical cycles for both subject groups. Further, the neurophysiological tests including Benton Face Recognition (BFR), Digit Span test (DST), Warrington Topographic Memory test (WTMT

  16. Solving of L0 norm constrained EEG inverse problem.

    Science.gov (United States)

    Xu, Peng; Lei, Xu; Hu, Xiao; Yao, Dezhong

    2009-01-01

    l(0) norm is an effective constraint used to solve EEG inverse problem for a sparse solution. However, due to the discontinuous and un-differentiable properties, it is an open issue to solve the l(0) norm constrained problem, which is usually instead solved by using some alternative functions like l(1) norm to approximate l(0) norm. In this paper, a continuous and differentiable function having the same form as the transfer function of Butterworth low-pass filter is introduced to approximate l(0) norm constraint involved in EEG inverse problem. The new approximation based approach was compared with l(1) norm and LORETA solutions on a realistic head model using simulated sources. The preliminary results show that this alternative approximation to l(0) norm is promising for the estimation of EEG sources with sparse distribution.

  17. Multireference adaptive noise canceling applied to the EEG.

    Science.gov (United States)

    James, C J; Hagan, M T; Jones, R D; Bones, P J; Carroll, G J

    1997-08-01

    The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroeancephalogram (EEG), with the adaptation implemented by means of a multilayer-perception artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

  18. 'You don't show everyone your weakness': Older adults' views on using Family Group Conferencing to regain control and autonomy

    NARCIS (Netherlands)

    Metze, R.N.; Kwekkeboom, R.H.; Abma, T.A.

    2015-01-01

    Aim: Family Group Conferencing (FGC), a model in which a person and his or her social network make their own 'care' plan, is used in youth care and might also be useful in elderly care to support older persons living at home. In Amsterdam, the Netherlands, FGC was implemented for older adults but

  19. Comprehensive genotyping for 18 blood group systems using a multiplex ligation-dependent probe amplification assay shows a high degree of accuracy

    NARCIS (Netherlands)

    Haer-Wigman, Lonneke; Ji, Yanli; Lodén, Martin; de Haas, Masja; van der Schoot, C. Ellen; Veldhuisen, Barbera

    2013-01-01

    In recent years genotyping methods have been implemented in blood banks as alternative to comprehensive serologic typing. We evaluated a newly developed assay for convenient and comprehensive genotyping of blood group alleles based on multiplex ligation-dependent probe amplification (MLPA)

  20. EEG Findings in Burnout Patients

    NARCIS (Netherlands)

    Luijtelaar, E.L.J.M. van; Verbraak, M.J.P.M.; Bunt, P.M. van den; Keijsers, G.P.J.; Arns, M.W.

    2010-01-01

    The concept of burnout remains enigmatic since it is only determined by behavioral characteristics. Moreover, the differential diagnosis with depression and chronic fatigue syndrome is difficult. EEG-related variables in 13 patients diagnosed with burnout syndrome were compared with 13 healthy

  1. The antinociceptive effects of Monechma ciliatum and changes in EEG waves following oral and intrathecal administration in rats

    Science.gov (United States)

    Meraiyebu, Ajibola B.; Adelaiye, Alexander B.; O, Odeh S.

    2010-02-01

    The research work was carried out to study the effect of Oral and Intrathecal Monechma Ciliatum on antinociception and EEG readings in Wistar Rats. Traditionally the extract is given to women in labour believed to reduce pain and ease parturition, though past works show that it has oesteogenic and oxytotic effects. The rats were divided into 5 major groups. Group 1 served as oral control group while groups 2 and 3 served as oral experimental groups and were treated with 500mg/kg and 1000mg/kg monechma ciliatum respectively. Group 4 served as intrathecal control group treated with intrathecal dextrose and group 5 received 1000mg/kg Monechma Ciliatrum intrathecally. The antinociceptive effect was analysed using a Von Frey's aesthesiometer. Monechma Ciliatum showed significant antinociceptive effect both orally and intrathecally, although it had a greater effect orally and during the first 15 minutes of intrathecal administration. EEG readings were also taken for all the groups and there was a decrease in amplitude and an increase in frequency for high dose (1000mg/ml) experimental groups and the mid brain electrodes produced a change from theta waves (3.5 - 7 waves per second) to alpha waves (7.5 - 13 waves per second) as seen in relaxed persons and caused decreased amplitudes and change in distribution seen in beta waves. Properties similarly accentuated by sedativehypnotic drugs.

  2. 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.

  3. Cerebral perfusion abnormalities in therapy-resistant epilepsy in childhood: comparison between EEG, MRI and 99Tcm-ECD brain SPET.

    Science.gov (United States)

    Vattimo, A; Burroni, L; Bertelli, P; Volterrani, D; Vella, A

    1996-01-01

    We performed 99Tcm-ethyl cysteinate dimer (ECD) interictal single photon emission tomography (SPET) in 26 children with severe therapy-resistant epilepsy. All the children underwent a detailed clinical examination, an electroencephalogram (EEG) investigation and brain magnetic resonance imaging (MRI). In 21 of the 26 children, SPET demonstrated brain blood flow abnormalities, in 13 cases in the same territories that showed EEG alterations. MRI showed structural lesions in 6 of the 26 children, while SPET imaging confirmed these abnormalities in only 5 children. The lesion not detected on SPET was shown to be 3 mm thick on MRI. Five symptomatic patients had normal SPET. In one of these patients, the EEG findings were normal and MRI revealed a small calcific nodule (4 mm thick); in the others, the EEG showed non-focal but diffuse abnormalities. These data confirm that brain SPET is sensitive in detecting and localizing hypoperfused areas that could be associated with epileptic foci in this group of patients, even when the MRI image is normal.

  4. Automatic detection of rhythmic and periodic patterns in critical care EEG based on American Clinical Neurophysiology Society (ACNS) standardized terminology.

    Science.gov (United States)

    Fürbass, F; Hartmann, M M; Halford, J J; Koren, J; Herta, J; Gruber, A; Baumgartner, C; Kluge, T

    2015-09-01

    Continuous EEG from critical care patients needs to be evaluated time efficiently to maximize the treatment effect. A computational method will be presented that detects rhythmic and periodic patterns according to the critical care EEG terminology (CCET) of the American Clinical Neurophysiology Society (ACNS). The aim is to show that these detected patterns support EEG experts in writing neurophysiological reports. First of all, three case reports exemplify the evaluation procedure using graphically presented detections. Second, 187 hours of EEG from 10 critical care patients were used in a comparative trial study. For each patient the result of a review session using the EEG and the visualized pattern detections was compared to the original neurophysiology report. In three out of five patients with reported seizures, all seizures were reported correctly. In two patients, several subtle clinical seizures with unclear EEG correlation were missed. Lateralized periodic patterns (LPD) were correctly found in 2/2 patients and EEG slowing was correctly found in 7/9 patients. In 8/10 patients, additional EEG features were found including LPDs, EEG slowing, and seizures. The use of automatic pattern detection will assist in review of EEG and increase efficiency. The implementation of bedside surveillance devices using our detection algorithm appears to be feasible and remains to be confirmed in further multicenter studies. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  5. Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data

    Science.gov (United States)

    Puthanmadam Subramaniyam, Narayan; Hyttinen, Jari

    2014-10-01

    In this letter, we study the influence of observational noise on recurrence network (RN) measures, the global clustering coefficient (C) and average path length (L) using the Rössler system and propose the application of RN measures to analyze the structural properties of electroencephalographic (EEG) data. We find that for an appropriate recurrence rate (RR>0.02) the influence of noise on C can be minimized while L is independent of RR for increasing levels of noise. Indications of structural complexity were found for healthy EEG, but to a lesser extent than epileptic EEG. Furthermore, C performed better than L in case of epileptic EEG. Our results show that RN measures can provide insights into the structural properties of EEG in normal and pathological states.

  6. The value of CT and EEG in cases of posttraumatic epilepsy

    International Nuclear Information System (INIS)

    Reisner, T.; Zeiler, K.; Wessely, P.

    1979-01-01

    The results of the clinical neurological investigation were compared with those of electroencephalography (EEG) and computed tomography (CT) in 64 patients suffering from verified posttraumatic epilepsy. Only 18 patients (28%) showed central neurological features with corresponding focal disorders on CT (15 cases) and EEG (11 cases). EEG results were normal in 12 cases (19%), CT in 14 cases (22%). The combined application of both methods led to positive results in 94% on the part of at least one accessory examination. The clinical neurological investigation as well as the EEG and CT were normal in only 3 cases, although the traumatic etiology of epilepsy was beyond doubt. In addition to the clinical neurological investigation, EEG and CT are most important accessory examinations for the diagnosis and followup studies in cases of posttraumatic epilepsy. (orig.) [de

  7. Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data

    International Nuclear Information System (INIS)

    Puthanmadam Subramaniyam, Narayan; Hyttinen, Jari

    2014-01-01

    In this letter, we study the influence of observational noise on recurrence network (RN) measures, the global clustering coefficient (C) and average path length (L) using the Rössler system and propose the application of RN measures to analyze the structural properties of electroencephalographic (EEG) data. We find that for an appropriate recurrence rate (RR>0.02) the influence of noise on C can be minimized while L is independent of RR for increasing levels of noise. Indications of structural complexity were found for healthy EEG, but to a lesser extent than epileptic EEG. Furthermore, C performed better than L in case of epileptic EEG. Our results show that RN measures can provide insights into the structural properties of EEG in normal and pathological states. - Highlights: • We study the influence of noise on the recurrence network measures C and L. • We propose the application of C and L to healthy and epileptic EEG data. • The influence of noise can be minimized by increasing the recurrence rate. • Measures C and L can describe the structural complexity of EEG data. • In case of epileptic EEG, C performs better than L

  8. Characterization of dynamical systems under noise using recurrence networks: Application to simulated and EEG data

    Energy Technology Data Exchange (ETDEWEB)

    Puthanmadam Subramaniyam, Narayan, E-mail: npsubramaniyam@gmail.com [Department of Electronics and Communications, Tampere University of Technology, Tampere (Finland); BioMediTech, Tampere (Finland); Hyttinen, Jari [Department of Electronics and Communications, Tampere University of Technology, Tampere (Finland); BioMediTech, Tampere (Finland)

    2014-10-24

    In this letter, we study the influence of observational noise on recurrence network (RN) measures, the global clustering coefficient (C) and average path length (L) using the Rössler system and propose the application of RN measures to analyze the structural properties of electroencephalographic (EEG) data. We find that for an appropriate recurrence rate (RR>0.02) the influence of noise on C can be minimized while L is independent of RR for increasing levels of noise. Indications of structural complexity were found for healthy EEG, but to a lesser extent than epileptic EEG. Furthermore, C performed better than L in case of epileptic EEG. Our results show that RN measures can provide insights into the structural properties of EEG in normal and pathological states. - Highlights: • We study the influence of noise on the recurrence network measures C and L. • We propose the application of C and L to healthy and epileptic EEG data. • The influence of noise can be minimized by increasing the recurrence rate. • Measures C and L can describe the structural complexity of EEG data. • In case of epileptic EEG, C performs better than L.

  9. Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA.

    Science.gov (United States)

    Sai, Chong Yeh; Mokhtar, Norrima; Arof, Hamzah; Cumming, Paul; Iwahashi, Masahiro

    2018-05-01

    Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain-computer interface applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal. We now propose a novel approach for identifying artifactual components separated by wavelet-ICA using a pretrained support vector machine (SVM). Our method presents a robust and extendable system that enables fully automated identification and removal of artifacts from EEG signals, without applying any arbitrary thresholding. Using test data contaminated by eye blink artifacts, we show that our method performed better in identifying artifactual components than did existing thresholding methods. Furthermore, wavelet-ICA in conjunction with SVM successfully removed target artifacts, while largely retaining the EEG source signals of interest. We propose a set of features including kurtosis, variance, Shannon's entropy, and range of amplitude as training and test data of SVM to identify eye blink artifacts in EEG signals. This combinatorial method is also extendable to accommodate multiple types of artifacts present in multichannel EEG. We envision future research to explore other descriptive features corresponding to other types of artifactual components.

  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. PyEEG: an open source Python module for EEG/MEG feature extraction.

    Science.gov (United States)

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.

  12. Psychogenic seizures and frontal disconnection: EEG synchronisation study.

    Science.gov (United States)

    Knyazeva, Maria G; Jalili, Mahdi; Frackowiak, Richard S; Rossetti, Andrea O

    2011-05-01

    Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.

  13. Sparse Source EEG Imaging with the Variational Garrote

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai

    2013-01-01

    EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutions...

  14. Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats.

    Science.gov (United States)

    Ouyang, Gaoxiang; Li, Xiaoli; Dang, Chuangyin; Richards, Douglas A

    2008-08-01

    Understanding the transition of brain activity towards an absence seizure is a challenging task. In this paper, we use recurrence quantification analysis to indicate the deterministic dynamics of EEG series at the seizure-free, pre-seizure and seizure states in genetic absence epilepsy rats. The determinism measure, DET, based on recurrence plot, was applied to analyse these three EEG datasets, each dataset containing 300 single-channel EEG epochs of 5-s duration. Then, statistical analysis of the DET values in each dataset was carried out to determine whether their distributions over the three groups were significantly different. Furthermore, a surrogate technique was applied to calculate the significance level of determinism measures in EEG recordings. The mean (+/-SD) DET of EEG was 0.177+/-0.045 in pre-seizure intervals. The DET values of pre-seizure EEG data are significantly higher than those of seizure-free intervals, 0.123+/-0.023, (Pdeterminism in EEG epochs was present in 25 of 300 (8.3%), 181 of 300 (60.3%) and 289 of 300 (96.3%) in seizure-free, pre-seizure and seizure intervals, respectively. Results provide some first indications that EEG epochs during pre-seizure intervals exhibit a higher degree of determinism than seizure-free EEG epochs, but lower than those in seizure EEG epochs in absence epilepsy. The proposed methods have the potential of detecting the transition between normal brain activity and the absence seizure state, thus opening up the possibility of intervention, whether electrical or pharmacological, to prevent the oncoming seizure.

  15. Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms

    Directory of Open Access Journals (Sweden)

    Yvonne Höller

    2017-09-01

    Full Text Available Single photon emission computed tomography (SPECT and Electroencephalography (EEG have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD, 69 patients with depressive cognitive impairment (DCI, 71 patients with amnestic mild cognitive impairment (aMCI, and 41 patients with amnestic subjective cognitive complaints (aSCC. We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%, aMCI vs. AD (70%, and AD vs. DCI (100%, while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82% and aMCI vs. DCI (90%. Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%. In general, accuracies were higher when measures of interaction (i.e., connectivity measures were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become

  16. Reduced Temporal-Central EEG Alpha Coherence during Joint Attention Perception in Adolescents with Autism Spectrum Disorder

    OpenAIRE

    Jaime, Mark; McMahon, Camilla M.; Davidson, Bridget C.; Newell, Lisa C.; Mundy, Peter C.; Henderson, Heather A.

    2016-01-01

    Although prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal?central alpha coherenc...

  17. 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.

  18. Effect of Low-Level Laser Stimulation on EEG Power in Normal Subjects with Closed Eyes

    Directory of Open Access Journals (Sweden)

    Jih-Huah Wu

    2013-01-01

    Full Text Available In a previous study, we found that the low-level laser (LLL stimulation at the palm with a frequency of 10 Hz was able to induce significant brain activation in normal subjects with opened eyes. However, the electroencephalography (EEG changes to LLL stimulation in subjects with closed eyes have not been studied. In the present study, the laser array stimulator was applied to deliver insensible laser stimulations to the palm of the tested subjects with closed eyes (the laser group. The EEG activities before, during, and after the laser stimulation were collected. The EEG amplitude powers of each EEG frequency band at 19 locations were calculated. These power data were then analyzed by SPSS software using repeated-measure ANOVAs and appropriate posthoc tests. We found a pronounced decrease in the EEG power in alpha-bandwidth during laser simulation and then less decrease in the EEG power in delta-bandwidth in normal subjects with laser stimulation. The EEG power in beta-bandwidth in the right occipital area also decreased significantly in the laser group. We suggest that LLL stimulation might be conducive to falling into sleep in patients with sleep problems.

  19. 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.

  20. Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study.

    Science.gov (United States)

    Imperatori, Claudio; Farina, Benedetto; Quintiliani, Maria Isabella; Onofri, Antonio; Castelli Gattinara, Paola; Lepore, Marta; Gnoni, Valentina; Mazzucchi, Edoardo; Contardi, Anna; Della Marca, Giacomo

    2014-10-01

    The aim of the present study was to explore the modifications of EEG power spectra and EEG connectivity of resting state (RS) condition in patients with post-traumatic stress disorder (PTSD). Seventeen patients and seventeen healthy subjects matched for age and gender were enrolled. EEG was recorded during 5min of RS. EEG analysis was conducted by means of the standardized Low Resolution Electric Tomography software (sLORETA). In power spectra analysis PTSD patients showed a widespread increase of theta activity (4.5-7.5Hz) in parietal lobes (Brodmann Area, BA 7, 4, 5, 40) and in frontal lobes (BA 6). In the connectivity analysis PTSD patients also showed increase of alpha connectivity (8-12.5Hz) between the cortical areas explored by Pz-P4 electrode. Our results could reflect the alteration of memory systems and emotional processing consistently altered in PTSD patients. Copyright © 2014 Elsevier B.V. All rights reserved.

  1. 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.

  2. Pediatric primary central nervous system germ cell tumors of different prognosis groups show characteristic miRNome traits and chromosome copy number variations

    Directory of Open Access Journals (Sweden)

    Liang Muh-Lii

    2010-02-01

    Full Text Available Abstract Background Intracranial pediatric germ cell tumors (GCTs are rare and heterogeneous neoplasms and vary in histological differentiation, prognosis and clinical behavior. Germinoma and mature teratoma are GCTs that have a good prognosis, while other types of GCTs, termed nongerminomatous malignant germ cell tumors (NGMGCTs, are tumors with an intermediate or poor prognosis. The second group of tumors requires more extensive drug and irradiation treatment regimens. The mechanisms underlying the differences in incidence and prognosis of the various GCT subgroups are unclear. Results We identified a distinct mRNA profile correlating with GCT histological differentiation and prognosis, and also present in this study the first miRNA profile of pediatric primary intracranial GCTs. Most of the differentially expressed miRNAs were downregulated in germinomas, but miR-142-5p and miR-146a were upregulated. Genes responsible for self-renewal (such as POU5F1 (OCT4, NANOG and KLF4 and the immune response were abundant in germinomas, while genes associated with neuron differentiation, Wnt/β-catenin pathway, invasiveness and epithelial-mesenchymal transition (including SNAI2 (SLUG and TWIST2 were abundant in NGMGCTs. Clear transcriptome segregation based on patient survival was observed, with malignant NGMGCTs being closest to embryonic stem cells. Chromosome copy number variations (CNVs at cytobands 4q13.3-4q28.3 and 9p11.2-9q13 correlated with GCT malignancy and clinical risk. Six genes (BANK1, CXCL9, CXCL11, DDIT4L, ELOVL6 and HERC5 within 4q13.3-4q28.3 were more abundant in germinomas. Conclusions Our results integrate molecular profiles with clinical observations and provide insights into the underlying mechanisms causing GCT malignancy. The genes, pathways and microRNAs identified have the potential to be novel therapeutic targets.

  3. Anterior EEG asymmetries and opponent process theory.

    Science.gov (United States)

    Kline, John P; Blackhart, Ginette C; Williams, William C

    2007-03-01

    The opponent process theory of emotion [Solomon, R.L., and Corbit, J.D. (1974). An opponent-process theory of motivation: I. Temporal dynamics of affect. Psychological Review, 81, 119-143.] predicts a temporary reversal of emotional valence during the recovery from emotional stimulation. We hypothesized that this affective contrast would be apparent in asymmetrical activity patterns in the frontal lobes, and would be more apparent for left frontally active individuals. The present study tested this prediction by examining EEG asymmetries during and after blocked presentations of aversive pictures selected from the International Affective Picture System (IAPS). 12 neutral images, 12 aversive images, and 24 neutral images were presented in blocks. Participants who were right frontally active at baseline did not show changes in EEG asymmetry while viewing aversive slides or after cessation. Participants left frontally active at baseline, however, exhibited greater relative left frontal activity after aversive stimulation than before stimulation. Asymmetrical activity patterns in the frontal lobes may relate to affect regulatory processes, including contrasting opponent after-reactions to aversive stimuli.

  4. Schizophrenia patients and 22q11.2 deletion syndrome adolescents at risk express the same deviant patterns of resting state EEG microstates: A candidate endophenotype of schizophrenia

    Directory of Open Access Journals (Sweden)

    Miralena I. Tomescu

    2015-09-01

    Full Text Available Schizophrenia is a complex psychiatric disorder and many of the factors contributing to its pathogenesis are poorly understood. In addition, identifying reliable neurophysiological markers would improve diagnosis and early identification of this disease. The 22q11.2 deletion syndrome (22q11DS is one major risk factor for schizophrenia. Here, we show further evidence that deviant temporal dynamics of EEG microstates are a potential neurophysiological marker by showing that the resting state patterns of 22q11DS are similar to those found in schizophrenia patients. The EEG microstates are recurrent topographic distributions of the ongoing scalp potential fields with temporal stability of around 80 ms that are mapping the fast reconfiguration of resting state networks. Five minutes of high-density EEG recordings was analysed from 27 adult chronic schizophrenia patients, 27 adult controls, 30 adolescents with 22q11DS, and 28 adolescent controls. In both patient groups we found increased class C, but decreased class D presence and high transition probabilities towards the class C microstates. Moreover, these aberrant temporal dynamics in the two patient groups were also expressed by perturbations of the long-range dependency of the EEG microstates. These findings point to a deficient function of the salience and attention resting state networks in schizophrenia and 22q11DS as class C and class D microstates were previously associated with these networks, respectively. These findings elucidate similarities between individuals at risk and schizophrenia patients and support the notion that abnormal temporal patterns of EEG microstates might constitute a marker for developing schizophrenia.

  5. Acculturation and psychosocial stress show differential relationships to insulin resistance (HOMA) and body fat distribution in two groups of blacks living in the US Virgin Islands.

    Science.gov (United States)

    Tull, Eugene S.; Thurland, Anne; LaPorte, Ronald E.; Chambers, Earle C.

    2003-01-01

    The objective of this study was to determine whether acculturation and psychosocial stress exert differential effects on body fat distribution and insulin resistance among native-born African Americans and African-Caribbean immigrants living in the US Virgin Islands (USVI). Data collected from a non-diabetic sample of 183 USVI-born African Americans and 296 African-Caribbean immigrants age > 20 on the island of St. Croix, USVI were studied. Information on demographic characteristics, acculturation and psychosocial stress was collected by questionnaire. Anthropometric measurements were taken, and serum glucose and insulin were measured from fasting blood samples. Insulin resistance was estimated by the homeostasis model assessment (HOMA) method. The results showed that in multivariate regression analyses, controlling for age, education, gender, BMI, waist circumference, family history of diabetes, smoking and alcohol consumption, acculturation was independently related to logarithm of HOMA (InHOMA) scores among USVI-born African Americans, but not among African-Caribbean immigrants. In contrast, among USVI-born African Americans psychosocial stress was not significantly related to InHOMA, while among African-Caribbean immigrants psychosocial stress was independently related to InHOMA in models that included BMI, but not in those which included waist circumference. This study suggests that acculturation and psychosocial stress may have a differential effect on body fat distribution and insulin resistance among native-born and immigrant blacks living in the US Virgin Islands. PMID:12911254

  6. 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.

  7. Juvenile myoclonic epilepsy: clinical and EEG features

    DEFF Research Database (Denmark)

    Pedersen, S B; Petersen, K A

    1998-01-01

    We aimed to characterize the clinical profile and EEG features of 43 patients with juvenile myoclonic epilepsy. In a retrospective design we studied the records of, and re-interviewed, 43 patients diagnosed with JME from the epilepsy clinic data base. Furthermore, available EEGs were re...... were sleep deprivation (84%), stress (70%), and alcohol consumption (51%). EEG findings included rapid spike-wave and polyspike-wave....

  8. Juvenile myoclonic epilepsy: clinical and EEG features

    DEFF Research Database (Denmark)

    Pedersen, S B; Petersen, K A

    1998-01-01

    We aimed to characterize the clinical profile and EEG features of 43 patients with juvenile myoclonic epilepsy. In a retrospective design we studied the records of, and re-interviewed, 43 patients diagnosed with JME from the epilepsy clinic data base. Furthermore, available EEGs were re-evaluated...... were sleep deprivation (84%), stress (70%), and alcohol consumption (51%). EEG findings included rapid spike-wave and polyspike-wave....

  9. Standardized computer-based organized reporting of EEG SCORE - Second version

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Aurlien, Harald; Brøgger, Jan C

    2017-01-01

    Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted in the se......Standardized terminology for computer-based assessment and reporting of EEG has been previously developed in Europe. The International Federation of Clinical Neurophysiology established a taskforce in 2013 to develop this further, and to reach international consensus. This work resulted...... in the second, revised version of SCORE (Standardized Computer-based Organized Reporting of EEG), which is presented in this paper. The revised terminology was implemented in a software package (SCORE EEG), which was tested in clinical practice on 12,160 EEG recordings. Standardized terms implemented in SCORE....... In the end, the diagnostic significance is scored, using a standardized list of terms. SCORE has specific modules for scoring seizures (including seizure semiology and ictal EEG patterns), neonatal recordings (including features specific for this age group), and for Critical Care EEG Terminology. SCORE...

  10. Effect of Skull Resistivity on the Relative Sensitivity Distributions of EEG and MEG Measurements

    National Research Council Canada - National Science Library

    Malmivuo, J

    2001-01-01

    The authors have previously published calculations that show that, despite the high resistivity of the skull, the spatial sensitivity of magnetoencephalography, MEG, is no better than that of electroencephalography, EEG...

  11. 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.

  12. EEG-fMRI Evaluation of Patients with Mesial Temporal Lobe Sclerosis

    Science.gov (United States)

    Avesani, Mirko; Giacopuzzi, Silvia; Bongiovanni, Luigi Giuseppe; Borelli, Paolo; Cerini, Roberto; Pozzi Mucelli, Roberto; Fiaschi, Antonio

    2014-01-01

    Summary This preliminary study sought more information on blood oxygen level dependent (BOLD) activation, especially contralateral temporal/extratemporal spread, during continuous EEG-fMRI recordings in four patients with mesial temporal sclerosis (MTS). In two patients, EEG showed unilateral focal activity during the EEG-fMRI session concordant with the interictal focus previously identified with standard and video-poly EEG. In the other two patients EEG demonstrated a contralateral diffusion of the irritative focus. In the third patient (with the most drug-resistant form and also extratemporal clinical signs), there was an extratemporal diffusion over frontal regions, ipsilateral to the irritative focus. fMRI analysis confirmed a single activation in the mesial temporal region in two patients whose EEG showed unilateral focal activity, while it demonstrated a bilateral activation in the mesial temporal regions in the other two patients. In the third patient, fMRI demonstrated an activation in the supplementary motxor area. This study confirms the most significant activation with a high firing rate of the irritative focus, but also suggests the importance of using new techniques (such as EEG-fMRI to examine cerebral blood flow) to identify the controlateral limbic activation, and any other extratemporal activations, possible causes of drug resistance in MTS that may require a more precise pre-surgical evaluation with invasive techniques. PMID:24571833

  13. Effects of CPAP-therapy on brain electrical activity in obstructive sleep apneic patients: a combined EEG study using LORETA and Omega complexity : reversible alterations of brain activity in OSAS.

    Science.gov (United States)

    Toth, Marton; Faludi, Bela; Kondakor, Istvan

    2012-10-01

    Effects of initiation of continuous positive airway pressure (CPAP) therapy on EEG background activity were investigated in patients with obstructive sleep apnea syndrome (OSAS, N = 25) to test possible reversibility of alterations of brain electrical activity caused by chronic hypoxia. Normal control group (N = 14) was also examined. Two EEG examinations were done in each groups: at night and in the next morning. Global and regional (left vs. right, anterior vs. posterior) measures of spatial complexity (Omega complexity) were used to characterize the degree of spatial synchrony of EEG. Low resolution electromagnetic tomography (LORETA) was used to localize generators of EEG activity in separate frequency bands. Before CPAP-treatment, a significantly lower Omega complexity was found globally and over the right hemisphere. Due to CPAP-treatment, these significant differences vanished. Significantly decreased Omega complexity was found in the anterior region after treatment. LORETA showed a decreased activity in all of the beta bands after therapy in the right hippocampus, premotor and temporo-parietal cortex, and bilaterally in the precuneus, paracentral and posterior cingulate cortex. No significant changes were seen in control group. Comparing controls and patients before sleep, an increased alpha2 band activity was seen bilaterally in the precuneus, paracentral and posterior cingulate cortex, while in the morning an increased beta3 band activity in the left precentral and bilateral premotor cortex and a decreased delta band activity in the right temporo-parietal cortex and insula were observed. These findings indicate that effect of sleep on EEG background activity is different in OSAS patients and normal controls. In OSAS patients, significant changes lead to a more normal EEG after a night under CPAP-treatment. Compensatory alterations of brain electrical activity in regions associated with influencing sympathetic outflow, visuospatial abilities, long

  14. 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.

  15. EEG spectral coherence data distinguish chronic fatigue syndrome patients from healthy controls and depressed patients--a case control study.

    Science.gov (United States)

    Duffy, Frank H; McAnulty, Gloria B; McCreary, Michelle C; Cuchural, George J; Komaroff, Anthony L

    2011-07-01

    Previous studies suggest central nervous system involvement in chronic fatigue syndrome (CFS), yet there are no established diagnostic criteria. CFS may be difficult to differentiate from clinical depression. The study's objective was to determine if spectral coherence, a computational derivative of spectral analysis of the electroencephalogram (EEG), could distinguish patients with CFS from healthy control subjects and not erroneously classify depressed patients as having CFS. This is a study, conducted in an academic medical center electroencephalography laboratory, of 632 subjects: 390 healthy normal controls, 70 patients with carefully defined CFS, 24 with major depression, and 148 with general fatigue. Aside from fatigue, all patients were medically healthy by history and examination. EEGs were obtained and spectral coherences calculated after extensive artifact removal. Principal Components Analysis identified coherence factors and corresponding factor loading patterns. Discriminant analysis determined whether spectral coherence factors could reliably discriminate CFS patients from healthy control subjects without misclassifying depression as CFS. Analysis of EEG coherence data from a large sample (n = 632) of patients and healthy controls identified 40 factors explaining 55.6% total variance. Factors showed highly significant group differentiation (p EEG spectral coherence analysis identified unmedicated patients with CFS and healthy control subjects without misclassifying depressed patients as CFS, providing evidence that CFS patients demonstrate brain physiology that is not observed in healthy normals or patients with major depression. Studies of new CFS patients and comparison groups are required to determine the possible clinical utility of this test. The results concur with other studies finding neurological abnormalities in CFS, and implicate temporal lobe involvement in CFS pathophysiology.

  16. Temporo-insular enhancement of EEG low and high frequencies in patients with chronic tinnitus. QEEG study of chronic tinnitus patients

    Directory of Open Access Journals (Sweden)

    Weisz Nathan

    2010-03-01

    Full Text Available Abstract Background The physiopathological mechanism underlying the tinnitus phenomenon is still the subject of an ongoing debate. Since oscillatory EEG activity is increasingly recognized as a fundamental hallmark of cortical integrative functions, this study investigates deviations from the norm of different resting EEG parameters in patients suffering from chronic tinnitus. Results Spectral parameters of resting EEG of male tinnitus patients (n = 8, mean age 54 years were compared to those of age-matched healthy males (n = 15, mean age 58.8 years. On average, the patient group exhibited higher spectral power over the frequency range of 2-100 Hz. Using LORETA source analysis, the generators of delta, theta, alpha and beta power increases were localized dominantly to left auditory (Brodmann Areas (BA 41,42, 22, temporo-parietal, insular posterior, cingulate anterior and parahippocampal cortical areas. Conclusions Tinnitus patients show a deviation from the norm of different resting EEG parameters, characterized by an overproduction of resting state delta, theta and beta brain activities, providing further support for the microphysiological and magnetoencephalographic evidence pointing to a thalamocortical dysrhythmic process at the source of tinnitus. These results also provide further confirmation that reciprocal involvements of both auditory and associative/paralimbic areas are essential in the generation of tinnitus.

  17. Investigating long-range correlation properties in EEG during complex cognitive tasks

    International Nuclear Information System (INIS)

    Karkare, Siddharth; Saha, Goutam; Bhattacharya, Joydeep

    2009-01-01

    Previous work shows the presence of scale invariance and long-range correlations in ongoing and spontaneous activity of large scale brain responses (i.e. EEG), and such scaling behavior can also be modulated by simple sensory stimulus. However, little is known whether such alteration but not destruction in scaling properties also occurs during complex cognitive processing and if neuroplasticity plays any role in mediating such changes. In this study, we addressed these issues by investigating scaling properties of multivariate EEG signals obtained from two broad groups - artists and non-artists - while they performed complex tasks of perception and mental imagery of visual art objects. We found that brain regions showing increased correlation properties from rest were similar for both tasks, suggesting that brain networks responsible for visual perception are reactivated for mental imagery. Further, we observed that the two groups could be differentiated by scaling exponents and an artificial neural network based classifier achieved a classification efficiency of over 80%. These results altogether suggest that specific complex cognitive task demands and task-specific expertise can modify the temporal scale-free dynamics of brain responses.

  18. Investigating long-range correlation properties in EEG during complex cognitive tasks

    Energy Technology Data Exchange (ETDEWEB)

    Karkare, Siddharth [Department of Electrical Engineering, Indian Institute of Technology, Kharagpur 721302 (India); Saha, Goutam [Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur 721302 (India); Bhattacharya, Joydeep [Department of Psychology, Goldsmiths College, University of London, New Cross, London SE14 6NW (United Kingdom); Commission for Scientific Visualization, Austrian Academy of Sciences, Vienna A1220 (Austria)], E-mail: j.bhattacharya@gold.ac.uk

    2009-11-30

    Previous work shows the presence of scale invariance and long-range correlations in ongoing and spontaneous activity of large scale brain responses (i.e. EEG), and such scaling behavior can also be modulated by simple sensory stimulus. However, little is known whether such alteration but not destruction in scaling properties also occurs during complex cognitive processing and if neuroplasticity plays any role in mediating such changes. In this study, we addressed these issues by investigating scaling properties of multivariate EEG signals obtained from two broad groups - artists and non-artists - while they performed complex tasks of perception and mental imagery of visual art objects. We found that brain regions showing increased correlation properties from rest were similar for both tasks, suggesting that brain networks responsible for visual perception are reactivated for mental imagery. Further, we observed that the two groups could be differentiated by scaling exponents and an artificial neural network based classifier achieved a classification efficiency of over 80%. These results altogether suggest that specific complex cognitive task demands and task-specific expertise can modify the temporal scale-free dynamics of brain responses.

  19. Neural Entrainment and Sensorimotor Synchronization to the Beat in Children with Developmental Dyslexia: An EEG Study

    Directory of Open Access Journals (Sweden)

    Lincoln J. Colling

    2017-07-01

    Full Text Available Tapping in time to a metronome beat (hereafter beat synchronization shows considerable variability in child populations, and individual differences in beat synchronization are reliably related to reading development. Children with developmental dyslexia show impairments in beat synchronization. These impairments may reflect deficiencies in auditory perception of the beat which in turn affect auditory-motor mapping, or may reflect an independent motor deficit. Here, we used a new methodology in EEG based on measuring beat-related steady-state evoked potentials (SS-EPs, Nozaradan et al., 2015 in an attempt to disentangle neural sensory and motor contributions to behavioral beat synchronization in children with dyslexia. Children tapped with both their left and right hands to every second beat of a metronome pulse delivered at 2.4 Hz, or listened passively to the beat. Analyses of preferred phase in EEG showed that the children with dyslexia had a significantly different preferred phase compared to control children in all conditions. Regarding SS-EPs, the groups differed significantly for the passive Auditory listening condition at 2.4 Hz, and showed a trend toward a difference in the Right hand tapping condition at 3.6 Hz (sensorimotor integration measure. The data suggest that neural rhythmic entrainment is atypical in children with dyslexia for both an auditory beat and during sensorimotor coupling (tapping. The data are relevant to a growing literature suggesting that rhythm-based interventions may help language processing in children with developmental disorders of language learning.

  20. Epilepsia partialis continua Kozevnikov. Correlation of cranial computertomography and EEG-findings

    Energy Technology Data Exchange (ETDEWEB)

    Lipinski, C.G.

    1982-10-01

    Two children with onset of epilepsia partialis continua (Epc) in the age of 8 years are described. EEG-findings and cranial computertomography are compared. Were as the CAT is demonstrating the underlaying morphological lesion of Epc, the EEG is showing the epileptic phenomena. Both, anatomic lesion and electronencephalographic focus can show quite different localisations. Despite this findings, to establish the diagnosis Epc, in our cases the computertomographic proof of a cortical and subcortical lesion seems to be important.

  1. Epilepsia partialis continua Kozevnikov. Correlation of cranial computertomography and EEG-findings

    International Nuclear Information System (INIS)

    Lipinski, C.G.

    1982-01-01

    Two children with onset of epilepsia partialis continua (Epc) in the age of 8 years are described. EEG-findings and cranial computertomography are compared. Were as the CAT is demonstrating the underlaying morphological lesion of Epc, the EEG is showing the epileptic phenomena. Both, anatomic lesion and electronencephalographic focus can show quite different localisations. Despite this findings, to establish the diagnosis Epc, in our cases the computertomographic proof of a cortical and subcortical lesion seems to be important. (orig.) [de

  2. EEG evidence for mirror neuron dysfunction in autism spectrum disorders.

    Science.gov (United States)

    Oberman, Lindsay M; Hubbard, Edward M; McCleery, Joseph P; Altschuler, Eric L; Ramachandran, Vilayanur S; Pineda, Jaime A

    2005-07-01

    Autism spectrum disorders (ASD) are largely characterized by deficits in imitation, pragmatic language, theory of mind, and empathy. Previous research has suggested that a dysfunctional mirror neuron system may explain the pathology observed in ASD. Because EEG oscillations in the mu frequency (8-13 Hz) over sensorimotor cortex are thought to reflect mirror neuron activity, one method for testing the integrity of this system is to measure mu responsiveness to actual and observed movement. It has been established that mu power is reduced (mu suppression) in typically developing individuals both when they perform actions and when they observe others performing actions, reflecting an observation/execution system which may play a critical role in the ability to understand and imitate others' behaviors. This study investigated whether individuals with ASD show a dysfunction in this system, given their behavioral impairments in understanding and responding appropriately to others' behaviors. Mu wave suppression was measured in ten high-functioning individuals with ASD and ten age- and gender-matched control subjects while watching videos of (1) a moving hand, (2) a bouncing ball, and (3) visual noise, or (4) moving their own hand. Control subjects showed significant mu suppression to both self and observed hand movement. The ASD group showed significant mu suppression to self-performed hand movements but not to observed hand movements. These results support the hypothesis of a dysfunctional mirror neuron system in high-functioning individuals with ASD.

  3. [Compressive-spectral analysis of EEG in patients with panic attacks in the context of different psychiatric diseases].

    Science.gov (United States)

    Tuter, N V; Gnezditskiĭ, V V

    2008-01-01

    Panic disorders (PD) which develop in the context of different psychiatric diseases (neurotic, personality disorder and schizotypal disorders) have their own clinical and neurophysiological features. The results of compressive-spectral analysis of EEG (CSA EEG) in patients with panic attack were different depending on the specifics of initial psychiatric status. EEG parameters in patients differed from those in controls. The common feature for all PD patients was the lower spectral density of theta-, alpha- and beta-bands as well as total spectral density without any alterations of region distribution. The decrease of electrical activity of activation systems was found in the groups with neurotic and schizotypal disorders and that of inhibition systems - in the group with schizotypal disorders. The EEG results did not suggest any depression of activation systems in patients with specific personality disorders. The data obtained with CSA EEG mirror the integrative brain activity which determinad of the appearance of PA as well as of nosology of psychiatre disease.

  4. The FNS-based analyzing the EEG to diagnose the bipolar affective disorder

    International Nuclear Information System (INIS)

    Panischev, Yu; Panischeva, S N; Demin, S A

    2015-01-01

    Here we demonstrate a capability of method based on the Flicker-Noise Spectroscopy (FNS) in analyzing the manifestation bipolar affective disorder (BAD) in EEG. Generally EEG from BAD patient does not show the visual differences from healthy EEG. Analyzing the behavior of FNS-parameters and the structure of 3D-cross correlators allows to discover the differential characteristics of BAD. The cerebral cortex electric activity of BAD patients have a specific collective dynamics and configuration of the FNS-characteristics in comparison with healthy subjects. (paper)

  5. Multi-scale symbolic transfer entropy analysis of EEG

    Science.gov (United States)

    Yao, Wenpo; Wang, Jun

    2017-10-01

    From both global and local perspectives, we symbolize two kinds of EEG and analyze their dynamic and asymmetrical information using multi-scale transfer entropy. Multi-scale process with scale factor from 1 to 199 and step size of 2 is applied to EEG of healthy people and epileptic patients, and then the permutation with embedding dimension of 3 and global approach are used to symbolize the sequences. The forward and reverse symbol sequences are taken as the inputs of transfer entropy. Scale factor intervals of permutation and global way are (37, 57) and (65, 85) where the two kinds of EEG have satisfied entropy distinctions. When scale factor is 67, transfer entropy of the healthy and epileptic subjects of permutation, 0.1137 and 0.1028, have biggest difference. And the corresponding values of the global symbolization is 0.0641 and 0.0601 which lies in the scale factor of 165. Research results show that permutation which takes contribution of local information has better distinction and is more effectively applied to our multi-scale transfer entropy analysis of EEG.

  6. Analysis of tractable distortion metrics for EEG compression applications

    International Nuclear Information System (INIS)

    Bazán-Prieto, Carlos; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando; Cárdenas-Barrera, Julián

    2012-01-01

    Coding distortion in lossy electroencephalographic (EEG) signal compression methods is evaluated through tractable objective criteria. The percentage root-mean-square difference, which is a global and relative indicator of the quality held by reconstructed waveforms, is the most widely used criterion. However, this parameter does not ensure compliance with clinical standard guidelines that specify limits to allowable noise in EEG recordings. As a result, expert clinicians may have difficulties interpreting the resulting distortion of the EEG for a given value of this parameter. Conversely, the root-mean-square error is an alternative criterion that quantifies distortion in understandable units. In this paper, we demonstrate that the root-mean-square error is better suited to control and to assess the distortion introduced by compression methods. The experiments conducted in this paper show that the use of the root-mean-square error as target parameter in EEG compression allows both clinicians and scientists to infer whether coding error is clinically acceptable or not at no cost for the compression ratio. (paper)

  7. EEG activity in Muslim prayer: A pilot study

    Directory of Open Access Journals (Sweden)

    Haider H. Alwasiti

    2010-12-01

    Full Text Available Almost all religions incorporate some form of meditation. Muslim prayer is the meditation of Islam. It is an obligatory prayer for all Muslims that is performed five times a day. Although a large body of literature exists on EEG changes in meditation, to date there has been no research published in a peer-reviewed journal on EEG changes during Muslim prayer. The purpose of this pilot study is to encourage further investigation on this type of meditation. Results of EEG analysis in twenty-five trials of Muslim prayer are reported. Some of the findings are consistent with the majority of the previous meditation studies (alpha rhythm slowing, increased alpha rhythm coherence. However, Muslim prayer does not show an increase in alpha and/or theta power like most of the results of other meditation studies. The possible cause of this discrepancy in meditation-related studies is highlighted and a systematic and standardised roadmap for future Muslim prayer EEG research is proposed.

  8. Prediction of subjective ratings of emotional pictures by EEG features

    Science.gov (United States)

    McFarland, Dennis J.; Parvaz, Muhammad A.; Sarnacki, William A.; Goldstein, Rita Z.; Wolpaw, Jonathan R.

    2017-02-01

    Objective. Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. Approach. To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. Main results. Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. Significance. The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.

  9. Increased theta band EEG power in sickle cell disease patients

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    Case M

    2017-12-01

    Full Text Available Michelle Case,1 Sina Shirinpour,1 Huishi Zhang,1 Yvonne H Datta,2 Stephen C Nelson,3 Karim T Sadak,4 Kalpna Gupta,2 Bin He1,5 1Department of Biomedical Engineering, 2Department of Medicine, University of Minnesota, 3Pediatric Hematology-Oncology, Children’s Hospitals and Clinics of Minnesota, 4Pediatric Hematology-Oncology, University of Minnesota Masonic Children’s Hospital, 5Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA Objective: Pain is a major issue in the care of patients with sickle cell disease (SCD. The mechanisms behind pain and the best way to treat it are not well understood. We studied how electroencephalography (EEG is altered in SCD patients. Methods: We recruited 20 SCD patients and compared their resting state EEG to that of 14 healthy controls. EEG power was found across frequency bands using Welch’s method. Electrophysiological source imaging was assessed for each frequency band using the eLORETA algorithm. Results: SCD patients had increased theta power and decreased beta2 power compared to controls. Source localization revealed that areas of greater theta band activity were in areas related to pain processing. Imaging parameters were significantly correlated to emergency department visits, which indicate disease severity and chronic pain intensity. Conclusion: The present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients. Significance: Our findings show that EEG can be used to quantitatively evaluate differences between controls and SCD patients. Our results show the potential of EEG to differentiate between different levels of pain in an unbiased setting, where specific frequency bands could be used as biomarkers for chronic pain. Keywords: sickle cell disease, electroencephalography, chronic pain, electrophysiological source imaging, thalamocortical dysrhythmia

  10. Video-EEG epilepsian diagnostiikassa - milloin ja miksi?

    OpenAIRE

    Mervaala, Esa; Mäkinen, Riikka; Peltola, Jukka; Eriksson, Kai; Jutila, Leena; Immonen, Arto

    2009-01-01

    Aivosähkötoimintaa mittaava EEG on epilepsian spesifinen tutkimus. Video-EEG:llä (V-EEG) tarkoitetaan EEG:n ja videokuvan samanaikaista tallennusta. Valtaosa epilepsiapotilaista joudutaan diagnosoimaan ilman V-EEG:tä, varsinkin jos kohtauksia on esiintynyt vain muutama. Kohtausten toistuessa tavoite on päästä kohtauksenaikaiseen V-EEG-rekisteröintiin. V-EEG:n käyttöaiheista tärkein on epilepsian diagnostiikka ja erotusdiagnostiikka. V-EEG:llä pystytään erottamaan epileptiset kohtaukset esimer...

  11. 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.

  12. Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

    Science.gov (United States)

    Schindler, Kaspar; Rummel, Christian; Andrzejak, Ralph G; Goodfellow, Marc; Zubler, Frédéric; Abela, Eugenio; Wiest, Roland; Pollo, Claudio; Steimer, Andreas; Gast, Heidemarie

    2016-09-01

    To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  13. Improving Generalization Based on l1-Norm Regularization for EEG-Based Motor Imagery Classification

    Directory of Open Access Journals (Sweden)

    Yuwei Zhao

    2018-05-01

    Full Text Available Multichannel electroencephalography (EEG is widely used in typical brain-computer interface (BCI systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l1-norm-based approach to combine the decision value obtained from each EEG channel directly. By extracting the information from different channels on independent frequency bands (FB with l1-norm regularization, the method proposed fits the training data with much less parameters compared to common spatial pattern (CSP methods in order to reduce overfitting. Moreover, an effective and efficient solution to minimize the optimization object is proposed. The experimental results on dataset IVa of BCI competition III and dataset I of BCI competition IV show that, the proposed method contributes to high classification accuracy and increases generalization performance for the classification of MI EEG. As the training set ratio decreases from 80 to 20%, the average classification accuracy on the two datasets changes from 85.86 and 86.13% to 84.81 and 76.59%, respectively. The classification performance and generalization of the proposed method contribute to the practical application of MI based BCI systems.

  14. Effect of invasive EEG monitoring on cognitive outcome after left temporal lobe epilepsy surgery.

    Science.gov (United States)

    Busch, Robyn M; Love, Thomas E; Jehi, Lara E; Ferguson, Lisa; Yardi, Ruta; Najm, Imad; Bingaman, William; Gonzalez-Martinez, Jorge

    2015-10-27

    The objective of this cohort study was to compare neuropsychological outcomes following left temporal lobe resection (TLR) in patients with epilepsy who had or had not undergone prior invasive monitoring. Data were obtained from an institutional review board-approved, neuropsychology registry for patients who underwent epilepsy surgery at Cleveland Clinic between 1997 and 2013. A total of 176 patients (45 with and 131 without invasive EEG) met inclusion criteria. Primary outcome measures were verbal memory and language scores. Other cognitive outcomes were also examined. Outcomes were assessed using difference in scores from before to after surgery and by presence/absence of clinically meaningful decline using reliable change indices (RCIs). Effect of invasive EEG on cognitive outcomes was estimated using weighting and propensity score adjustment to account for differences in baseline characteristics. Linear and logistic regression models compared surgical groups on all cognitive outcomes. Patients with invasive monitoring showed greater declines in confrontation naming; however, when RCIs were used to assess clinically meaningful change, there was no significant treatment effect on naming performance. No difference in verbal memory was observed, regardless of how the outcome was measured. In secondary outcomes, patients with invasive monitoring showed greater declines in working memory, which were no longer apparent using RCIs to define change. There were no outcome differences on other cognitive measures. Results suggest that invasive EEG monitoring conducted prior to left TLR is not associated with greater cognitive morbidity than left TLR alone. This information is important when counseling patients regarding cognitive risks associated with this elective surgery. © 2015 American Academy of Neurology.

  15. Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems.

    Science.gov (United States)

    Chai, Rifai; Naik, Ganesh R; Ling, Sai Ho; Nguyen, Hung T

    2017-01-07

    One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.

  16. A Parametric Empirical Bayesian framework for the EEG/MEG inverse problem: generative models for multisubject and multimodal integration

    Directory of Open Access Journals (Sweden)

    Richard N Henson

    2011-08-01

    Full Text Available We review recent methodological developments within a Parametric Empirical Bayesian (PEB framework for reconstructing intracranial sources of extracranial electroencephalographic (EEG and magnetoencephalographic (MEG data under linear Gaussian assumptions. The PEB framework offers a natural way to integrate multiple constraints (spatial priors on this inverse problem, such as those derived from different modalities (e.g., from functional magnetic resonance imaging, fMRI or from multiple replications (e.g., subjects. Using variations of the same basic generative model, we illustrate the application of PEB to three cases: 1 symmetric integration (fusion of MEG and EEG; 2 asymmetric integration of MEG or EEG with fMRI, and 3 group-optimisation of spatial priors across subjects. We evaluate these applications on multimodal data acquired from 18 subjects, focusing on energy induced by face perception within a time-frequency window of 100-220ms, 8-18Hz. We show the benefits of multi-modal, multi-subject integration in terms of the model evidence and the reproducibility (over subjects of cortical responses to faces.

  17. 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.

  18. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

    Henriksen, Jonas; Remvig, Line Sofie; Madsen, Rasmus Elsborg

    2010-01-01

    Several different algorithms have been proposed for automatic detection of epileptic seizures based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours...... of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet transformation (WT) features and classified by a support vector machine. When implementing a method used for sEEG on iEEG data, a great improvement in performance was obtained when the high...... frequency containing lower levels in the WT were included in the analysis. We were able to obtain a sensitivity of 96.4% and a false detection rate (FDR) of 0.20/h. In general, when implementing an automatic seizure detection algorithm made for sEEG on iEEG, great improvement can be obtained if a frequency...

  19. EEG applications for sport and performance.

    Science.gov (United States)

    Thompson, Trevor; Steffert, Tony; Ros, Tomas; Leach, Joseph; Gruzelier, John

    2008-08-01

    One approach to understanding processes that underlie skilled performing has been to study electrical brain activity using electroencephalography (EEG). A notorious problem with EEG is that genuine cerebral data is often contaminated by artifacts of non-cerebral origin. Unfortunately, such artifacts tend to be exacerbated when the subject is in motion, meaning that obtaining reliable data during exercise is inherently problematic. These problems may explain the limited number of studies using EEG as a methodological tool in the sports sciences. This paper discusses how empirical studies have generally tackled the problem of movement artifact by adopting alternative paradigms which avoid recording during actual physical exertion. Moreover, the specific challenges that motion presents to obtaining reliable EEG data are discussed along with practical and computational techniques to confront these challenges. Finally, as EEG recording in sports is often underpinned by a desire to optimise performance, a brief review of EEG-biofeedback and peak performance studies is also presented. A knowledge of practical aspects of EEG recording along with the advent of new technology and increasingly sophisticated processing models offer a promising approach to minimising, if perhaps not entirely circumventing, the problem of obtaining reliable EEG data during motion.

  20. Effects of oral amines on the EEG.

    Science.gov (United States)

    Scott, D F; Moffett, A M; Swash, M

    1977-02-01

    Oral tyramine activated pre-existing episodic EEG abnormalities--namely, sharp waves, spike and wave, and localised theta activity--in epileptic patients. Little change was found in the EEGs of migrainous subjects after chocolate or beta-phenylethylamine. The implications of the findings with tyramine are discussed.

  1. Source localization of rhythmic ictal EEG activity

    DEFF Research Database (Denmark)

    Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana

    2013-01-01

    Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal...... EEG activity using a distributed source model....

  2. 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.

  3. Changes of hypnagogic imagery and EEG stages

    OpenAIRE

    Hayashi, Mitsuo; Katoh, Kohichi; Hori, Tadao

    1998-01-01

    The aim of this study is to investigate the relationships between hypnagogic imagery and EEG stages. According to Hori, et al. (1994), the hypnagogic EEGs was classified into 9 stages, those were 1) alpha wave train, 2) alpha wave intermittent (>50%), 3) alpha wave intermittent (

  4. Continuous EEG Monitoring in Aneurysmal Subarachnoid Hemorrhage

    DEFF Research Database (Denmark)

    Kondziella, Daniel; Friberg, Christian Kærsmose; Wellwood, Ian

    2015-01-01

    BACKGROUND: Continuous EEG (cEEG) may allow monitoring of patients with aneurysmal subarachnoid hemorrhage (SAH) for delayed cerebral ischemia (DCI) and seizures, including non-convulsive seizures (NCSz), and non-convulsive status epilepticus (NCSE). We aimed to evaluate: (a) the diagnostic...

  5. Regional differences in trait-like characteristics of the waking EEG in early adolescence.

    Science.gov (United States)

    Benz, Dominik C; Tarokh, Leila; Achermann, Peter; Loughran, Sarah P

    2013-10-09

    The human waking EEG spectrum shows high heritability and stability and, despite maturational cortical changes, high test-retest reliability in children and teens. These phenomena have also been shown to be region specific. We examined the stability of the morphology of the wake EEG spectrum in children aged 11 to 13 years recorded over weekly intervals and assessed whether the waking EEG spectrum in children may also be trait-like. Three minutes of eyes open and three minutes of eyes closed waking EEG was recorded in 22 healthy children once a week for three consecutive weeks. Eyes open and closed EEG power density spectra were calculated for two central (C3LM and C4LM) and two occipital (O1LM and O2LM) derivations. A hierarchical cluster analysis was performed to determine whether the morphology of the waking EEG spectrum between 1 and 20 Hz is trait-like. We also examined the stability of the alpha peak using an ANOVA. The morphology of the EEG spectrum recorded from central derivations was highly stable and unique to an individual (correctly classified in 85% of participants), while the EEG recorded from occipital derivations, while stable, was much less unique across individuals (correctly classified in 42% of participants). Furthermore, our analysis revealed an increase in alpha peak height concurrent with a decline in the frequency of the alpha peak across weeks for occipital derivations. No changes in either measure were observed in the central derivations. Our results indicate that across weekly recordings, power spectra at central derivations exhibit more "trait-like" characteristics than occipital derivations. These results may be relevant for future studies searching for links between phenotypes, such as psychiatric diagnoses, and the underlying genes (i.e., endophenotypes) by suggesting that such studies should make use of more anterior rather than posterior EEG derivations.

  6. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns.

    Science.gov (United States)

    Liao, Shih-Cheng; Wu, Chien-Te; Huang, Hao-Chuan; Cheng, Wei-Teng; Liu, Yi-Hung

    2017-06-14

    Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP

  7. Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

    Directory of Open Access Journals (Sweden)

    Shih-Cheng Liao

    2017-06-01

    Full Text Available Major depressive disorder (MDD has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP. The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total. Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be

  8. Propofol Anesthesia and Sleep: A High-Density EEG Study

    Science.gov (United States)

    Murphy, Michael; Bruno, Marie-Aurelie; Riedner, Brady A.; Boveroux, Pierre; Noirhomme, Quentin; Landsness, Eric C.; Brichant, Jean-Francois; Phillips, Christophe; Massimini, Marcello; Laureys, Steven; Tononi, Giulio; Boly, Melanie

    2011-01-01

    Study Objectives: The electrophysiological correlates of anesthetic sedation remain poorly understood. We used high-density electroencephalography (hd-EEG) and source modeling to investigate the cortical processes underlying propofol anesthesia and compare them to sleep. Design: 256-channel EEG recordings in humans during propofol anesthesia. Setting: Hospital operating room. Patients or Participants: 8 healthy subjects (4 males) Interventions: N/A Measurements and Results: Initially, propofol induced increases in EEG power from 12–25 Hz. Loss of consciousness (LOC) was accompanied by the appearance of EEG slow waves that resembled the slow waves of NREM sleep. We compared slow waves in propofol to slow waves recorded during natural sleep and found that both populations of waves share similar cortical origins and preferentially propagate along the mesial components of the default network. However, propofol slow waves were spatially blurred compared to sleep slow waves and failed to effectively entrain spindle activity. Propofol also caused an increase in gamma (25–40 Hz) power that persisted throughout LOC. Source modeling analysis showed that this increase in gamma power originated from the anterior and posterior cingulate cortices. During LOC, we found increased gamma functional connectivity between these regions compared to the wakefulness. Conclusions: Propofol anesthesia is a sleep-like state and slow waves are associated with diminished consciousness even in the presence of high gamma activity. Citation: Murphy M; Bruno MA; Riedner BA; Boveroux P; Noirhomme Q; Landsness EC; Brichant JF; Phillips C; Massimini M; Laureys S; Tononi G; Boly M. Propofol anesthesia and sleep: a high-density EEG study. SLEEP 2011;34(3):283-291. PMID:21358845

  9. Statistical features of hypnagogic EEG measured by a new scoring system.

    Science.gov (United States)

    Tanaka, H; Hayashi, M; Hori, T

    1996-11-01

    The purpose of this study was to examine the durations of individual occurrences of each of nine hypnagogic electroencephalographic (EEG) stages and the interchange relationship among these stages. Most of the alpha patterns (stages 1, 2, and 3), ripples (stage 5), and spindles (stage 9) tended to last > 2 minutes. On the other hand, histograms of the durations of time in EEG flattening (stage 4) and vertex sharp wave (stages 6, 7, and 8) patterns had peaks that lasted hypnagogic state. This was especially true for the first five stages. EEG stages with vertex sharp waves (stages 6, 7, and 8), however, showed less-smooth changes, with approximately 20% of all changes involving a jump of more than one stage. These results show that the basic EEG activities in the sleep onset period are the alpha, theta, and sleep spindles activities, whereas the activities of vertex sharp waves seem to have a secondary or enhancing role, instead of independent characteristics.

  10. Sleep EEG findings in ICD-10 borderline personality disorder in Egypt.

    Science.gov (United States)

    Asaad, Tarek; Okasha, Tarek; Okasha, Ahmed

    2002-09-01

    Previous work has demonstrated that patients with borderline personality disorder show some similarities to patients with major depression, especially regarding their sleep profile. This study aimed at investigating such a hypothesis in an Egyptian sample, considering the possible influence of cultural differences. All night polysomnographic assessments were made for 20 ICD-10 diagnosed borderline patients (without co-morbid depression), in addition to 20 patients with major depression and 20 healthy matched controls. The two patient groups differed significantly from controls in their sleep profile, especially regarding sleep continuity measures, decreased SWS and REM sleep abnormalities. High similarity was found in EEG sleep profile of the two patient groups, though the changes were more robust in patients with depression. The small number of subjects precluded finer analyses of sleep microstructure by depressive symptoms. The great similarity in EEG sleep profile between borderline personality disorder patients and patients with major depression suggests a common biological origin for both conditions, with the difference being 'quantitative' rather than 'qualitative'. Our data are all the more compelling in that the presumed personality disturbance in the Egyptian culture manifests neurophysiologically as in the Western world.

  11. EEG study of the mirror neuron system in children with high functioning autism.

    Science.gov (United States)

    Raymaekers, Ruth; Wiersema, Jan Roelf; Roeyers, Herbert

    2009-12-22

    Individuals with Autism Spectrum Disorder (ASD) are characterised by an impaired imitation, thought to be critical for early affective, social and communicative development. One neurological system proposed to underlie this function is the mirror neuron system (MNS) and previous research has suggested a dysfunctional MNS in ASD. The EEG mu frequency, more precisely the reduction of the mu power, is considered to be an index for mirror neuron functioning. In this work, EEG registrations are used to evaluate the mirror neuron functioning of twenty children with high functioning autism (HFA) between 8 and 13 years. Their mu suppression to self-executed and observed movement is compared to typically developing peers and related to age, intelligence and symptom severity. Both groups show significant mu suppression to both self and observed hand movements. No group differences are found in either condition. These results do not support the hypothesis that HFA is associated with a dysfunctional MNS. The discrepancy with previous research is discussed in light of the heterogeneity of the ASD population.

  12. Developmental trajectories of resting EEG power: an endophenotype of autism spectrum disorder.

    Directory of Open Access Journals (Sweden)

    Adrienne L Tierney

    Full Text Available Current research suggests that autism spectrum disorder (ASD is characterized by asynchronous neural oscillations. However, it is unclear whether changes in neural oscillations represent an index of the disorder or are shared more broadly among both affected and unaffected family members. Additionally, it remains unclear how early these differences emerge in development and whether they remain constant or change over time. In this study we examined developmental trajectories in spectral power in infants at high- or low-risk for ASD. Spectral power was extracted from resting EEG recorded over frontal regions of the scalp when infants were 6, 9, 12, 18 and 24 months of age. We used multilevel modeling to assess change over time between risk groups in the delta, theta, low alpha, high alpha, beta, and gamma frequency bands. The results indicated that across all bands, spectral power was lower in high-risk infants as compared to low-risk infants at 6-months of age. Furthermore high-risk infants showed different trajectories of change in spectral power in the subsequent developmental window indicating that not only are the patterns of change different, but that group differences are dynamic within the first two years of life. These findings remained the same after removing data from a subset of participants who displayed ASD related behaviors at 24 or 36 months. These differences in the nature of the trajectories of EEG power represent important endophenotypes of ASD.

  13. Simultaneous EEG and EMG biofeedback for peak performance in musicians.

    Science.gov (United States)

    Markovska-Simoska, Silvana; Pop-Jordanova, Nada; Georgiev, Dejan

    2008-07-01

    The aim of this study was to determine the effects of alpha neurofeedback and EMG biofeedback protocols for improvement of musical performance in violinists. The sample consisted of 12 music students (10 violinists and 2 viola players) from the Faculty of Music, Skopje (3 males, mean age of 20 +/- 0 and 9 females, mean age = 20.89 +/- 2.98). Six of them had a low alpha peak frequency (APF) ( 10 Hz). The sample was randomized in two groups. The students from the experimental group participated in 20 sessions of biofeedback (alpha/EMG), combined with music practice, while the students from the control group did only music practice. Average absolute power, interhemispheric coherence in the alpha band, alpha peak frequency (APF), individual alpha band width (IABW), amount of alpha suppression (AAS) and surface forehead integrated EMG power (IEMG), as well as a score on musical performance and inventories measuring anxiety, were assessed. Alpha-EEG/EMG-biofeedback was associated with a significant increase in average alpha power, APF and IABW in all the participants and with decreases in IEMG only in high-APF musicians. The biofeedback training success was positively correlated with the alpha power, IcoH, APF, IABW and baseline level of APF and IABW. Alpha-EEG/EMG biofeedback is capable of increasing voluntary self-regulation and the quality of musical performance. The efficiency of biofeedback training depends on the baseline EEG alpha activity status, in particular the APF.

  14. 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.

  15. 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.

  16. Long-Range Reduced Predictive Information Transfers of Autistic Youths in EEG Sensor-Space During Face Processing.

    Science.gov (United States)

    Khadem, Ali; Hossein-Zadeh, Gholam-Ali; Khorrami, Anahita

    2016-03-01

    The majority of previous functional/effective connectivity studies conducted on the autistic patients converged to the underconnectivity theory of ASD: "long-range underconnectivity and sometimes short-rang overconnectivity". However, to the best of our knowledge the total (linear and nonlinear) predictive information transfers (PITs) of autistic patients have not been investigated yet. Also, EEG data have rarely been used for exploring the information processing deficits in autistic subjects. This study is aimed at comparing the total (linear and nonlinear) PITs of autistic and typically developing healthy youths during human face processing by using EEG data. The ERPs of 12 autistic youths and 19 age-matched healthy control (HC) subjects were recorded while they were watching upright and inverted human face images. The PITs among EEG channels were quantified using two measures separately: transfer entropy with self-prediction optimality (TESPO), and modified transfer entropy with self-prediction optimality (MTESPO). Afterwards, the directed differential connectivity graphs (dDCGs) were constructed to characterize the significant changes in the estimated PITs of autistic subjects compared with HC ones. By using both TESPO and MTESPO, long-range reduction of PITs of ASD group during face processing was revealed (particularly from frontal channels to right temporal channels). Also, it seemed the orientation of face images (upright or upside down) did not modulate the binary pattern of PIT-based dDCGs, significantly. Moreover, compared with TESPO, the results of MTESPO were more compatible with the underconnectivity theory of ASD in the sense that MTESPO showed no long-range increase in PIT. It is also noteworthy that to the best of our knowledge it is the first time that a version of MTE is applied for patients (here ASD) and it is also its first use for EEG data analysis.

  17. Frontal EEG asymmetry in borderline personality disorder is associated with alexithymia

    OpenAIRE

    Flasbeck, Vera; Popkirov, Stoyan; Brüne, Martin

    2017-01-01

    Background Frontal EEG asymmetry is a widely studied correlate of emotion processing and psychopathology. Recent research suggests that frontal EEG asymmetry during resting state is related to approach/withdrawal motivation and is also found in affective disorders such as major depressive disorder. Patients with borderline personality disorder (BPD) show aberrant behavior in relation to both approach and withdrawal motivation, which may arguably be associated with their difficulties in emotio...

  18. EEG Mu (µ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults.

    Science.gov (United States)

    Saltuklaroglu, Tim; Harkrider, Ashley W; Thornton, David; Jenson, David; Kittilstved, Tiffani

    2017-06-01

    Stuttering is linked to sensorimotor deficits related to internal modeling mechanisms. This study compared spectral power and oscillatory activity of EEG mu (μ) rhythms between persons who stutter (PWS) and controls in listening and auditory discrimination tasks. EEG data were analyzed from passive listening in noise and accurate (same/different) discrimination of tones or syllables in quiet and noisy backgrounds. Independent component analysis identified left and/or right μ rhythms with characteristic alpha (α) and beta (β) peaks localized to premotor/motor regions in 23 of 27 people who stutter (PWS) and 24 of 27 controls. PWS produced μ spectra with reduced β amplitudes across conditions, suggesting reduced forward modeling capacity. Group time-frequency differences were associated with noisy conditions only. PWS showed increased μ-β desynchronization when listening to noise and early in discrimination events, suggesting evidence of heightened motor activity that might be related to forward modeling deficits. PWS also showed reduced μ-α synchronization in discrimination conditions, indicating reduced sensory gating. Together these findings indicate spectral and oscillatory analyses of μ rhythms are sensitive to stuttering. More specifically, they can reveal stuttering-related sensorimotor processing differences in listening and auditory discrimination that also may be influenced by basal ganglia deficits. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Complexity analysis of EEG in patients with schizophrenia using fractal dimension

    International Nuclear Information System (INIS)

    Raghavendra, B S; Dutt, D Narayana; Halahalli, Harsha N; John, John P

    2009-01-01

    We computed Higuchi's fractal dimension (FD) of resting, eyes closed EEG recorded from 30 scalp locations in 18 male neuroleptic-naïve, recent-onset schizophrenia (NRS) subjects and 15 male healthy control (HC) subjects, who were group-matched for age. Schizophrenia patients showed a diffuse reduction of FD except in the bilateral temporal and occipital regions, with the reduction being most prominent bifrontally. The positive symptom (PS) schizophrenia subjects showed FD values similar to or even higher than HC in the bilateral temporo-occipital regions, along with a co-existent bifrontal FD reduction as noted in the overall sample of NRS. In contrast, this increase in FD values in the bilateral temporo-occipital region was absent in the negative symptom (NS) subgroup. The regional differences in complexity suggested by these findings may reflect the aberrant brain dynamics underlying the pathophysiology of schizophrenia and its symptom dimensions. Higuchi's method of measuring FD directly in the time domain provides an alternative for the more computationally intensive nonlinear methods of estimating EEG complexity

  20. Effects of Soft Drinks on Resting State EEG and Brain-Computer Interface Performance.

    Science.gov (United States)

    Meng, Jianjun; Mundahl, John; Streitz, Taylor; Maile, Kaitlin; Gulachek, Nicholas; He, Jeffrey; He, Bin

    2017-01-01

    Motor imagery-based (MI based) brain-computer interface (BCI) using electroencephalography (EEG) allows users to directly control a computer or external device by modulating and decoding the brain waves. A variety of factors could potentially affect the performance of BCI such as the health status of subjects or the environment. In this study, we investigated the effects of soft drinks and regular coffee on EEG signals under resting state and on the performance of MI based BCI. Twenty-six healthy human subjects participated in three or four BCI sessions with a resting period in each session. During each session, the subjects drank an unlabeled soft drink with either sugar (Caffeine Free Coca-Cola), caffeine (Diet Coke), neither ingredient (Caffeine Free Diet Coke), or a regular coffee if there was a fourth session. The resting state spectral power in each condition was compared; the analysis showed that power in alpha and beta band after caffeine consumption were decreased substantially compared to control and sugar condition. Although the attenuation of powers in the frequency range used for the online BCI control signal was shown, group averaged BCI online performance after consuming caffeine was similar to those of other conditions. This work, for the first time, shows the effect of caffeine, sugar intake on the online BCI performance and resting state brain signal.

  1. Correntropy measures to detect daytime sleepiness from EEG signals

    International Nuclear Information System (INIS)

    Melia, Umberto; Vallverdú, Montserrat; Caminal, Pere; Guaita, Marc; Montserrat, Josep M; Vilaseca, Isabel; Salamero, Manel; Gaig, Carles; Santamaria, Joan

    2014-01-01

    Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders and has a great impact on patients’ lives. While many studies have been carried out in order to assess daytime sleepiness, automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on correntropy function analysis of EEG signals was proposed in order to detect patients suffering from EDS. Multichannel EEG signals were recorded during five Maintenance of Wakefulness Tests (MWT) and Multiple Sleep Latency Tests (MSLT) alternated throughout the day for patients suffering from sleep disordered breathing (SDB). A group of 20 patients with EDS was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60 s EEG windows in a waking state. Measures obtained from the cross-correntropy function (CCORR) and auto-correntropy function (ACORR) were calculated in the EEG frequency bands: δ, 0.1–4 Hz; θ, 4–8 Hz; α, 8–12 Hz; β, 12–30 Hz; total band TB, 0.1–45 Hz. These functions permitted the quantification of complex signal properties and the non-linear couplings between different areas of the scalp. Statistical differences between EDS and WDS groups were mainly found in the β band during MSLT events (p-value < 0.0001). The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group. At best, ACORR and CCORR measures yielded sensitivity and specificity above 80% and the area under ROC curve (AUC) was above 0.85 in classifying EDS and WDS patients. These performances represent an improvement with respect to classical EEG indices applied in the same database (sensitivity and specificity were never above 80% and AUC was under 0.75). (paper)

  2. Quantitative EEG in Children and Adults With Attention Deficit Hyperactivity Disorder: Comparison of Absolute and Relative Power Spectra and Theta/Beta Ratio.

    Science.gov (United States)

    Markovska-Simoska, Silvana; Pop-Jordanova, Nada

    2017-01-01

    In recent decades, resting state electroencephalographic (EEG) measures have been widely used to document underlying neurophysiological dysfunction in attention deficit hyperactivity disorder (ADHD). Although most EEG studies focus on children, there is a growing interest in adults with ADHD too. The aim of this study was to objectively assess and compare the absolute and relative EEG power as well as the theta/beta ratio in children and adults with ADHD. The evaluated sample comprised 30 male children and 30 male adults with ADHD diagnosed according to DSM-IV criteria. They were compared with 30 boys and 30 male adults matched by age. The mean age (±SD) of the children's group was 9 (±2.44) years and the adult group 35.8 (±8.65) years. EEG was recorded during an eyes-open condition. Spectral analysis of absolute (μV 2 ) and relative power (%) was carried out for 4 frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-21 Hz). The findings obtained for ADHD children are increased absolute power of slow waves (theta and delta), whereas adults exhibited no differences compared with normal subjects. For the relative power spectra there were no differences between the ADHD and control groups. Across groups, the children showed greater relative power than the adults in the delta and theta bands, but for the higher frequency bands (alpha and beta) the adults showed more relative power than children. Only ADHD children showed greater theta/beta ratio compared to the normal group. Classification analysis showed that ADHD children could be differentiated from the control group by the absolute theta values and theta/beta ratio at Cz, but this was not the case with ADHD adults. The question that should be further explored is if these differences are mainly due to maturation processes or if there is a core difference in cortical arousal between ADHD children and adults. © EEG and Clinical Neuroscience Society (ECNS) 2016.

  3. 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.

  4. EEG-fMRI Bayesian framework for neural activity estimation: a simulation study

    Science.gov (United States)

    Croce, Pierpaolo; Basti, Alessio; Marzetti, Laura; Zappasodi, Filippo; Del Gratta, Cosimo

    2016-12-01

    Objective. Due to the complementary nature of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), and given the possibility of simultaneous acquisition, the joint data analysis can afford a better understanding of the underlying neural activity estimation. In this simulation study we want to show the benefit of the joint EEG-fMRI neural activity estimation in a Bayesian framework. Approach. We built a dynamic Bayesian framework in order to perform joint EEG-fMRI neural activity time course estimation. The neural activity is originated by a given brain area and detected by means of both measurement techniques. We have chosen a resting state neural activity situation to address the worst case in terms of the signal-to-noise ratio. To infer information by EEG and fMRI concurrently we used a tool belonging to the sequential Monte Carlo (SMC) methods: the particle filter (PF). Main results. First, despite a high computational cost, we showed the feasibility of such an approach. Second, we obtained an improvement in neural activity reconstruction when using both EEG and fMRI measurements. Significance. The proposed simulation shows the improvements in neural activity reconstruction with EEG-fMRI simultaneous data. The application of such an approach to real data allows a better comprehension of the neural dynamics.

  5. Early Changes in Alpha Band Power and DMN BOLD Activity in Alzheimer’s Disease: A Simultaneous Resting State EEG-fMRI Study

    Directory of Open Access Journals (Sweden)

    Katharina Brueggen

    2017-10-01

    Full Text Available Simultaneous resting state functional magnetic resonance imaging (rsfMRI–resting state electroencephalography (rsEEG studies in healthy adults showed robust positive associations of signal power in the alpha band with BOLD signal in the thalamus, and more heterogeneous associations in cortical default mode network (DMN regions. Negative associations were found in occipital regions. In Alzheimer’s disease (AD, rsfMRI studies revealed a disruption of the DMN, while rsEEG studies consistently reported a reduced power within the alpha band. The present study is the first to employ simultaneous rsfMRI-rsEEG in an AD sample, investigating the association of alpha band power and BOLD signal, compared to healthy controls (HC. We hypothesized to find reduced positive associations in DMN regions and reduced negative associations in occipital regions in the AD group. Simultaneous resting state fMRI–EEG was recorded in 14 patients with mild AD and 14 HC, matched for age and gender. Power within the EEG alpha band (8–12 Hz, 8–10 Hz, and 10–12 Hz was computed from occipital electrodes and served as regressor in voxel-wise linear regression analyses, to assess the association with the BOLD signal. Compared to HC, the AD group showed significantly decreased positive associations between BOLD signal and occipital alpha band power in clusters in the superior, middle and inferior frontal cortex, inferior temporal lobe and thalamus (p < 0.01, uncorr., cluster size ≥ 50 voxels. This group effect was more pronounced in the upper alpha sub-band, compared to the lower alpha sub-band. Notably, we observed a high inter-individual heterogeneity. Negative associations were only reduced in the lower alpha range in the hippocampus, putamen and cerebellum. The present study gives first insights into the relationship of resting-state EEG and fMRI characteristics in an AD sample. The results suggest that positive associations between alpha band power and BOLD

  6. Video-EEG recording: a four-year clinical audit.

    LENUS (Irish Health Repository)

    O'Rourke, K

    2012-02-03

    In the setting of a regional neurological unit without an epilepsy surgery service as in our case, video-EEG telemetry is undertaken for three main reasons; to investigate whether frequent paroxysmal events represent seizures when there is clinical doubt, to attempt anatomical localization of partial seizures when standard EEG is unhelpful, and to attempt to confirm that seizures are non-epileptic when this is suspected. A clinical audit of all telemetry performed over a four-year period was carried out, in order to determine the clinical utility of this aspect of the service and to determine means of improving effectiveness in the unit. Analysis of the data showed a high rate of negative studies with no attacks recorded. Of the positive studies approximately 50% showed non-epileptic attacks. Strategies for improving the rate of positive investigations are discussed.

  7. Computerized EEG and brain imaging studies in untreated schizophrenic patients

    International Nuclear Information System (INIS)

    Miyauchi, Toshiro; Kishimoto, Hideji; Hagimoto, Hiroshi; Fujita, Haruhiro; Tanaka, Kenkichi

    1993-01-01

    We undertook routine EEG, Z-map, CT and PET scans in seven acute untreated schizophrenics. Routine EEGs showed slower activity in only one case. However, the Z-map showed slower activity in all the cases. CT demonstrated brain atrophy in three of the cases, and PET revealed hypofrontality in two, right hypoparietality in four, and both conditions in one case. There was no relation between CT and PET or the Z-map. However, a significant increase in alpha 1 activity was demonstrated on the Z-map in cases who were found to be the parietal type on PET; this was not conspicuous in the frontal type on PET. Moreover, in three of the patients, the Z-map findings were similar to the lesion indicated on PET. (author)

  8. High-accuracy user identification using EEG biometrics.

    Science.gov (United States)

    Koike-Akino, Toshiaki; Mahajan, Ruhi; Marks, Tim K; Ye Wang; Watanabe, Shinji; Tuzel, Oncel; Orlik, Philip

    2016-08-01

    We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.

  9. 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

  10. 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).

  11. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  12. 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.

  13. 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.

  14. 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.

  15. A method for detecting nonlinear determinism in normal and epileptic brain EEG signals.

    Science.gov (United States)

    Meghdadi, Amir H; Fazel-Rezai, Reza; Aghakhani, Yahya

    2007-01-01

    A robust method of detecting determinism for short time series is proposed and applied to both healthy and epileptic EEG signals. The method provides a robust measure of determinism through characterizing the trajectories of the signal components which are obtained through singular value decomposition. Robustness of the method is shown by calculating proposed index of determinism at different levels of white and colored noise added to a simulated chaotic signal. The method is shown to be able to detect determinism at considerably high levels of additive noise. The method is then applied to both intracranial and scalp EEG recordings collected in different data sets for healthy and epileptic brain signals. The results show that for all of the studied EEG data sets there is enough evidence of determinism. The determinism is more significant for intracranial EEG recordings particularly during seizure activity.

  16. The use of Matlab for colour fuzzy representation of multichannel EEG short time spectra.

    Science.gov (United States)

    Bigan, C; Strungaru, R

    1998-01-01

    During the last years, a lot of EEG research efforts was directed to intelligent methods for automatic analysis of data from multichannel EEG recordings. However, all the applications reported were focused on specific single tasks like detection of one specific "event" in the EEG signal: spikes, sleep spindles, epileptic seizures, K complexes, alpha or other rhythms or even artefacts. The aim of this paper is to present a complex system being able to perform a representation of the dynamic changes in frequency components of each EEG channel. This representation uses colours as a powerful means to show the only one frequency range chosen from the shortest epoch of signal able to be processed with the conventional "Short Time Fast Fourier Transform" (S.T.F.F.T.) method.

  17. Using Support Vector Machine on EEG for Advertisement Impact Assessment

    Directory of Open Access Journals (Sweden)

    Zhen Wei

    2018-03-01

    Full Text Available The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk. Or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.

  18. Using Support Vector Machine on EEG for Advertisement Impact Assessment.

    Science.gov (United States)

    Wei, Zhen; Wu, Chao; Wang, Xiaoyi; Supratak, Akara; Wang, Pan; Guo, Yike

    2018-01-01

    The advertising industry depends on an effective assessment of the impact of advertising as a key performance metric for their products. However, current assessment methods have relied on either indirect inference from observing changes in consumer behavior after the launch of an advertising campaign, which has long cycle times and requires an ad campaign to have already have been launched (often meaning costs having been sunk). Or through surveys or focus groups, which have a potential for experimental biases, peer pressure, and other psychological and sociological phenomena that can reduce the effectiveness of the study. In this paper, we investigate a new approach to assess the impact of advertisement by utilizing low-cost EEG headbands to record and assess the measurable impact of advertising on the brain. Our evaluation shows the desired performance of our method based on user experiment with 30 recruited subjects after watching 220 different advertisements. We believe the proposed SVM method can be further developed to a general and scalable methodology that can enable advertising agencies to assess impact rapidly, quantitatively, and without bias.

  19. The infant mirror neuron system studied with high density EEG.

    Science.gov (United States)

    Nyström, Pär

    2008-01-01

    The mirror neuron system has been suggested to play a role in many social capabilities such as action understanding, imitation, language and empathy. These are all capabilities that develop during infancy and childhood, but the human mirror neuron system has been poorly studied using neurophysiological measures. This study measured the brain activity of 6-month-old infants and adults using a high-density EEG net with the aim of identifying mirror neuron activity. The subjects viewed both goal-directed movements and non-goal-directed movements. An independent component analysis was used to extract the sources of cognitive processes. The desynchronization of the mu rhythm in adults has been shown to be a marker for activation of the mirror neuron system and was used as a criterion to categorize independent components between subjects. The results showed significant mu desynchronization in the adult group and significantly higher ERP activation in both adults and 6-month-olds for the goal-directed action observation condition. This study demonstrate that infants as young as 6 months display mirror neuron activity and is the first to present a direct ERP measure of the mirror neuron system in infants.

  20. 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.

  1. 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.

  2. Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

    Science.gov (United States)

    Schetinin, Vitaly; Jakaite, Livija; Nyah, Ndifreke; Novakovic, Dusica; Krzanowski, Wojtek

    2018-08-01

    The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

  3. EEG review comments on the geotechnical reports provided by DOE to EEG under the stipulated agreement through March 1, 1983

    International Nuclear Information System (INIS)

    1983-04-01

    The purpose of the Environmental Evaluation Group (EEG) is to conduct an independent technical evaluation of the potential radiation exposure to people from the proposed federal radioactive Waste Isolation Pilot Plant (WIPP) near Carlsbad, in order to protect the public health and safety and ensure that there is minimal environmental degradation. Analyses are conducted of available data concerning the proposed site, the design of the repository, its planned operation, and its long-term stability. These analyses include assessments of reports issued by the US Department of Energy (DOE) and its contractors, other federal agencies and organizations, as they relate to the potential health, safety and environmental impacts from WIPP. This publication is a compilation of EEG's written comments on each of the following reports: Deep Dissolution; Breccia Pipes; DMG Hydrology; Natural Resources; Plans for Site and Preliminary Design Validation; Plans for Simulated Waste; Brine Reservoir Report; Disturbed Zone Exploration; and Fracture Flow in the Rustler Aquifers

  4. Assessment of preconscious sucrose perception using EEG

    DEFF Research Database (Denmark)

    Rotvel, Camilla; Møller, Stine; Nielsen, Rene R

    The objective of the current study is to develop a methodology for food ingredient screening based on Electro-Encephalo-Graphy (EEG). EEG measures electrical activity in the central nervous system, allowing assessment of activity in the ascending gustatory pathway from the taste buds on the tongue...... stimulus. The EEG was recorded using a 64 electrode setup, and gustatory evoked potentials (GEP) were estimated by coherent averaging across all 60 stimulations for each concentration. Cortical source localization based on the GEP was performed using a low resolution electromagnetic tomography (LORETA...

  5. 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.

  6. Study on EEG power and coherence in patients with mild cognitive impairment during working memory task

    Institute of Scientific and Technical Information of China (English)

    JIANG Zheng-yan

    2005-01-01

    To investigate the features of electroencephalography (EEG) power and coherence at rest and during a working memory task of patients with mild cognitive impairment (MCI). Thirty-five patients (17 males, 18 females; 52~71 years old) and 34 sex- and age-matched controls (17 males, 17 females; 51~63 years old) were recruited in the present study. Mini-Mental State Examination (MMSE) of 35 patients with MCI and 34 normal controls revealed that the scores of MCI patients did not differ significantly from those of normal controls (P>0.05). Then, EEGs at rest and during working memory task with three levels of working memory load were recorded. The EEG power was computed over 10 channels: right and left frontal (F3, F4), central (C3,C4), parietal (P3, P4), temporal (TS, T6) and occipital (O1, O2); inter-hemispheric coherences were computed from five electrode pairs of F3-F4, C3-C4, P3-P4, T5-T6 and O1-O2 for delta (1.0~3.5 Hz), theta (4.0~7.5 Hz), alpha-1 (8.0~10.0 Hz), alpha-2 (10.5~13.0 Hz), beta-1 (13.5~18.0 Hz) and beta-2 (18.5~30.0 Hz) frequency bands. All values of the EEG power of MCI patients were found to be higher than those of normal controls at rest and during working memory tasks. Furthermore, the values of EEG power in the theta, alpha-1, alpha-2 and beta-1 bands of patients with MCI were significantly high (P<0.05) in comparison with those of normal controls. Correlation analysis indicated a significant negative correlation between the EEG powers and MMSE scores. In addition, during working memory tasks, the EEG coherences in all bands were significantly higher in the MCI group in comparison with those in the control group (P<0.05). However, there was no significant difference in EEG coherences between two groups at rest. These findings comprise evidence that MCI patients have higher EEG power at rest, and higher EEG power and coherence during working conditions. It suggests that MCI may be associated with compensatory processes at

  7. PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

    OpenAIRE

    Bao, Forrest Sheng; Liu, Xin; Zhang, Christina

    2011-01-01

    Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting ...

  8. NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

    Directory of Open Access Journals (Sweden)

    João Paulo Silva Cunha

    Full Text Available Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG findings and characteristic seizure-induced body movements--called seizure semiology. Thus, synchronous EEG and (2Dvideo recording systems (known as Video-EEG are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE (n = 19 and extratemporal (ETE brain regions (n = 23. The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3 vs. 0.14 m(3, p<0.05, respectively. Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01 and presented higher jerking levels (mean = 345 ms(-3 vs 172 ms(-3, p<0.05. Our newly implemented 3D approach is faster by 87.5% in extracting body

  9. NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

    Science.gov (United States)

    Cunha, João Paulo Silva; Choupina, Hugo Miguel Pereira; Rocha, Ana Patrícia; Fernandes, José Maria; Achilles, Felix; Loesch, Anna Mira; Vollmar, Christian; Hartl, Elisabeth; Noachtar, Soheyl

    2016-01-01

    Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure-induced body movements--called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body

  10. Data-driven analysis of simultaneous EEG/fMRI reveals neurophysiological phenotypes of impulse control.

    Science.gov (United States)

    Schmüser, Lena; Sebastian, Alexandra; Mobascher, Arian; Lieb, Klaus; Feige, Bernd; Tüscher, Oliver

    2016-09-01

    Response inhibition is the ability to suppress inadequate but prepotent or ongoing response tendencies. A fronto-striatal network is involved in these processes. Between-subject differences in the intra-individual variability have been suggested to constitute a key to pathological processes underlying impulse control disorders. Single-trial EEG/fMRI analysis allows to increase sensitivity for inter-individual differences by incorporating intra-individual variability. Thirty-eight healthy subjects performed a visual Go/Nogo task during simultaneous EEG/fMRI. Of 38 healthy subjects, 21 subjects reliably showed Nogo-related ICs (Nogo-IC-positive) while 17 subjects (Nogo-IC-negative) did not. Comparing both groups revealed differences on various levels: On trait level, Nogo-IC-negative subjects scored higher on questionnaires regarding attention deficit/hyperactivity disorder; on a behavioral level, they displayed slower response times (RT) and higher intra-individual RT variability while both groups did not differ in their inhibitory performance. On the neurophysiological level, Nogo-IC-negative subjects showed a hyperactivation of left inferior frontal cortex/insula and left putamen as well as significantly reduced P3 amplitudes. Thus, a data-driven approach for IC classification and the resulting presence or absence of early Nogo-specific ICs as criterion for group selection revealed group differences at behavioral and neurophysiological levels. This may indicate electrophysiological phenotypes characterized by inter-individual variations of neural and behavioral correlates of impulse control. We demonstrated that the inter-individual difference in an electrophysiological correlate of response inhibition is correlated with distinct, potentially compensatory neural activity. This may suggest the existence of electrophysiologically dissociable phenotypes of behavioral and neural motor response inhibition with the Nogo-IC-positive phenotype possibly providing

  11. Decreased Modulation of EEG Oscillations in High-Functioning Autism During a Motor Control Task

    Directory of Open Access Journals (Sweden)

    Joshua Benjamin Ewen

    2016-05-01

    Full Text Available Autism spectrum disorders (ASD are thought to result in part from altered cortical excitatory-inhibitory balance; this pathophysiology may impact the generation of oscillations on EEG. We investigated premotor-parietal cortical physiology associated with praxis, which has strong theoretical and empirical associations with ASD symptomatology. 25 children with high-functioning ASD (HFA and 33 controls performed a praxis task involving the pantomiming of tool use, while EEG was recorded. We assessed task-related modulation of signal power in alpha and beta frequency bands. Compared with controls, subjects with HFA showed 27% less left central (motor/premotor beta (18-22 Hz event-related desynchronization (ERD (p = 0.030, as well as 24% less left parietal alpha (7-13 Hz ERD (p = 0.046. Within the HFA group, blunting of central ERD attenuation was associated with impairments in clinical measures of praxis imitation (r = -0.4; p = 0.04 and increased autism severity (r = 0.48; p = 0.016. The modulation of central beta activity is associated, among other things, with motor imagery, which may be necessary for imitation. Impaired imitation has been associated with core features of ASD. Altered modulation of oscillatory activity may be mechanistically involved in those aspects of motor network function that relate to the core symptoms of ASD.

  12. EEG Mu Rhythm and Imitation Impairments in Individuals with Autism Spectrum Disorder

    Science.gov (United States)

    Bernier, R.; Dawson, G.; Webb, S.; Murias, M.

    2009-01-01

    Imitation ability has consistently been shown to be impaired in individuals with autism. A dysfunctional execution/observation matching system has been proposed to account for this impairment. The EEG mu rhythm is believed to reflect an underlying execution/observation matching system. This study investigated evidence of differential mu rhythm attenuation during the observation, execution, and imitation of movements and examined its relation to behaviorally assessed imitation abilities. Fourteen high-functioning adults with autism spectrum disorder (ASD) and 15 IQ- and age-matched typical adults participated. On the behavioral imitation task, adults with ASD demonstrated significantly poorer performance compared to typical adults in all domains of imitation ability. On the EEG task, both groups demonstrated significant attenuation of the mu rhythm when executing an action. However, when observing movement, the individuals with ASD showed significantly reduced attenuation of the mu wave. Behaviorally assessed imitation skills were correlated with degree of mu wave attenuation during observation of movement. These findings suggest that there is execution/observation matching system dysfunction in individuals with autism and that this matching system is related to degree of impairment in imitation abilities. PMID:17451856

  13. The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation.

    Science.gov (United States)

    Olejarczyk, Elzbieta; Bogucki, Piotr; Sobieszek, Aleksander

    2017-01-01

    Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

  14. Localizing Brain Activity from Multiple Distinct Sources via EEG

    Directory of Open Access Journals (Sweden)

    George Dassios

    2014-01-01

    Full Text Available An important question arousing in the framework of electroencephalography (EEG is the possibility to recognize, by means of a recorded surface potential, the number of activated areas in the brain. In the present paper, employing a homogeneous spherical conductor serving as an approximation of the brain, we provide a criterion which determines whether the measured surface potential is evoked by a single or multiple localized neuronal excitations. We show that the uniqueness of the inverse problem for a single dipole is closely connected with attaining certain relations connecting the measured data. Further, we present the necessary and sufficient conditions which decide whether the collected data originates from a single dipole or from numerous dipoles. In the case where the EEG data arouses from multiple parallel dipoles, an isolation of the source is, in general, not possible.

  15. Early EEG for outcome prediction of postanoxic coma: prospective cohort study with cost-minimization analysis.

    Science.gov (United States)

    Sondag, Lotte; Ruijter, Barry J; Tjepkema-Cloostermans, Marleen C; Beishuizen, Albertus; Bosch, Frank H; van Til, Janine A; van Putten, Michel J A M; Hofmeijer, Jeannette

    2017-05-15

    We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis. Early prognosis contributes to communication between doctors and family, and may prevent inappropriate treatment. A prospective cohort study including 430 subsequent comatose patients after cardiac arrest was conducted at intensive care units of two teaching hospitals. Continuous EEG was started within 12 hours after cardiac arrest and continued up to 3 days. EEG patterns were visually classified as unfavorable (isoelectric, low-voltage, or burst suppression with identical bursts) or favorable (continuous patterns) at 12 and 24 hours after cardiac arrest. Outcome at 6 months was classified as good (cerebral performance category (CPC) 1 or 2) or poor (CPC 3, 4, or 5). Predictive values of EEG measures and cost-consequences from a hospital perspective were investigated, assuming EEG-based decision- making about withdrawal of life-sustaining treatment in the case of a poor predicted outcome. Poor outcome occurred in 197 patients (51% of those included in the analyses). Unfavorable EEG patterns at 24 hours predicted a poor outcome with specificity of 100% (95% CI 98-100%) and sensitivity of 29% (95% CI 22-36%). Favorable patterns at 12 hours predicted good outcome with specificity of 88% (95% CI 81-93%) and sensitivity of 51% (95% CI 42-60%). Treatment withdrawal based on an unfavorable EEG pattern at 24 hours resulted in a reduced mean ICU length of stay without increased mortality in the long term. This gave small cost reductions, depending on the timing of withdrawal. Early EEG contributes to reliable prediction of good or poor outcome of postanoxic coma and may lead to reduced length of ICU stay. In turn, this may bring small cost reductions.

  16. 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

  17. EEG and MEG Data Analysis in SPM8

    Directory of Open Access Journals (Sweden)

    Vladimir Litvak

    2011-01-01

    Full Text Available SPM is a free and open source software written in MATLAB (The MathWorks, Inc.. In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii dynamic causal modelling (DCM, an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra, induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI and batching tools.

  18. EEG and MEG data analysis in SPM8.

    Science.gov (United States)

    Litvak, Vladimir; Mattout, Jérémie; Kiebel, Stefan; Phillips, Christophe; Henson, Richard; Kilner, James; Barnes, Gareth; Oostenveld, Robert; Daunizeau, Jean; Flandin, Guillaume; Penny, Will; Friston, Karl

    2011-01-01

    SPM is a free and open source software written in MATLAB (The MathWorks, Inc.). In addition to standard M/EEG preprocessing, we presently offer three main analysis tools: (i) statistical analysis of scalp-maps, time-frequency images, and volumetric 3D source reconstruction images based on the general linear model, with correction for multiple comparisons using random field theory; (ii) Bayesian M/EEG source reconstruction, including support for group studies, simultaneous EEG and MEG, and fMRI priors; (iii) dynamic causal modelling (DCM), an approach combining neural modelling with data analysis for which there are several variants dealing with evoked responses, steady state responses (power spectra and cross-spectra), induced responses, and phase coupling. SPM8 is integrated with the FieldTrip toolbox , making it possible for users to combine a variety of standard analysis methods with new schemes implemented in SPM and build custom analysis tools using powerful graphical user interface (GUI) and batching tools.

  19. Plethysmogram and EEG: Effects of Music and Voice Sound

    Science.gov (United States)

    Miao, Tiejun; Oyama-Higa, Mayumi; Sato, Sadaka; Kojima, Junji; Lin, Juan; Reika, Sato

    2011-06-01

    We studied a relation of chaotic dynamics of finger plethysmogram to complexity of high cerebral center in both theoretical and experimental approaches. We proposed a mathematical model to describe emergence of chaos in finger tip pulse wave, which gave a theoretical prediction indicating increased chaoticity in higher cerebral center leading to an increase of chaos dynamics in plethysmograms. We designed an experiment to observe scalp-EEG and finger plethysmogram using two mental tasks to validate the relationship. We found that scalp-EEG showed an increase of the largest Lyapunov exponents (LLE) during speaking certain voices. Topographical scalp map of LLE showed enhanced arise around occipital and right cerebral area. Whereas there was decreasing tendency during listening music, where LLE scalp map revealed a drop around center cerebral area. The same tendency was found for LLE obtained from finger plethysmograms as ones of EEG under either speaking or listening tasks. The experiment gave results that agreed well with the theoretical relation derived from our proposed model.

  20. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

    Directory of Open Access Journals (Sweden)

    Shanzhi Xu

    2018-02-01

    Full Text Available The recorded electroencephalography (EEG signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

  1. Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal.

    Science.gov (United States)

    Xu, Shanzhi; Hu, Hai; Ji, Linhong; Wang, Peng

    2018-02-26

    The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.

  2. Attenuation of artifacts in EEG signals measured inside an MRI scanner using constrained independent component analysis

    International Nuclear Information System (INIS)

    Rasheed, Tahir; Lee, Young-Koo; Lee, Soo Yeol; Kim, Tae-Seong

    2009-01-01

    Integration of electroencephalography (EEG) and functional magnetic imaging (fMRI) resonance will allow analysis of the brain activities at superior temporal and spatial resolution. However simultaneous acquisition of EEG and fMRI is hindered by the enhancement of artifacts in EEG, the most prominent of which are ballistocardiogram (BCG) and electro-oculogram (EOG) artifacts. The situation gets even worse if the evoked potentials are measured inside MRI for their minute responses in comparison to the spontaneous brain responses. In this study, we propose a new method of attenuating these artifacts from the spontaneous and evoked EEG data acquired inside an MRI scanner using constrained independent component analysis with a priori information about the artifacts as constraints. With the proposed techniques of reference function generation for the BCG and EOG artifacts as constraints, our new approach performs significantly better than the averaged artifact subtraction (AAS) method. The proposed method could be an alternative to the conventional ICA method for artifact attenuation, with some advantages. As a performance measure we have achieved much improved normalized power spectrum ratios (INPS) for continuous EEG and correlation coefficient (cc) values with outside MRI visual evoked potentials for visual evoked EEG, as compared to those obtained with the AAS method. The results show that our new approach is more effective than the conventional methods, almost fully automatic, and no extra ECG signal measurements are involved

  3. Evaluation of EEG Features in Decoding Individual Finger Movements from One Hand

    Directory of Open Access Journals (Sweden)

    Ran Xiao

    2013-01-01

    Full Text Available With the advancements in modern signal processing techniques, the field of brain-computer interface (BCI is progressing fast towards noninvasiveness. One challenge still impeding these developments is the limited number of features, especially movement-related features, available to generate control signals for noninvasive BCIs. A few recent studies investigated several movement-related features, such as spectral features in electrocorticography (ECoG data obtained through a spectral principal component analysis (PCA and direct use of EEG temporal data, and demonstrated the decoding of individual fingers. The present paper evaluated multiple movement-related features under the same task, that is, discriminating individual fingers from one hand using noninvasive EEG. The present results demonstrate the existence of a broadband feature in EEG to discriminate individual fingers, which has only been identified previously in ECoG. It further shows that multiple spectral features obtained from the spectral PCA yield an average decoding accuracy of 45.2%, which is significantly higher than the guess level (P<0.05 and other features investigated (P<0.05, including EEG spectral power changes in alpha and beta bands and EEG temporal data. The decoding of individual fingers using noninvasive EEG is promising to improve number of features for control, which can facilitate the development of noninvasive BCI applications with rich complexity.

  4. Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

    Science.gov (United States)

    Adib, Mani; Cretu, Edmond

    2013-01-01

    We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

  5. Hybrid EEG-fNIRS-Based Eight-Command Decoding for BCI: Application to Quadcopter Control.

    Science.gov (United States)

    Khan, Muhammad Jawad; Hong, Keum-Shik

    2017-01-01

    In this paper, a hybrid electroencephalography-functional near-infrared spectroscopy (EEG-fNIRS) scheme to decode eight active brain commands from the frontal brain region for brain-computer interface is presented. A total of eight commands are decoded by fNIRS, as positioned on the prefrontal cortex, and by EEG, around the frontal, parietal, and visual cortices. Mental arithmetic, mental counting, mental rotation, and word formation tasks are decoded with fNIRS, in which the selected features for classification and command generation are the peak, minimum, and mean ΔHbO values within a 2-s moving window. In the case of EEG, two eyeblinks, three eyeblinks, and eye movement in the up/down and left/right directions are used for four-command generation. The features in this case are the number of peaks and the mean of the EEG signal during 1 s window. We tested the generated commands on a quadcopter in an open space. An average accuracy of 75.6% was achieved with fNIRS for four-command decoding and 86% with EEG for another four-command decoding. The testing results show the possibility of controlling a quadcopter online and in real-time using eight commands from the prefrontal and frontal cortices via the proposed hybrid EEG-fNIRS interface.

  6. Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

    Directory of Open Access Journals (Sweden)

    Mani Adib

    2013-01-01

    Full Text Available We present a new method for removing artifacts in electroencephalography (EEG records during Galvanic Vestibular Stimulation (GVS. The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters.

  7. 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.

  8. 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 %

  9. Correlation between intra- and extracranial background EEG

    DEFF Research Database (Denmark)

    Duun-Henriksen, Jonas; Kjaer, Troels W.; Madsen, Rasmus E.

    2012-01-01

    Scalp EEG is the most widely used modality to record the electrical signals of the brain. It is well known that the volume conduction of these brain waves through the brain, cerebrospinal fluid, skull and scalp reduces the spatial resolution and the signal amplitude. So far the volume conduction...... has primarily been investigated by realistic head models or interictal spike analysis. We have set up a novel and more realistic experiment that made it possible to compare the information in the intra- and extracranial EEG. We found that intracranial EEG channels contained correlated patterns when...... placed less than 30 mm apart, that intra- and extracranial channels were partly correlated when placed less than 40 mm apart, and that extracranial channels probably were correlated over larger distances. The underlying cortical area that influences the extracranial EEG is found to be up to 45 cm2...

  10. Amplitude-Integrated EEG in the Newborn

    Directory of Open Access Journals (Sweden)

    J Gordon Millichap

    2008-11-01

    Full Text Available Th value of amplitude-integrated electroencephalography (aEEG in the newborn is explored by researchers at Washington University, St Louis; Wilhelmina Children’s Hospital, Utrecht, Netherlands; and Uppsala University Hospital, Sweden.

  11. Predictive Values of Electroencephalography (EEG) in Epilepsy ...

    African Journals Online (AJOL)

    Predictive Values of Electroencephalography (EEG) in Epilepsy Patients with Abnormal Behavioural Symptoms. OR Obiako, SO Adeyemi, TL Sheikh, LF Owolabi, MA Majebi, MO Gomina, F Adebayo, EU Iwuozo ...

  12. EEG analysis in a telemedical virtual world

    NARCIS (Netherlands)

    Jovanov, E.; Starcevic, D.; Samardzic, A.; Marsh, A.; Obrenovic, Z.

    1999-01-01

    Telemedicine creates virtual medical collaborative environments. We propose here a novel concept of virtual medical devices (VMD) for telemedical applications. VMDs provide different views on biomedical recordings and efficient signal analysis. In this paper we present a telemedical EEG analysis

  13. MRI in temporal lobe epilepsy. Correlation between EEG, SPECT and clinical features

    International Nuclear Information System (INIS)

    Uesugi, Hideji; Onuma, Teiichi; Matsuda, Hiroshi; Ishida, Shiro

    1996-01-01

    The relationship between MRI, SPECT, EEG and clinical features in temporal lobe epilepsy was investigated. Subjects were 162 patients (84 males, 78 females) whose average age was 38.1±12.1 years. SPECT was carried out in 45 patients. The results were as follows: abnormal MR images were obtained in 36% of the group without epileptic discharge, and in 42% of the group with temporal spikes. There was no correlation between epileptic discharge in EEG and MRI abnormality. The lateralities of epileptic discharge and MRI were in disagreement in 9 of 39 patients (23%), indicating that determining the epileptic focus from scalp EEG was difficult. There was no correlation between the basic activity in EEG and abnormality in MRI. The rate of abnormal SPECT (89%) was higher than that of abnormal MRI (40%). The rate of the group with ictal automatism (52%) was higher than that of the group without ictal automatism (35%). The rate of abnormal MR images was high in the group with encephalitis (73%). The rate was higher in the group with febrile convulsion (62%) than in the group without it (28%). The rate of the abnormal MR images was higher in the group with a seizure frequency of at least several mal/month (48%) than in the group with a seizure frequency of less than several mal/year (29%). (author)

  14. Thinner abuse alters peak of frequency of EEG spectra analyses El abuso de tíner altera el pico de frecuencia del análisis espectral del EEG

    Directory of Open Access Journals (Sweden)

    Adrián Poblano

    2006-12-01

    Full Text Available STUDY OBJECTIVE: The aim of the investigation was to use electroencephalography (EEG to study whether long-term thinner abuse may result in the slowing, disorganization and asymmetry of the EEG cortical rhythms. METHOD: Twenty-two patients attending with antecedent of thinner abuse only, and twenty two controls without alcohol, smoking, and drug abuse in the same age range and gender were studied. EEG recording were compared by means of the analyses of peak of frequency (POF, frequency of disorganization, and asymmetry of the background activity in patients and controls at rest eyes-closed condition in electrodes P3, P4, O1, and O2. RESULTS: Significant differences in POF among groups was observed in P3 and P4 location showing lower values in thinner abusers, but not in O1 and O2 locations. Frequencies of disorganization and asymmetry showed significantly higher proportions in thinner abusers. Bivariate correlations among POF at the four electrode location and time of thinner abuse showed significant values. However after partial correlation calculation correcting for age, significant values disappeared. CONCLUSION: Thus thinner abuse relates with slowing of POF in the EEG of patients with thinner abuse associated with disorganization, and asymmetry depending on time of abuse.OBJETIVO: Utilizar el electroencefalograma (EEG para estudiar si el abuso o intoxicación crónica por tíner produce lentificación, desorganización y asimetría de la actividad eléctrica cortical. MÉTODO: Se estudiaron 22 pacientes con antecedentes de intoxicación crónica por tíner y 22 sujetos sin antecedentes de abuso de tíner, alcohol, cigarro o drogas en el mismo rango de edad y en igual número de acuerdo al género. Se registro el EEG cuantitativo y se compararon: el promedio del pico de frecuencia (PoF, la frecuencia de desorganización de la actividad de fondo y la presencia de asimetría inter-hemisférica en la condición de reposo físico y mental entre

  15. Fast automatic analysis of antenatal dexamethasone on micro-seizure activity in the EEG

    International Nuclear Information System (INIS)

    Rastin, S.J.; Unsworth, C.P.; Bennet, L.

    2010-01-01

    Full text: In this work wc develop an automatic scheme for studying the effect of the antenatal Dexamethasone on the EEG activity. To do so an FFT (Fast Fourier Transform) based detector was designed and applied to the EEG recordings obtained from two groups of fetal sheep. Both groups received two injections with a time delay of 24 h between them. However the applied medicine was different for each group (Dex and saline). The detector developed was used to automatically identify and classify micro-seizures that occurred in the frequency bands corresponding to the EEG transients known as slow waves (2.5 14 Hz). For each second of the data recordings the spectrum was computed and the rise of the energy in each predefined frequency band then counted when the energy level exceeded a predefined corresponding threshold level (Where the threshold level was obtained from the long term average of the spectral points at each band). Our results demonstrate that it was possible to automatically count the micro-seizures for the three different bands in a time effective manner. It was found that the number of transients did not strongly depend on the nature of the injected medicine which was consistent with the results manually obtained by an EEG expert. Tn conclusion, the automatic detection scheme presented here would allow for rapid micro-seizure event identification of hours of highly sampled EEG data thus providing a valuable time-saving device.

  16. Connectivity Measures in EEG Microstructural Sleep Elements.

    Science.gov (United States)

    Sakellariou, Dimitris; Koupparis, Andreas M; Kokkinos, Vasileios; Koutroumanidis, Michalis; Kostopoulos, George K

    2016-01-01

    During Non-Rapid Eye Movement sleep (NREM) the brain is relatively disconnected from the environment, while connectedness between brain areas is also decreased. Evidence indicates, that these dynamic connectivity changes are delivered by microstructural elements of sleep: short periods of environmental stimuli evaluation followed by sleep promoting procedures. The connectivity patterns of the latter, among other aspects of sleep microstructure, are still to be fully elucidated. We suggest here a methodology for the assessment and investigation of the connectivity patterns of EEG microstructural elements, such as sleep spindles. The methodology combines techniques in the preprocessing, estimation, error assessing and visualization of results levels in order to allow the detailed examination of the connectivity aspects (levels and directionality of information flow) over frequency and time with notable resolution, while dealing with the volume conduction and EEG reference assessment. The high temporal and frequency resolution of the methodology will allow the association between the microelements and the dynamically forming networks that characterize them, and consequently possibly reveal aspects of the EEG microstructure. The proposed methodology is initially tested on artificially generated signals for proof of concept and subsequently applied to real EEG recordings via a custom built MATLAB-based tool developed for such studies. Preliminary results from 843 fast sleep spindles recorded in whole night sleep of 5 healthy volunteers indicate a prevailing pattern of interactions between centroparietal and frontal regions. We demonstrate hereby, an opening to our knowledge attempt to estimate the scalp EEG connectivity that characterizes fast sleep spindles via an "EEG-element connectivity" methodology we propose. The application of the latter, via a computational tool we developed suggests it is able to investigate the connectivity patterns related to the occurrence

  17. 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.

  18. Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach.

    Science.gov (United States)

    Jäncke, Lutz; Alahmadi, Nsreen

    2016-01-01

    In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical

  19. EEG Based Inference of Spatio-Temporal Brain Dynamics

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese

    Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...

  20. 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.

  1. A Game Player Expertise Level Classification System Using Electroencephalography (EEG

    Directory of Open Access Journals (Sweden)

    Syed Muhammad Anwar

    2017-12-01

    Full Text Available The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users’ attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88 % , when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications.

  2. 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.

  3. Blind Source Separation of Event-Related EEG/MEG.

    Science.gov (United States)

    Metsomaa, Johanna; Sarvas, Jukka; Ilmoniemi, Risto Juhani

    2017-09-01

    Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain-computer interfaces, artifact removal, and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses, which is challenging because of nonstationary data. We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA), which is tailored for event-related multitrial data. The method is based on approximate joint diagonalization of multiple covariance matrices estimated from the data at separate latencies. We further show how to extend the methodology for autocovariance matrices and how to apply BSS methods suitable for piecewise stationary data to event-related responses. We compared several BSS approaches by using simulated EEG as well as measured somatosensory and transcranial magnetic stimulation (TMS) evoked EEG. Among the compared methods, MUCA was the most tolerant one to noise, TMS artifacts, and other challenges in the data. With measured somatosensory data, over half of the estimated components were found to be similar by MUCA and independent component analysis. MUCA was also stable when tested with several input datasets. MUCA is based on simple assumptions, and the results suggest that MUCA is robust with nonideal data. Event-related responses and BSS are valuable and popular tools in neuroscience. Correctly designed BSS is an efficient way of identifying artifactual and neural processes from nonstationary event-related data.

  4. Effect of levomepromazine on EEG and on clinical side effects after lumbar myelography with metrizamide

    International Nuclear Information System (INIS)

    Standnes, B.; Oftedal, S.-I.; Weber, H.

    1982-01-01

    In patients with lumbago-sciatica levomepromazine is a potent supplement to analgetics in pain treatment. The hypothesis that neuroleptics increase the risk of epileptic seizures after metrizamide myelography was not comfirmed in a series of 77 patients, 26 with and 51 without levomepromazine medication, before and after lumbar metrizamide myelography. No differences existed between the groups with regard to the appearance of EEG abnormalities such as slow waves or spikes. Mild side effects were more frequent in the levomepromazine group, except nausea and vomiting. Lumbar metrizamide epidurography in 30 patients did not cause any abnormal EEG. (Auth.)

  5. Effect of levomepromazine on EEG and on clinical side effects after lumbar myelography with metrizamide

    Energy Technology Data Exchange (ETDEWEB)

    Standnes, B.; Oftedal, S.I.; Weber, H. (Ullevaal Sykehus, Oslo (Norway))

    1982-01-01

    In patients with lumbago-sciatica levomepromazine is a potent supplement to analgesics in pain treatment. The hypothesis that neuroleptics increase the risk of epileptic seizures after metrizamide myelography was not comfirmed in a series of 77 patients, 26 with and 51 without levomepromazine medication, before and after lumbar metrizamide myelography. No differences existed between the groups with regard to the appearance of EEG abnormalities such as slow waves or spikes. Mild side effects were more frequent in the levomepromazine group, except nausea and vomiting. Lumbar metrizamide epidurography in 30 patients did not cause any abnormal EEG.

  6. Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way.

    Science.gov (United States)

    López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; García, Roberto

    2016-01-01

    Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does

  7. Method for Improving EEG Based Emotion Recognition by Combining It with Synchronized Biometric and Eye Tracking Technologies in a Non-invasive and Low Cost Way

    Science.gov (United States)

    López-Gil, Juan-Miguel; Virgili-Gomá, Jordi; Gil, Rosa; Guilera, Teresa; Batalla, Iolanda; Soler-González, Jorge; García, Roberto

    2016-01-01

    Technical advances, particularly the integration of wearable and embedded sensors, facilitate tracking of physiological responses in a less intrusive way. Currently, there are many devices that allow gathering biometric measurements from human beings, such as EEG Headsets or Health Bracelets. The massive data sets generated by tracking of EEG and physiology may be used, among other things, to infer knowledge about human moods and emotions. Apart from direct biometric signal measurement, eye tracking systems are nowadays capable of determining the point of gaze of the users when interacting in ICT environments, which provides an added value research on many different areas, such as psychology or marketing. We present a process in which devices for eye tracking, biometric, and EEG signal measurements are synchronously used for studying both basic and complex emotions. We selected the least intrusive devices for different signal data collection given the study requirements and cost constraints, so users would behave in the most natural way possible. On the one hand, we have been able to determine basic emotions participants were experiencing by means of valence and arousal. On the other hand, a complex emotion such as empathy has also been detected. To validate the usefulness of this approach, a study involving forty-four people has been carried out, where they were exposed to a series of affective stimuli while their EEG activity, biometric signals, and eye position were synchronously recorded to detect self-regulation. The hypothesis of the work was that people who self-regulated would show significantly different results when analyzing their EEG data. Participants were divided into two groups depending on whether Electro Dermal Activity (EDA) data indicated they self-regulated or not. The comparison of the results obtained using different machine learning algorithms for emotion recognition shows that using EEG activity alone as a predictor for self-regulation does

  8. The early electroclinical manifestations of infantile spasms: A video EEG study

    Directory of Open Access Journals (Sweden)

    Mary Iype

    2016-01-01

    Full Text Available Purpose: Infantile spasms are described as flexor extensor and mixed; but more features of their semiology and ictal electroencephalography (EEG changes are sparse in the literature. The purpose of the study was to describe the clinical and ictal video-EEG characteristics of consecutive cases with infantile spasms and to try to find an association with the etiology. Materials and Methods: The clinical phenomenology and EEG characteristics on video-EEG were analyzed in 16 babies with infantile spasms. Results: A total of 869 spasms were reviewed. Nine (56.3% showed focal seizures at least once during the recording and 1 (6.3% had multifocal myoclonus in addition to the spasms. The duration of the cluster and interval between spasms was totally variable in all patients. Lateralizing phenomena were present in at least some of the spasms in all patients. Unilateral manual automatism in the form of holding the pinna was noted in three patients following the spasm. The ictal EEG activity in the majority (75% was the slow wave. Four (25% showed fast generalized spindle-like ictal discharges. Spikes, spike and wave activity, or electrodecremental pattern alone during the ictus was seen in none. On bivariate analysis, no factor noted on the video EEG had association with the etiology. Conclusion: Infantile spasms could be associated with focal and other seizures, has unique, non-uniform and variable semiology from patient to patient. The ictal EEG manifestation in the majority (75% of our patients was the slow wave transient with 25% showing generalized fast spindle-like activity.

  9. 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.

  10. Short and long-term effects of sham-controlled prefrontal EEG-neurofeedback training in healthy subjects.

    Science.gov (United States)

    Engelbregt, H J; Keeser, D; van Eijk, L; Suiker, E M; Eichhorn, D; Karch, S; Deijen, J B; Pogarell, O

    2016-04-01

    In this study we evaluated long-term effects of frontal beta EEG-neurofeedback training (E-NFT) on healthy subjects. We hypothesized that E-NFT can change frontal beta activity in the long-term and that changes in frontal beta EEG activity are accompanied by altered cognitive performance. 25 healthy subjects were included and randomly assigned to active or sham E-NFT. On average the subjects underwent 15 E-NFT training sessions with a training duration of 45 min. Resting-state EEG was recorded prior to E-NFT training (t1) and in a 3-year follow-up (t3). Compared to sham E-NFT, which was used for the control group, real E-NFT increased beta activity in a predictable way. This increase was maintained over a period of three years post training. However, E-NFT did not result in significantly improved cognitive performance. Based on our results, we conclude that EEG-NFT can selectively modify EEG beta activity both in short and long-term. This is a sham controlled EEG neurofeedback study demonstrating long-term effects in resting state EEG. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  11. EEG II. Annexes and regulations. Comment; EEG II. Anlagen und Verordnungen. Kommentar

    Energy Technology Data Exchange (ETDEWEB)

    Frenz, Walter (ed.) [Rheinisch-Westfaelische Technische Hochschule Aachen (Germany). Berg-, Umwelt- und Europarecht

    2016-11-01

    Berlin commentary EEG II: safe through the paraphernalia Like hardly any other law, the Renewable Energies Act (EEG) is subject to constant changes. With the 2014 amendment, the EEG was fundamentally redesigned. This makes the application of the complex rules a challenge even for experts. In addition, the sub-rules contain important statements in the form of numerous annexes and regulations - with the EEG amendment 2014, this has become even more detailed. In it, many calculations are only defined in detail and the legal provisions of the EEG are made more definite and supplemented. The Berlin commentary EEG II accompanies you expertly through this complex matter. Experts explain the widely divergent rules in practice. If necessary for a better understanding, the provisions of the EEG 2014 are also explained. Consistently designed for your practice As a buyer of the work, you also benefit from access to an extensive, regularly updated database. This contains important legal energy regulations of the EU, the federal government and the countries. Even earlier legal positions remain searchable and can be conveniently compared with current versions. So you can see at a glance what has changed. [German] Berliner Kommentar EEG II: sicher durch den Paragrafengeflecht Wie kaum ein anderes Gesetz ist das Erneuerbare-Energien-Gesetz (EEG) staendigen Aenderungen unterworfen. Mit der Novelle 2014 wurde das EEG grundlegend umgestaltet. Dies macht die Anwendung der komplexen Regeln selbst fuer Experten zu einer Herausforderung. Zudem enthaelt auch das untergesetzliche Regelwerk wichtige Aussagen in Form zahlreicher Anlagen und Verordnungen - mit der EEG-Novelle 2014 ist dieses noch ausfuehrlicher geworden. In ihm werden viele Berechnungen erst naeher festgelegt und gesetzliche Bestimmungen des EEG entscheidend konkretisiert und ergaenzt. Der Berliner Kommentar EEG II begleitet Sie fachkundig durch diese komplexe Materie. Experten erlaeutern Ihnen praxisorientiert die

  12. EEG-neurofeedback training of beta band (12-22Hz) affects alpha and beta frequencies - A controlled study of a healthy population.

    Science.gov (United States)

    Jurewicz, Katarzyna; Paluch, Katarzyna; Kublik, Ewa; Rogala, Jacek; Mikicin, Mirosław; Wróbel, Andrzej

    2018-01-08

    The frequency-function relation of various EEG bands has inspired EEG-neurofeedback procedures intending to improve cognitive abilities in numerous clinical groups. In this study, we administered EEG-neurofeedback (EEG-NFB) to a healthy population to determine the efficacy of this procedure. We evaluated feedback manipulation in the beta band (12-22Hz), known to be involved in visual attention processing. Two groups of healthy adults were trained to either up- or down-regulate beta band activity, thus providing mutual control. Up-regulation training induced increases in beta and alpha band (8-12Hz) amplitudes during the first three sessions. Group-independent increases in the activity of both bands were observed in the later phase of training. EEG changes were not matched by measured behavioural indices of attention. Parallel changes in the two bands challenge the idea of frequency-specific EEG-NFB protocols and suggest their interdependence. Our study exposes the possibility (i) that the alpha band is more prone to manipulation, and (ii) that changes in the bands' amplitudes are independent from specified training. We therefore encourage a more comprehensive approach to EEG-neurofeedback training embracing physiological and/or operational relations among various EEG bands. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  14. Cordance derived from REM sleep EEG as a biomarker for treatment response in depression--a naturalistic study after antidepressant medication.

    Science.gov (United States)

    Adamczyk, Marek; Gazea, Mary; Wollweber, Bastian; Holsboer, Florian; Dresler, Martin; Steiger, Axel; Pawlowski, Marcel

    2015-04-01

    To evaluate whether prefrontal cordance in theta frequency band derived from REM sleep EEG after the first week of antidepressant medication could characterize the treatment response after 4 weeks of therapy in depressed patients. 20 in-patients (15 females, 5 males) with a depressive episode and 20 healthy matched controls were recruited into 4-week, open label, case-control study. Patients were treated with various antidepressants. No significant differences in age (responders (mean ± SD): 45 ± 22) years; non-responders: 49 ± 12 years), medication or Hamilton Depression Rating Scale (HAM-D) score (responders: 23.8 ± 4.5; non-responders 24.5 ± 7.6) at inclusion into the study were found between responders and non-responders. Response to treatment was defined as a ≥50% reduction of HAM-D score at the end of four weeks of active medication. Sleep EEG of patients was recorded after the first and the fourth week of medication. Cordance was computed for prefrontal EEG channels in theta frequency band during tonic REM sleep. The group of 8 responders had significantly higher prefrontal theta cordance in relation to the group of 12 non-responders after the first week of antidepressant medication. This finding was significant also when controlling for age, gender and number of previous depressive episodes (F1,15 = 6.025, P = .027). Furthermore, prefrontal cordance of all patients showed significant positive correlation (r = 0.52; P = .019) with the improvement of HAM-D score between the inclusion week and fourth week of medication. The results suggest that prefrontal cordance derived from REM sleep EEG could provide a biomarker for the response to antidepressant treatment in depressed patients. Copyright © 2015 Elsevier Ltd. All rights reserved.

  15. [The Influence of the Functional State of Brain Regulatory Structures on the Programming, Selective Regulation and Control of Cognitive Activity in Children. Report I: Neuropsychological and EEG Analysis of Age-Related Changes in Brain Regulatory Functions in Children Aged 9-12 Years].

    Science.gov (United States)

    Semenova, A; Machinskaya, R I; Lomakin, D I

    2015-01-01

    Age-related changes in brain regulatory functions in children aged from 9 to 12 years with typical development were studied by means of neuropsychological and EEG analysis. The participants of the study were 107 children without learning difficulties and behavior deviations; they were devided into three groups (9-10, 10-11 and 11-12 years). The neuropsychological tests revealed nonlinear age-related changes in different executive brain functions. The group of 10-11-year-old children showed better results in programming, in- hibition of impulsive reactions and in the perception of socially relevant information than the group of 9-10- year-old children. At the same time, these children had more difficulties with selective activity regulation as compared with the younger group. The difficulties were mainly caused by switching from one element of the program to another and by retention of learned sequence of actions. These children also showed a lower level of motivation for task performance. The children aged 11-12 years had less difficulties with selective activity regulation; however, impulsive behavior was more frequent; these children also had a higher level of task performance motivation than in children aged 10-11 years. The analysis of resting state EEG revealed age-related differences in deviated EEG patterns associated with non-optimal functioning of fronto-thalamic system and hypothalamic structures. The incidence of these two types of EEG patterns was significantly higher in children aged 10-11 years as compared with children aged 9-10 years. The EEG of the groups of 10-11 and 11-12-years-old children did not show any significant differences.

  16. Relationships between automated EEG and cranial CT in patients with senile dementia

    International Nuclear Information System (INIS)

    Mori, Katsumi; Miyasaka, Mutue; Nakano, Takashi; Ohtaka, Tadashi

    1988-01-01

    The degree of dementia was examined in relation to findings of automated EEG and cranial CT in 50 demented old patients aged 51 - 86. Slow waves on EEC, and dilated ventricle and atrophied cortex on cranial CT were frequently associated with poor psychological scores. As the degree of dilated ventricle increased, the frequency and amplitude of slow waves increased and the frequency and continuity of α waves decreased. Dilated ventricle was more strongly correlated with abnormal EEG findings than atrophied cortex. This was more marked in cases of severer patients. Abnormal EEC findings associated with poor psychological scores were more frequently observed in the group of patients with vascular dementia (VD, n = 30) than the group of patients with senile dementia of Alzheimer type (SDAT, n = 20). Abnormal EEG findings were correlated with poor scores on Hasegawa test in the VD group and with those on Bender Gestalt test in the SDAT group. The degree of dilated ventricle was more correlated with abnormal EEG findings in the SDAT group than the VD group. (Namekawa, K.)

  17. Relationships between automated EEG and cranial CT in patients with senile dementia

    Energy Technology Data Exchange (ETDEWEB)

    Mori, Katsumi; Miyasaka, Mutue; Nakano, Takashi; Ohtaka, Tadashi

    1988-08-01

    The degree of dementia was examined in relation to findings of automated EEG and cranial CT in 50 demented old patients aged 51 - 86. Slow waves on EEC, and dilated ventricle and atrophied cortex on cranial CT were frequently associated with poor psychological scores. As the degree of dilated ventricle increased, the frequency and amplitude of slow waves increased and the frequency and continuity of ..cap alpha.. waves decreased. Dilated ventricle was more strongly correlated with abnormal EEG findings than atrophied cortex. This was more marked in cases of severer patients. Abnormal EEC findings associated with poor psychological scores were more frequently observed in the group of patients with vascular dementia (VD, n = 30) than the group of patients with senile dementia of Alzheimer type (SDAT, n = 20). Abnormal EEG findings were correlated with poor scores on Hasegawa test in the VD group and with those on Bender Gestalt test in the SDAT group. The degree of dilated ventricle was more correlated with abnormal EEG findings in the SDAT group than the VD group. (Namekawa, K.).

  18. 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

  19. 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.

  20. 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.

  1. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS) Severity.

    Science.gov (United States)

    Bosch-Bayard, Jorge; Galán-García, Lídice; Fernandez, Thalia; Lirio, Rolando B; Bringas-Vega, Maria L; Roca-Stappung, Milene; Ricardo-Garcell, Josefina; Harmony, Thalía; Valdes-Sosa, Pedro A

    2017-01-01

    In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to) different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS) disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia), Mathematics (Dyscalculia), or Writing (Dysgraphia). By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  2. Stable Sparse Classifiers Identify qEEG Signatures that Predict Learning Disabilities (NOS Severity

    Directory of Open Access Journals (Sweden)

    Jorge Bosch-Bayard

    2018-01-01

    Full Text Available In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the area under the ROC curve. This method allows assessing the performance of the classification by the ROC technique, when more than two groups are involved in the classification problem, i.e., when the gold standard is not binary. We apply this methodology to the EEG spectral signatures to find biomarkers that allow discriminating between (and predicting pertinence to different subgroups of children diagnosed as Not Otherwise Specified Learning Disabilities (LD-NOS disorder. Children with LD-NOS have notable learning difficulties, which affect education but are not able to be put into some specific category as reading (Dyslexia, Mathematics (Dyscalculia, or Writing (Dysgraphia. By using the EEG spectra, we aim to identify EEG patterns that may be related to specific learning disabilities in an individual case. This could be useful to develop subject-based methods of therapy, based on information provided by the EEG. Here we study 85 LD-NOS children, divided in three subgroups previously selected by a clustering technique over the scores of cognitive tests. The classification equation produced stable marginal areas under the ROC of 0.71 for discrimination between Group 1 vs. Group 2; 0.91 for Group 1 vs. Group 3; and 0.75 for Group 2 vs. Group1. A discussion of the EEG characteristics of each group related to the cognitive scores is also presented.

  3. Effects of Marijuana on Ictal and Interictal EEG Activities in Idiopathic Generalized Epilepsy.

    Science.gov (United States)

    Sivakumar, Sanjeev; Zutshi, Deepti; Seraji-Bozorgzad, Navid; Shah, Aashit K

    2017-01-01

    Marijuana-based treatment for refractory epilepsy shows promise in surveys, case series, and clinical trials. However, literature on their EEG effects is sparse. Our objective is to analyze the effect of marijuana on EEG in a 24-year-old patient with idiopathic generalized epilepsy treated with cannabis. We blindly reviewed 3 long-term EEGs-a 24-hour study while only on antiepileptic drugs, a 72-hour EEG with Cannabis indica smoked on days 1 and 3 in addition to antiepileptic drugs, and a 48-hour EEG with combination C indica/sativa smoked on day 1 plus antiepileptic drugs. Generalized spike-wave discharges and diffuse paroxysmal fast activity were categorized as interictal and ictal, based on duration of less than 10 seconds or greater, respectively. Data from three studies concatenated into contiguous time series, with usage of marijuana modeled as time-dependent discrete variable while interictal and ictal events constituted dependent variables. Analysis of variance as initial test for significance followed by time series analysis using Generalized Autoregressive Conditional Heteroscedasticity model was performed. Statistical significance for lower interictal events (analysis of variance P = 0.001) was seen during C indica use, but not for C indica/sativa mixture (P = 0.629) or ictal events (P = 0.087). However, time series analysis revealed a significant inverse correlation between marijuana use, with interictal (P EEG data, we demonstrate a decrease in interictal and ictal electrographic events during marijuana use. Larger samples of patients and EEG, with standardized cannabinoid formulation and dosing, are needed to validate our findings.

  4. 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.

  5. Extreme delta brush: a unique EEG pattern in adults with anti-NMDA receptor encephalitis.

    Science.gov (United States)

    Schmitt, Sarah E; Pargeon, Kimberly; Frechette, Eric S; Hirsch, Lawrence J; Dalmau, Josep; Friedman, Daniel

    2012-09-11

    To determine continuous EEG (cEEG) patterns that may be unique to anti-NMDA receptor (NMDAR) encephalitis in a series of adult patients with this disorder. We evaluated the clinical and EEG data of 23 hospitalized adult patients with anti-NMDAR encephalitis who underwent cEEG monitoring between January 2005 and February 2011 at 2 large academic medical centers. Twenty-three patients with anti-NMDAR encephalitis underwent a median of 7 (range 1-123) days of cEEG monitoring. The median length of hospitalization was 44 (range 2-200) days. Personality or behavioral changes (100%), movement disorders (82.6%), and seizures (78.3%) were the most common symptoms. Seven of 23 patients (30.4%) had a unique electrographic pattern, which we named "extreme delta brush" because of its resemblance to waveforms seen in premature infants. The presence of extreme delta brush was associated with a more prolonged hospitalization (mean 128.3 ± 47.5 vs 43.2 ± 39.0 days, p = 0.008) and increased days of cEEG monitoring (mean 27.6 ± 42.3 vs 6.2 ± 5.6 days, p = 0.012). The modified Rankin Scale score showed a trend toward worse scores in patients with the extreme delta brush pattern (mean 4.0 ± 0.8 vs 3.1 ± 1.1, p = 0.089). Extreme delta brush is a novel EEG finding seen in many patients with anti-NMDAR encephalitis. The presence of this pattern is associated with a more prolonged illness. Although the specificity of this pattern is unclear, its presence should raise consideration of this syndrome.

  6. Removal of ocular artifacts from the REM sleep EEG

    NARCIS (Netherlands)

    Waterman, D.; Woestenburg, J.C.; Elton, M.; Hofman, W.; Kok, A.

    1992-01-01

    The present report concerns the first study in which electrooculographic (EOG) contamination of electroencephalographic (EEG) recordings in rapid eye movement (REM) sleep is systematically investigated. Contamination of REM sleep EEG recordings in six subjects was evaluated in the frequency domain.

  7. Extended seizure detection algorithm for intracranial EEG recordings

    DEFF Research Database (Denmark)

    Kjaer, T. W.; Remvig, L. S.; Henriksen, J.

    2010-01-01

    Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient...... had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal...... and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using...

  8. 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.

  9. Long-term EEG in patients with the ring chromosome 20 epilepsy syndrome.

    Science.gov (United States)

    Freire de Moura, Maria; Flores-Guevara, Roberto; Gueguen, Bernard; Biraben, Arnaud; Renault, Francis

    2016-05-01

    The recognizable electroencephalography (EEG) pattern of ring chromosome 20 epilepsy syndrome can be missing in patients with r(20) chromosomal anomaly, and may be found in patients with frontal lobe epilepsy of other origin. This study aims to search for more specific EEG signs by using long-term recordings and measuring the duration of paroxysmal anomalies. The series included 12 adult patients with r(20) anomaly, and 12 controls without any chromosomal aberration. We measured the duration of every paroxysmal burst and calculated the sum of their durations for each long-term EEG recording. We compared patients to controls using the Mann-Whitney U-test. Every patient showed long-lasting paroxysmal EEG bursts, up to 60 min; controls did not show any bursts longer than 60 s (p < 0.0001). The total duration of paroxysmal anomalies was significantly longer in patients (31-692 min) compared to controls (0-48 min) (p < 0.0001). Thus, long-term recordings enhance the contribution of EEG methods for characterizing the ring 20 chromosome epilepsy syndrome. Wiley Periodicals, Inc. © 2016 International League Against Epilepsy.

  10. [Changes in the EEG spectral power during perception of neutral and emotionally salient words in schizophrenic patients, their relatives and healthy individuals from the general population].

    Science.gov (United States)

    Alfimova, M V; Uvarova, L G

    2007-01-01

    To search for EEG-correlates of emotional processing that might be indicators of genetic predisposition to schizophrenia, changes in EEG spectral power during perception of neutral and emotionally salient words were examined in 36 schizophrenic patients, 50 of their unaffected first-degree relatives, and 47 healthy individuals without any family history of psychoses. In healthy persons, passive listening to neutral words induced minimum changes in cortical rhythmical activity, predominantly in the form of synchronization of slow and fast waves, whereas perception of emotional words was followed by a generalized depression of the alpha and beta1 activity and a locally specific decrease in the power of theta and beta2 frequency bands. The patients and their relatives showed a decrease in the alpha and beta1 activity simultaneously with an increase in the power of delta activity in response to both groups of words. Thus, in the patients and their relatives, reactions to neutral and emotional words were ulterior as a result of augmented reactions to the neutral words. These findings suggest that the EEG changes reflect familial and possibly hereditable abnormal involuntary attention. No prominent decrease in reactivity to emotional stimuli was revealed in schizophrenic families.

  11. The Mozart Effect: A quantitative EEG study.

    Science.gov (United States)

    Verrusio, Walter; Ettorre, Evaristo; Vicenzini, Edoardo; Vanacore, Nicola; Cacciafesta, Mauro; Mecarelli, Oriano

    2015-09-01

    The aim of this study is to investigate the influence of Mozart's music on brain activity through spectral analysis of the EEG in young healthy adults (Adults), in healthy elderly (Elderly) and in elderly with Mild Cognitive Impairment (MCI). EEG recording was performed at basal rest conditions and after listening to Mozart's K448 or "Fur Elise" Beethoven's sonatas. After listening to Mozart, an increase of alpha band and median frequency index of background alpha rhythm activity (a pattern of brain wave activity linked to memory, cognition and open mind to problem solving) was observed both in Adults and in Elderly. No changes were observed in MCI. After listening to Beethoven, no changes in EEG activity were detected. This results may be representative of the fact that said Mozart's music is able to "activate" neuronal cortical circuits related to attentive and cognitive functions. Copyright © 2015 Elsevier Inc. All rights reserved.

  12. 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.

  13. 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.

  14. A new method for quantifying the performance of EEG blind source separation algorithms by referencing a simultaneously recorded ECoG signal.

    Science.gov (United States)

    Oosugi, Naoya; Kitajo, Keiichi; Hasegawa, Naomi; Nagasaka, Yasuo; Okanoya, Kazuo; Fujii, Naotaka

    2017-09-01

    Blind source separation (BSS) algorithms extract neural signals from electroencephalography (EEG) data. However, it is difficult to quantify source separation performance because there is no criterion to dissociate neural signals and noise in EEG signals. This study develops a method for evaluating BSS performance. The idea is neural signals in EEG can be estimated by comparison with simultaneously measured electrocorticography (ECoG). Because the ECoG electrodes cover the majority of the lateral cortical surface and should capture most of the original neural sources in the EEG signals. We measured real EEG and ECoG data and developed an algorithm for evaluating BSS performance. First, EEG signals are separated into EEG components using the BSS algorithm. Second, the EEG components are ranked using the correlation coefficients of the ECoG regression and the components are grouped into subsets based on their ranks. Third, canonical correlation analysis estimates how much information is shared between the subsets of the EEG components and the ECoG signals. We used our algorithm to compare the performance of BSS algorithms (PCA, AMUSE, SOBI, JADE, fastICA) via the EEG and ECoG data of anesthetized nonhuman primates. The results (Best case >JADE = fastICA >AMUSE = SOBI ≥ PCA >random separation) were common to the two subjects. To encourage the further development of better BSS algorithms, our EEG and ECoG data are available on our Web site (http://neurotycho.org/) as a common testing platform. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  15. EEG. Renewables Act. Comment. 3. new rev. and enl. ed.; EEG. Erneuerbare-Energien-Gesetz. Kommentar

    Energy Technology Data Exchange (ETDEWEB)

    Frenz, Walter [Rheinisch-Westfaelische Technische Hochschule (RWTH), Aachen (Germany). Lehr- und Forschungsgebiet Berg-, Umwelt- und Europarecht; Mueggenborg, Hans-Juergen (eds.) [Kassel Univ. (Germany)

    2013-05-01

    Like hardly any other law, the Renewable Energy Sources Law (EEG) is a subject to continuing modifications. This makes the application of the already complicated regulations even for experts to a special challenge. With the proven Berliner comment EEG, now a reliable companion through the bureaucratic jungle is available. All regulations of the EEG are commented precisely and easily to understand by profound experts. An extensive selection of terminology enables a rapid orientation within this book. In addition to the excursions to renewable energy technologies, this book also describes the structural aspects in the establishment of a photovoltaic system.

  16. The colorful brain: Visualization of EEG background patterns

    NARCIS (Netherlands)

    van Putten, Michel Johannes Antonius Maria

    2008-01-01

    This article presents a method to transform routine clinical EEG recordings to an alternative visual domain. The method is intended to support the classic visual interpretation of the EEG background pattern and to facilitate communication about relevant EEG characteristics. In addition, it provides

  17. A comparison of EEG spectral entropy with conventional quantitative ...

    African Journals Online (AJOL)

    A comparison of EEG spectral entropy with conventional quantitative EEG at varying depths of sevoflurane anaesthesia. PR Bartel, FJ Smith, PJ Becker. Abstract. Background and Aim: Recently an electroencephalographic (EEG) spectral entropy module (M-ENTROPY) for an anaesthetic monitor has become commercially ...

  18. Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

    Directory of Open Access Journals (Sweden)

    François-B. Vialatte

    2011-01-01

    Full Text Available Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD. However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz, α1 (7.5–9.5 Hz, α2 (9.5–12.5 Hz, and β (12.5–25 Hz. The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

  19. Analysis of routine EEG usage in a general adult ICU.

    LENUS (Irish Health Repository)

    McHugh, J C

    2009-09-01

    Non-convulsive seizures and status epilepticus are common in brain-injured patients in intensive care units. Continuous electroencephalography (cEEG) monitoring is the most sensitive means of their detection. In centres where cEEG is unavailable, routine EEG is often utilized for diagnosis although its sensitivity is lower.

  20. Event-Related EEG Oscillations to Semantically Unrelated Words in Normal and Learning Disabled Children

    Science.gov (United States)

    Fernandez, Thalia; Harmony, Thalia; Mendoza, Omar; Lopez-Alanis, Paula; Marroquin, Jose Luis; Otero, Gloria; Ricardo-Garcell, Josefina

    2012-01-01

    Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that…

  1. Relation of EEG alpha background to cognitive fuction, brain atrophy, and cerebral metabolism in Down's syndrome. Age-specific changes

    International Nuclear Information System (INIS)

    Devinsky, O.; Sato, S.; Conwit, R.A.; Schapiro, M.B.

    1990-01-01

    We studied 19 young adults (19 to 37 years old) and 9 older patients (42 to 66 years old) with Down's syndrome (DS) and a control group of 13 healthy adults (22 to 38 years old) to investigate the relation of electroencephalographic (EEG) alpha background to cognitive function and cerebral metabolism. Four of the older patients with DS had a history of mental deterioration, disorientation, and memory loss and were demented. Patients and control subjects had EEGs, psychometric testing, quantitative computed tomography, and positron emission tomography with fludeoxyglucose F 18. A blinded reader classified the EEGs into two groups--those with normal alpha background or those with abnormal background. All the control subjects, the 13 young adult patients with DS, and the 5 older patients with DS had normal EEG backgrounds. In comparison with the age-matched patients with DS with normal alpha background, older patients with DS with decreased alpha background had dementia, fewer visuospatial skills, decreased attention span, larger third ventricles, and a global decrease in cerebral glucose utilization with parietal hypometabolism. In the young patients with DS, the EEG background did not correlate with psychometric or positron emission tomographic findings, but the third ventricles were significantly larger in those with abnormal EEG background. The young patients with DS, with or without normal EEG background, had positron emission tomographic findings similar to those of the control subjects. The mechanism underlying the abnormal EEG background may be the neuropathologic changes of Alzheimer's disease in older patients with DS and may be cerebral immaturity in younger patients with DS

  2. moviEEG: An animation toolbox for visualization of intracranial electroencephalography synchronization dynamics.

    Science.gov (United States)

    Wong, Simeon M; Ibrahim, George M; Ochi, Ayako; Otsubo, Hiroshi; Rutka, James T; Snead, O Carter; Doesburg, Sam M

    2016-06-01

    We introduce and describe the functions of moviEEG (Multiple Overlay Visualizations for Intracranial ElectroEncephaloGraphy), a novel MATLAB-based toolbox for spatiotemporal mapping of network synchronization dynamics in intracranial electroencephalography (iEEG) data. The toolbox integrates visualizations of inter-electrode phase-locking relationships in peri-ictal epileptogenic networks with signal spectral properties and graph-theoretical network measures overlaid upon operating room images of the electrode grid. Functional connectivity between every electrode pair is evaluated over a sliding window indexed by phase synchrony. Two case studies are presented to provide preliminary evidence for the application of the toolbox to guide network-based mapping of epileptogenic cortex and to distinguish these regions from eloquent brain networks. In both cases, epileptogenic cortex was visually distinct. We introduce moviEEG, a novel toolbox for animation of oscillatory network dynamics in iEEG data, and provide two case studies showing preliminary evidence for utility of the toolbox in delineating the epileptogenic zone. Despite evidence that atypical network synchronization has shown to be altered in epileptogenic brain regions, network based techniques have yet to be incorporated into clinical pre-surgical mapping. moviEEG provides a set of functions to enable easy visualization with network based techniques. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  3. Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation.

    Science.gov (United States)

    Dmochowski, Jacek P; Koessler, Laurent; Norcia, Anthony M; Bikson, Marom; Parra, Lucas C

    2017-08-15

    To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4-7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  4. EEG, MRI, and SPECT in epilepsy. Relative contributions to preoperative evaluation

    International Nuclear Information System (INIS)

    Seki, Gaku; Hoshida, Tohru; Goda, Kazuo; Hashimoto, Hiroshi; Nakase, Hiroyuki; Hirabayashi, Hidehiro; Kawaguchi, Shoichiro; Morimoto, Tetsuya; Sakaki, Toshisuke

    1998-01-01

    We comparatively assessed detection of epileptogenic areas on preoperative evaluation in 33 patients with intractable partial epilepsy using scalp interictal and ictal electroencephalography (EEG), magnetic resonance imaging (MRI), and interictal single photon emission computed tomography (SPECT). There are 22 temporal and 11 extratemporal lobe epilepsies. All or almost of their seizures have stopped after resective surgery for more than 12 months follow-up period, averaged 43 months. MRI studies demonstrated 21 organic lesions, 11 mesial temporal sclerosis and one patient showed normal brain tissue. Scalp EEG could correctly identify the focus in 14 of 33 cases (42%), interictal SPECT in 18 of 26 (69%), MRI in 29 of 33 (88%), interictal scalp EEG-video monitoring in 17 of 24 (71%), and ictal scalp EEG-video monitoring in 15 of 22 (68%). Although neuroimaging studies, especially MRI, are useful to detect not only localization of epileptic lesions but also epileptogenic focus, for example, mesial temporal sclerosis, the exact localization of epileptogenic areas could be done by comprehensive evaluation including ictal scalp EEG-video monitoring. (author)

  5. Early post-traumatic epilepsy following serious craniocerebral trauma, with emphasis on EEG and CT findings

    International Nuclear Information System (INIS)

    Goeller, T.

    1981-01-01

    In the study on hand, eighty patients with craniocerebral trauma were regularly examined by EEG. Twenty-two of the patients had clinically observable fits. In five other patients, only the EEG revealed paroxysmal events in the first week following trauma. In six patients who were observed to have fits, no corresponding EEG equivalent could be recorded for technical reasons. In case of early epileptical fits, CT always revealed an intercranial hemorrhage, except for two cases, the findings being in agreement with the lateral location of the focus as determined by EEG. The seriousness of cranial injury was directly related to the number of fits observed in the first post-traumatic week. Patients over thirty showed an increase of eraly post-traumatic fits. The lethality of patients with early fits was significantly increased as compared with patients without early fits. EEG examination as early as possible after the craniocerebral trauma is necessary and useful, both for detection of lesions and for appropriate drug therapy. (orig./MG) [de

  6. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

    Directory of Open Access Journals (Sweden)

    Ridha Djemal

    2017-01-01

    Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

  7. 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.

  8. EEG, MRI, and SPECT in epilepsy. Relative contributions to preoperative evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Seki, Gaku [Luoyang Medical Coll. Associated Hospital (China); Hoshida, Tohru; Goda, Kazuo; Hashimoto, Hiroshi; Nakase, Hiroyuki; Hirabayashi, Hidehiro; Kawaguchi, Shoichiro; Morimoto, Tetsuya; Sakaki, Toshisuke

    1998-07-01

    We comparatively assessed detection of epileptogenic areas on preoperative evaluation in 33 patients with intractable partial epilepsy using scalp interictal and ictal electroencephalography (EEG), magnetic resonance imaging (MRI), and interictal single photon emission computed tomography (SPECT). There are 22 temporal and 11 extratemporal lobe epilepsies. All or almost of their seizures have stopped after resective surgery for more than 12 months follow-up period, averaged 43 months. MRI studies demonstrated 21 organic lesions, 11 mesial temporal sclerosis and one patient showed normal brain tissue. Scalp EEG could correctly identify the focus in 14 of 33 cases (42%), interictal SPECT in 18 of 26 (69%), MRI in 29 of 33 (88%), interictal scalp EEG-video monitoring in 17 of 24 (71%), and ictal scalp EEG-video monitoring in 15 of 22 (68%). Although neuroimaging studies, especially MRI, are useful to detect not only localization of epileptic lesions but also epileptogenic focus, for example, mesial temporal sclerosis, the exact localization of epileptogenic areas could be done by comprehensive evaluation including ictal scalp EEG-video monitoring. (author)

  9. [Intracerebral EEG functioning as a reflexion of the systemic brain organization in norm and pathology].

    Science.gov (United States)

    Boldyreva, G N; Zhavoronkova, L A; Sharova, E V; Dobronravova, I S

    2003-01-01

    The authors summarized the EEG findings and defined the nature of intercentral EEG relationships in different functional states of healthy subjects and patients with organic cerebral pathology based on coherence analysis. The EEG features typical of healthy subjects were identified: an anterior-posterior gradient of the mean coherence and the character of cortical-subcortical relationships in the anterior cerebral structures. Right- and lefthanded subjects showed the frequency and regional differences in EEG coherence, which reflected, mainly, specific intracortical relationships. Development and regression of pathologic signs in right- and lefthanded patients with organic brain lesions are thought to be determined by these differences. As distinct from cortical pathology, lesions of regulatory structures (diencephalic, brainstem, and limbic) were shown to produce more diffuse changes in intercentral relationships with a tendency to reciprocity. Intercentral relations, including their interhemispheric differences, varied with changes in the functional state of healthy subjects (increase and decrease in the level of functioning). A certain time course of changes in intercentral relationships was also revealed in patients with organic brain lesions during recovery of their consciousness and mental activity. Changes in the dominance of activity of individual regulatory structures are considered to be one of the most important factors that determine the dynamic character of EEG coherence.

  10. Exploring resting-state EEG brain oscillatory activity in relation to cognitive functioning in multiple sclerosis.

    Science.gov (United States)

    Keune, Philipp M; Hansen, Sascha; Weber, Emily; Zapf, Franziska; Habich, Juliane; Muenssinger, Jana; Wolf, Sebastian; Schönenberg, Michael; Oschmann, Patrick

    2017-09-01

    Neurophysiologic monitoring parameters related to cognition in Multiple Sclerosis (MS) are sparse. Previous work reported an association between magnetoencephalographic (MEG) alpha-1 activity and information processing speed. While this remains to be replicated by more available electroencephalographic (EEG) methods, also other established EEG markers, e.g. the slow-wave/fast-wave ratio (theta/beta ratio), remain to be explored in this context. Performance on standard tests addressing information processing speed and attention (Symbol-Digit Modalities Test, SDMT; Test of Attention Performance, TAP) was examined in relation to resting-state EEG alpha-1 and alpha-2 activity and the theta/beta ratio in 25MS patients. Increased global alpha-1 and alpha-2 activity and an increased frontal theta/beta ratio (pronounced slow-wave relative to fast-wave activity) were associated with lower SDMT processing speed. In an exploratory analysis, clinically impaired attention was associated with a significantly increased frontal theta/beta ratio whereas alpha power did not show sensitivity to clinical impairment. EEG global alpha power and the frontal theta/beta ratio were both associated with attention. The theta/beta ratio involved potential clinical sensitivity. Resting-state EEG recordings can be obtained during the routine clinical process. The examined resting-state measures may represent feasible monitoring parameters in MS. This notion should be explored in future intervention studies. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  11. Neurofeedback Effects on Evoked and Induced EEG Gamma Band Reactivity to Drug-related Cues in Cocaine Addiction

    Science.gov (United States)

    Horrell, Timothy; El-Baz, Ayman; Baruth, Joshua; Tasman, Allan; Sokhadze, Guela; Stewart, Christopher; Sokhadze, Estate

    2010-01-01

    Introduction Preoccupation with drug and drug-related items is a typical characteristic of cocaine addicted individuals. It has been shown in multiple accounts that prolonged drug use has a profound effect on the EEG recordings of drug addicts when compared to controls during cue reactivity tests. Cue reactivity refers to a phenomenon in which individuals with a history of drug abuse exhibit excessive psychophysiological responses to cues associated with their drug of choice. One of the aims of this pilot study was to determine the presence of an attentional bias to preferentially process drug-related cues using evoked and induced gamma reactivity measures in cocaine addicts before and after biobehavioral treatment based on neurofeedback. Another aim was to show that central SMR amplitude increase and frontal theta control is possible in an experimental outpatient drug users group over 12 neurofeedback sessions. Method Ten current cocaine abusers participated in this pilot research study using neurofeedback combined with Motivational Interviewing sessions. Eight of them completed all planned pre- and post –neurofeedback cue reactivity tests with event-related EEG recording and clinical evaluations. Cue reactivity test represented a visual oddball task with images from the International Affective Picture System and drug-related pictures. Evoked and induced gamma responses to target and non-target drug cues were analyzed using wavelet analysis. Results Outpatient subjects with cocaine addiction completed the biobehavioral intervention and successfully increased SMR while keeping theta practically unchanged in 12 sessions of neurofeedback training. The addition of Motivational Interviewing helped retain patients in the study. Clinical evaluations immediately after completion of the treatment showed decreased self-reports on depression and stress scores, and urine tests collaborated reports of decreased use of cocaine and marijuana. Effects of neurofeedback resulted

  12. Obesity in show cats.

    Science.gov (United States)

    Corbee, R J

    2014-12-01

    Obesity is an important disease with a high prevalence in cats. Because obesity is related to several other diseases, it is important to identify the population at risk. Several risk factors for obesity have been described in the literature. A higher incidence of obesity in certain cat breeds has been suggested. The aim of this study was to determine whether obesity occurs more often in certain breeds. The second aim was to relate the increased prevalence of obesity in certain breeds to the official standards of that breed. To this end, 268 cats of 22 different breeds investigated by determining their body condition score (BCS) on a nine-point scale by inspection and palpation, at two different cat shows. Overall, 45.5% of the show cats had a BCS > 5, and 4.5% of the show cats had a BCS > 7. There were significant differences between breeds, which could be related to the breed standards. Most overweight and obese cats were in the neutered group. It warrants firm discussions with breeders and cat show judges to come to different interpretations of the standards in order to prevent overweight conditions in certain breeds from being the standard of beauty. Neutering predisposes for obesity and requires early nutritional intervention to prevent obese conditions. Journal of Animal Physiology and Animal Nutrition © 2014 Blackwell Verlag GmbH.

  13. 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.

  14. Effects of action observation therapy on hand dexterity and EEG-based cortical activation patterns in patients with post-stroke hemiparesis.

    Science.gov (United States)

    Kuk, Eun-Ju; Kim, Jong-Man; Oh, Duck-Won; Hwang, Han-Jeong

    2016-10-01

    Previous reports have suggested that action observation training (AOT) is beneficial in enhancing the early learning of new motor tasks; however, EEG-based investigation has received little attention for AOT. The purpose of this study was to illustrate the effects of AOT on hand dexterity and cortical activation in patients with post-stroke hemiparesis. Twenty patients with post-stroke hemiparesis were randomly divided into either the experimental group (EG) or control group (CG), with 10 patients in each group. Prior to the execution of motor tasks (carrying wooden blocks from one box to another), subjects in the EG and CG observed a video clip displaying the execution of the same motor task and pictures showing landscapes, respectively. Outcome measures included the box and block test (BBT) to evaluate hand dexterity and EEG-based brain mapping to detect changes in cortical activation. The BBT scores (EG: 20.50 ± 6.62 at pre-test and 24.40 ± 5.42 at post-test; CG: 20.20 ± 6.12 at pre-test and 20.60 ± 7.17 at post-test) revealed significant main effects for the time and group and significant time-by-group interactions (p < 0.05). For the subjects in the EG, topographical representations obtained with the EEG-based brain mapping system were different in each session of the AOT and remarkable changes occurred from the 2nd session of AOT. Furthermore, the middle frontal gyrus was less active at post-test than at pre-test. These findings support that AOT may be beneficial in altering cortical activation patterns and hand dexterity.

  15. Non-linear Analysis of Scalp EEG by Using Bispectra: The Effect of the Reference Choice

    Directory of Open Access Journals (Sweden)

    Federico Chella

    2017-05-01

    Full Text Available Bispectral analysis is a signal processing technique that makes it possible to capture the non-linear and non-Gaussian properties of the EEG signals. It has found various applications in EEG research and clinical practice, including the assessment of anesthetic depth, the identification of epileptic seizures, and more recently, the evaluation of non-linear cross-frequency brain functional connectivity. However, the validity and reliability of the indices drawn from bispectral analysis of EEG signals are potentially biased by the use of a non-neutral EEG reference. The present study aims at investigating the effects of the reference choice on the analysis of the non-linear features of EEG signals through bicoherence, as well as on the estimation of cross-frequency EEG connectivity through two different non-linear measures, i.e., the cross-bicoherence and the antisymmetric cross-bicoherence. To this end, four commonly used reference schemes were considered: the vertex electrode (Cz, the digitally linked mastoids, the average reference, and the Reference Electrode Standardization Technique (REST. The reference effects were assessed both in simulations and in a real EEG experiment. The simulations allowed to investigated: (i the effects of the electrode density on the performance of the above references in the estimation of bispectral measures; and (ii the effects of the head model accuracy in the performance of the REST. For real data, the EEG signals recorded from 10 subjects during eyes open resting state were examined, and the distortions induced by the reference choice in the patterns of alpha-beta bicoherence, cross-bicoherence, and antisymmetric cross-bicoherence were assessed. The results showed significant differences in the findings depending on the chosen reference, with the REST providing superior performance than all the other references in approximating the ideal neutral reference. In conclusion, this study highlights the importance of

  16. Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

    Directory of Open Access Journals (Sweden)

    Ming-ai Li

    2017-01-01

    Full Text Available Electroencephalography (EEG is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG not only has a close correlation with the human imagination and movement intention but also contains a large amount of physiological or disease information. As a result, it has been fully studied in the field of rehabilitation. To correctly interpret and accurately extract the features of MI-EEG signals, many nonlinear dynamic methods based on entropy, such as Approximate Entropy (ApEn, Sample Entropy (SampEn, Fuzzy Entropy (FE, and Permutation Entropy (PE, have been proposed and exploited continuously in recent years. However, these entropy-based methods can only measure the complexity of MI-EEG based on a single scale and therefore fail to account for the multiscale property inherent in MI-EEG. To solve this problem, Multiscale Sample Entropy (MSE, Multiscale Permutation Entropy (MPE, and Multiscale Fuzzy Entropy (MFE are developed by introducing scale factor. However, MFE has not been widely used in analysis of MI-EEG, and the same parameter values are employed when the MFE method is used to calculate the fuzzy entropy values on multiple scales. Actually, each coarse-grained MI-EEG carries the characteristic information of the original signal on different scale factors. It is necessary to optimize MFE parameters to discover more feature information. In this paper, the parameters of MFE are optimized independently for each scale factor, and the improved MFE (IMFE is applied to the feature extraction of MI-EEG. Based on the event-related desynchronization (ERD/event-related synchronization (ERS phenomenon, IMFE features from multi channels are fused organically to construct the feature vector. Experiments are conducted on a public dataset by using Support Vector Machine (SVM as a classifier. The experiment results of 10-fold cross-validation show that the proposed method yields

  17. EEG phase reset due to auditory attention: an inverse time-scale approach

    International Nuclear Information System (INIS)

    Low, Yin Fen; Strauss, Daniel J

    2009-01-01

    We propose a novel tool to evaluate the electroencephalograph (EEG) phase reset due to auditory attention by utilizing an inverse analysis of the instantaneous phase for the first time. EEGs were acquired through auditory attention experiments with a maximum entropy stimulation paradigm. We examined single sweeps of auditory late response (ALR) with the complex continuous wavelet transform. The phase in the frequency band that is associated with auditory attention (6–10 Hz, termed as theta–alpha border) was reset to the mean phase of the averaged EEGs. The inverse transform was applied to reconstruct the phase-modified signal. We found significant enhancement of the N100 wave in the reconstructed signal. Analysis of the phase noise shows the effects of phase jittering on the generation of the N100 wave implying that a preferred phase is necessary to generate the event-related potential (ERP). Power spectrum analysis shows a remarkable increase of evoked power but little change of total power after stabilizing the phase of EEGs. Furthermore, by resetting the phase only at the theta border of no attention data to the mean phase of attention data yields a result that resembles attention data. These results show strong connections between EEGs and ERP, in particular, we suggest that the presentation of an auditory stimulus triggers the phase reset process at the theta–alpha border which leads to the emergence of the N100 wave. It is concluded that our study reinforces other studies on the importance of the EEG in ERP genesis

  18. 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.

  19. EEG phase reset due to auditory attention: an inverse time-scale approach.

    Science.gov (United States)

    Low, Yin Fen; Strauss, Daniel J

    2009-08-01

    We propose a novel tool to evaluate the electroencephalograph (EEG) phase reset due to auditory attention by utilizing an inverse analysis of the instantaneous phase for the first time. EEGs were acquired through auditory attention experiments with a maximum entropy stimulation paradigm. We examined single sweeps of auditory late response (ALR) with the complex continuous wavelet transform. The phase in the frequency band that is associated with auditory attention (6-10 Hz, termed as theta-alpha border) was reset to the mean phase of the averaged EEGs. The inverse transform was applied to reconstruct the phase-modified signal. We found significant enhancement of the N100 wave in the reconstructed signal. Analysis of the phase noise shows the effects of phase jittering on the generation of the N100 wave implying that a preferred phase is necessary to generate the event-related potential (ERP). Power spectrum analysis shows a remarkable increase of evoked power but little change of total power after stabilizing the phase of EEGs. Furthermore, by resetting the phase only at the theta border of no attention data to the mean phase of attention data yields a result that resembles attention data. These results show strong connections between EEGs and ERP, in particular, we suggest that the presentation of an auditory stimulus triggers the phase reset process at the theta-alpha border which leads to the emergence of the N100 wave. It is concluded that our study reinforces other studies on the importance of the EEG in ERP genesis.

  20. Study on patterns and prevalence of EEG abnormalities in children presenting with behavioural disturbances in psychiatry OPD, Gauhati Medical College and Hospital

    Directory of Open Access Journals (Sweden)

    Bobby Hmar

    2016-01-01

    Full Text Available Background of the study: Children with behavioural abnormalities and developmental disorders are often advised electroencephalography (EEG for evaluation of electrophysiological process of the brain to rule out any organic pathology. Various studies have reported abnormal EEG in these groups of children without history of clinical seizure on routine EEG and sleep EEG. Aim of the study: To study pattern and prevalence of EEG abnormalities in children with behavioural abnormalities without history of clinical seizure. Materials and methods: The study is a retrospective study. Ethical clearance has been obtained from institutional ethical committee for the study. To collect data, socio-demographic and clinical data proforma has been used. Data has been evaluated during the period from June 2011 to June 2014 as per selection criteria from the case history record of children with behavioural abnormalities attending child guidance clinic (CGC. Associations of abnormal EEG with various psychiatric diagnoses has been analysed and chi-square test has been used. p value <0.05 has been taken as test of significance. Result: Total 2011 children attended CGC from 2011 June to 2014 June. One hundred and ninety two children of various psychiatric diagnoses as per the tenth revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10 criteria had fulfilled the selection criteria and 113 children had done EEG. Abnormal EEG was found in 26.54% of children with various psychiatric diagnoses. Association was statistically significant (p<0.05. The EEG abnormalities were found more in male gender than female (p<0.05 and more in younger age group (four to ten years, p<0.05. Conclusion: Children with various psychiatric diagnoses have significant association with abnormal EEG without history of clinical seizure.

  1. Modification of EEG power spectra and EEG connectivity in autobiographical memory: a sLORETA study.

    Science.gov (United States)

    Imperatori, Claudio; Brunetti, Riccardo; Farina, Benedetto; Speranza, Anna Maria; Losurdo, Anna; Testani, Elisa; Contardi, Anna; Della Marca, Giacomo

    2014-08-01

    The aim of the present study was to explore the modifications of scalp EEG power spectra and EEG connectivity during the autobiographical memory test (AM-T) and during the retrieval of an autobiographical event (the high school final examination, Task 2). Seventeen healthy volunteers were enrolled (9 women and 8 men, mean age 23.4 ± 2.8 years, range 19-30). EEG was recorded at baseline and while performing the autobiographical memory (AM) tasks, by means of 19 surface electrodes and a nasopharyngeal electrode. EEG analysis was conducted by means of the standardized LOw Resolution Electric Tomography (sLORETA) software. Power spectra and lagged EEG coherence were compared between EEG acquired during the memory tasks and baseline recording. The frequency bands considered were as follows: delta (0.5-4 Hz); theta (4.5-7.5 Hz); alpha (8-12.5 Hz); beta1 (13-17.5 Hz); beta2 (18-30 Hz); gamma (30.5-60 Hz). During AM-T, we observed a significant delta power increase in left frontal and midline cortices (T = 3.554; p < 0.05) and increased EEG connectivity in delta band in prefrontal, temporal, parietal, and occipital areas, and for gamma bands in the left temporo-parietal regions (T = 4.154; p < 0.05). In Task 2, we measured an increased power in the gamma band located in the left posterior midline areas (T = 3.960; p < 0.05) and a significant increase in delta band connectivity in the prefrontal, temporal, parietal, and occipital areas, and in the gamma band involving right temporo-parietal areas (T = 4.579; p < 0.05). These results indicate that AM retrieval engages in a complex network which is mediated by both low- (delta) and high-frequency (gamma) EEG bands.

  2. EEG. Renewables Act. Comment. 4. new rev. and enl. ed.; EEG. Erneuerbare-Energien-Gesetz. Kommentar

    Energy Technology Data Exchange (ETDEWEB)

    Frenz, Walter [RWTH Aachen Univ. (Germany). Lehr- und Forschungsgebiet Berg-, Umwelt- und Europarecht; Mueggenborg, Hans-Juergen [Technische Hochschule Aachen (Germany); Kassel Univ. (Germany); Cosack, Tilman [Hochschule Trier, Umwelt-Campus Birkenfeld (Germany). IREK - Inst. fuer das Recht der Erneuerbaren Energien, Energieeffizienzrecht und Klimaschutzrecht; Ekardt, Felix (ed.) [Forschungsstelle Nachhaltigkeit und Klimapolitik, Leipzig (Germany)

    2015-07-01

    Unlike any other Act, the Renewable Energy Sources Act (EEG) changes continuously. Recently it has been fundamentally transformed with the amendment 2014. Comprehensive, readable and practice-oriented. The proven Berliner comment EEG is your reliable companion through the new regulatory regime. All provisions of the EEG 2014 thorough and easy to understand commented by experts of the matter. 2. The EEG Amending Act of 29.6.2015 has already been considered. A detailed introduction and contributions to the relevant European law and the antitrust aspects of the renewable energy sources to guarantee you a broad understanding of the rules. Valuable background information you provide, the digressions of the most important renewable energy technologies, will explain the pictures thanks to numerous the scientific and technical foundations. Moreover you the construction law aspects in the construction of photovoltaic and wind turbines are explained clearly. [German] Wie kaum ein anderes Gesetz veraendert sich das Erneuerbare-Energien-Gesetz (EEG) laufend. Zuletzt wurde es mit der Novelle 2014 grundlegend umgestaltet. Umfassend, verstaendlich und praxisgerecht Der bewaehrte Berliner Kommentar EEG ist Ihr verlaesslicher Begleiter durch das neue Regelungsregime. Alle Vorschriften des EEG 2014 werden gruendlich und leicht verstaendlich von Kennern der Materie kommentiert. Das 2. EEG-Aenderungsgesetz vom 29.06.2015 ist bereits beruecksichtigt. Eine ausfuehrliche Einleitung sowie Beitraege zum einschlaegigen europaeischen Recht und zu den kartellrechtlichen Aspekten der erneuerbaren Energien verhelfen Ihnen zu einem breiten Verstaendnis der Vorschriften. Wertvolles Hintergrundwissen liefern Ihnen auch die Exkurse zu den wichtigsten Erneuerbare-Energien-Technologien, die Ihnen dank zahlreicher Abbildungen die naturwissenschaftlich-technischen Grundlagen erlaeutern. Zudem werden Ihnen die baurechtlichen Aspekte bei der Errichtung von Photovoltaik- und Windenergieanlagen

  3. Reduced Temporal-Central EEG Alpha Coherence during Joint Attention Perception in Adolescents with Autism Spectrum Disorder

    Science.gov (United States)

    Jaime, Mark; McMahon, Camilla M.; Davidson, Bridget C.; Newell, Lisa C.; Mundy, Peter C.; Henderson, Heather A.

    2016-01-01

    Although prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal–central alpha coherence compared to typically developing adolescents. Greater right temporal–central alpha coherence during joint attention was positively associated with social cognitive performance in typical development but not in ASD. These results suggest that, in addition to a resting state, EEG coherence during joint attention perception is reduced in ASD. PMID:26659813

  4. Reduction in time-to-sleep through EEG based brain state detection and audio stimulation.

    Science.gov (United States)

    Zhuo Zhang; Cuntai Guan; Ti Eu Chan; Juanhong Yu; Aung Aung Phyo Wai; Chuanchu Wang; Haihong Zhang

    2015-08-01

    We developed an EEG- and audio-based sleep sensing and enhancing system, called iSleep (interactive Sleep enhancement apparatus). The system adopts a closed-loop approach which optimizes the audio recording selection based on user's sleep status detected through our online EEG computing algorithm. The iSleep prototype comprises two major parts: 1) a sleeping mask integrated with a single channel EEG electrode and amplifier, a pair of stereo earphones and a microcontroller with wireless circuit for control and data streaming; 2) a mobile app to receive EEG signals for online sleep monitoring and audio playback control. In this study we attempt to validate our hypothesis that appropriate audio stimulation in relation to brain state can induce faster onset of sleep and improve the quality of a nap. We conduct experiments on 28 healthy subjects, each undergoing two nap sessions - one with a quiet background and one with our audio-stimulation. We compare the time-to-sleep in both sessions between two groups of subjects, e.g., fast and slow sleep onset groups. The p-value obtained from Wilcoxon Signed Rank Test is 1.22e-04 for slow onset group, which demonstrates that iSleep can significantly reduce the time-to-sleep for people with difficulty in falling sleep.

  5. Behavioral approach system sensitivity and risk taking interact to predict left-frontal EEG asymmetry.

    Science.gov (United States)

    Black, Chelsea L; Goldstein, Kim E; LaBelle, Denise R; Brown, Christopher W; Harmon-Jones, Eddie; Abramson, Lyn Y; Alloy, Lauren B

    2014-09-01

    The Behavioral Approach System (BAS) hypersensitivity theory of bipolar disorder (BD; Alloy & Abramson, 2010; Depue & Iacono, 1989) suggests that hyperreactivity in the BAS results in the extreme fluctuations of mood characteristic of BD. In addition to risk conferred by BAS hypersensitivity, cognitive and personality variables may play a role in determining risk. We evaluated relationships among BAS sensitivity, risk taking, and an electrophysiological correlate of approach motivation, relative left-frontal electroencephalography (EEG) asymmetry. BAS sensitivity moderated the relationship between risk taking and EEG asymmetry. More specifically, individuals who were high in BAS sensitivity showed left-frontal EEG asymmetry regardless of their level of risk-taking behavior. However, among individuals who were moderate in BAS sensitivity, risk taking was positively associated with asymmetry. These findings suggest that cognitive and personality correlates of bipolar risk may evidence unique contributions to a neural measure of trait-approach motivation. Clinical implications of these findings are discussed. Copyright © 2014. Published by Elsevier Ltd.

  6. 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

  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. 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

  9. 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.

  10. 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.

  11. An overview of an amplitude integrated EEG

    Directory of Open Access Journals (Sweden)

    Setyo Handryastuti

    2007-05-01

    for neurodevelopmental problem in conditions such as hypoxic-ischemic encephalopathy (HIE, prematurity, neonatal seizures, central nervous system infection, metabolic disorders, intraventricular or intracranial bleeding and brain malformation. This article gives an overview about aEEG and its role in newborn.

  12. 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,

  13. Simultaneous recording of EEG and electromyographic polygraphy increases the diagnostic yield of video-EEG monitoring.

    Science.gov (United States)

    Hill, Aron T; Briggs, Belinda A; Seneviratne, Udaya

    2014-06-01

    To investigate the usefulness of adjunctive electromyographic (EMG) polygraphy in the diagnosis of clinical events captured during long-term video-EEG monitoring. A total of 40 patients (21 women, 19 men) aged between 19 and 72 years (mean 43) investigated using video-EEG monitoring were studied. Electromyographic activity was simultaneously recorded with EEG in four patients selected on clinical grounds. In these patients, surface EMG electrodes were placed over muscles suspected to be activated during a typical clinical event. Of the 40 patients investigated, 24 (60%) were given a diagnosis, whereas 16 (40%) remained undiagnosed. All four patients receiving adjunctive EMG polygraphy obtained a diagnosis, with three of these diagnoses being exclusively reliant on the EMG recordings. Specifically, one patient was diagnosed with propriospinal myoclonus, another patient was diagnosed with facio-mandibular myoclonus, and a third patient was found to have bruxism and periodic leg movements of sleep. The information obtained from surface EMG recordings aided the diagnosis of clinical events captured during video-EEG monitoring in 7.5% of the total cohort. This study suggests that EEG-EMG polygraphy may be used as a technique of improving the diagnostic yield of video-EEG monitoring in selected cases.

  14. Serial EEG findings in anti-NMDA receptor encephalitis: correlation between clinical course and EEG.

    Science.gov (United States)

    Ueda, Jun; Kawamoto, Michi; Hikiami, Ryota; Ishii, Junko; Yoshimura, Hajime; Matsumoto, Riki; Kohara, Nobuo

    2017-12-01

    Anti-NMDA receptor encephalitis is a paraneoplastic encephalitis characterised by psychiatric features, involuntary movement, and autonomic instability. Various EEG findings in patients with anti-NMDA receptor encephalitis have been reported, however, the correlation between the EEG findings and clinical course of anti-NMDA receptor encephalitis remains unclear. We describe a patient with anti-NMDA receptor encephalitis with a focus on EEG findings, which included: status epilepticus, generalised rhythmic delta activity, excess beta activity, extreme delta brush, and paroxysmal alpha activity upon arousal from sleep, which we term"arousal alpha pattern". Initially, status epilepticus was observed on the EEG when the patient was comatose with conjugate deviation. The EEG then indicated excess beta activity, followed by the emergence of continuous slow activity, including generalised rhythmic delta activity and extreme delta brush, in the most severe phase. Slow activity gradually faded in parallel with clinical amelioration. Excess beta activity persisted, even after the patient became almost independent in daily activities, and finally disappeared with full recovery. In summary, our patient with anti-NMDA receptor encephalitis demonstrated slow activity on the EEG, including extreme delta brush during the most severe phase, which gradually faded in parallel with clinical amelioration, with excess beta activity persisting into the recovery phase.

  15. EEG predictors of covert vigilant attention

    Science.gov (United States)

    Martel, Adrien; Dähne, Sven; Blankertz, Benjamin

    2014-06-01

    Objective. The present study addressed the question whether neurophysiological signals exhibit characteristic modulations preceding a miss in a covert vigilant attention task which mimics a natural environment in which critical stimuli may appear in the periphery of the visual field. Approach. Subjective, behavioural and encephalographic (EEG) data of 12 participants performing a modified Mackworth Clock task were obtained and analysed offline. The stimulus consisted of a pointer performing regular ticks in a clockwise sequence across 42 dots arranged in a circle. Participants were requested to covertly attend to the pointer and press a response button as quickly as possible in the event of a jump, a rare and random event. Main results. Significant increases in response latencies and decreases in the detection rates were found as a function of time-on-task, a characteristic effect of sustained attention tasks known as the vigilance decrement. Subjective sleepiness showed a significant increase over the duration of the experiment. Increased activity in the α-frequency range (8-14 Hz) was observed emerging and gradually accumulating 10 s before a missed target. Additionally, a significant gradual attenuation of the P3 event-related component was found to antecede misses by 5 s. Significance. The results corroborate recent findings that behavioural errors are presaged by specific neurophysiological activity and demonstrate that lapses of attention can be predicted in a covert setting up to 10 s in advance reinforcing the prospective use of brain-computer interface (BCI) technology for the detection of waning vigilance in real-world scenarios. Combining these findings with real-time single-trial analysis from BCI may pave the way for cognitive states monitoring systems able to determine the current, and predict the near-future development of the brain's attentional processes.

  16. EEG signal classification based on artificial neural networks and amplitude spectra features

    Science.gov (United States)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  17. Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

    Science.gov (United States)

    Ma, Yuliang; Ding, Xiaohui; She, Qingshan; Luo, Zhizeng; Potter, Thomas; Zhang, Yingchun

    2016-01-01

    Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. PMID:27313656

  18. Interrater variability of EEG interpretation in comatose cardiac arrest patients

    DEFF Research Database (Denmark)

    Westhall, Erik; Rosén, Ingmar; Rossetti, Andrea O

    2015-01-01

    OBJECTIVE: EEG is widely used to predict outcome in comatose cardiac arrest patients, but its value has been limited by lack of a uniform classification. We used the EEG terminology proposed by the American Clinical Neurophysiology Society (ACNS) to assess interrater variability in a cohort...... who were blinded for patient outcome. Percent agreement and kappa (κ) for the categories in the ACNS EEG terminology and for prespecified malignant EEG-patterns were calculated. RESULTS: There was substantial interrater agreement (κ 0.71) for highly malignant patterns and moderate agreement (κ 0.......42) for malignant patterns. Substantial agreement was found for malignant periodic or rhythmic patterns (κ 0.72) while agreement for identifying an unreactive EEG was fair (κ 0.26). CONCLUSIONS: The ACNS EEG terminology can be used to identify highly malignant EEG-patterns in post cardiac arrest patients...

  19. Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries

    Science.gov (United States)

    2016-12-01

    science of binaural beats . Retrieved from http://binauralbrains.com/the-science-of- binaural - beats / Biosignal. (2016). MicroEEG. Retrieved from http...Cap. Source: Binaural Brains (n.d.). ....................................4  Figure 3.  EEG Machine. Source: Refine Medical Technology (n.d...EEG. Figures 2, 3, and 4 display images of a standard EEG cap, EEG machine, and an EEG recording. Figure 2. Standard EEG Cap. Source: Binaural Brains

  20. Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction.

    Science.gov (United States)

    de Munck, Jan C; van Houdt, Petra J; Gonçalves, Sónia I; van Wegen, Erwin; Ossenblok, Pauly P W

    2013-01-01

    a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5 T scanner and 15 patients with epilepsy recorded in a 3 T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5T data) to 30% (3T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5T scanner show a small negative correlation between HBI and BCG amplitude. In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. EEG Neurofeedback treatments in children with ADHD: An updated meta-analysis of Randomized Controlled Trials

    Directory of Open Access Journals (Sweden)

    Jean-Arthur eMicoulaud Franchi

    2014-11-01

    Full Text Available Objective We undertook a meta-analysis of published Randomized Controlled Trials (RCT with semi-active control and sham-NF groups to determine whether EEG-NF significantly improves the overall symptoms, inattention and hyperactivity/impulsivity dimensions for probably unblinded assessment (parent assessment and probably blinded assessment (teacher assessment in children with Attention Deficit Hyperactivity Disorder (ADHD.Data Sources A systematic review identified independent studies that were eligible for inclusion in a random effects meta-analysis.Data Extraction Effect sizes for ADHD symptoms were expressed as standardized mean differences (SMD with 95% confidence intervals.ResultsFive identified studies met eligibility criteria, 263 patients with ADHD were included, 146 patients were trained with EEG-NF. On parent assessment (probably unblinded assessment, the overall ADHD score (SMD=-0.49 [-0.74, -0.24], the inattention score (SMD=-0.46 [-0.76, -0.15] and the hyperactivity/impulsivity score (SMD=-0.34 [-0.59, -0.09] were significantly improved in patients receiving EEG-NF compared to controls. On teacher assessment (probably blinded assessment, only the inattention score was significantly improved in patients receiving EEG-NF compared to controls (SMD=-0.30 [-0.58, -0.03]. ConclusionsThis meta-analysis of EEG-NF in children with ADHD highlights improvement in the inattention dimension of ADHD symptoms. Future investigations should pay greater attention to adequately blinded studies and EEG-NF protocols that carefully control the implementation and embedding of training.

  2. Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

    Directory of Open Access Journals (Sweden)

    Zhijie eBian

    2014-02-01

    Full Text Available Objective: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI from normal cognitive function in type 2 diabetes. Materials and Methods: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well. Results: The main findings of this study were that: 1 compared with the control group, the ratios of power in theta band (P(theta versus power in alpha band (P(alpha (P(theta/P(alpha in the frontal region and left temporal region were significantly higher for aMCI, and 2 for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC and left posterior-right posterior (LP-RP regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT region were increased. Conclusion: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.

  3. Deep learning with convolutional neural networks for EEG decoding and visualization.

    Science.gov (United States)

    Schirrmeister, Robin Tibor; Springenberg, Jost Tobias; Fiederer, Lukas Dominique Josef; Glasstetter, Martin; Eggensperger, Katharina; Tangermann, Michael; Hutter, Frank; Burgard, Wolfram; Ball, Tonio

    2017-11-01

    Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end-to-end EEG analysis, but a better understanding of how to design and train ConvNets for end-to-end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task-related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG-based brain mapping. Hum Brain Mapp 38:5391-5420, 2017. © 2017 Wiley Periodicals, Inc. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  4. Linking EEG signals, brain functions and mental operations: Advantages of the Laplacian transformation.

    Science.gov (United States)

    Vidal, Franck; Burle, Boris; Spieser, Laure; Carbonnell, Laurence; Meckler, Cédric; Casini, Laurence; Hasbroucq, Thierry

    2015-09-01

    Electroencephalography (EEG) is a very popular technique for investigating brain functions and/or mental processes. To this aim, EEG activities must be interpreted in terms of brain and/or mental processes. EEG signals being a direct manifestation of neuronal activity it is often assumed that such interpretations are quite obvious or, at least, straightforward. However, they often rely on (explicit or even implicit) assumptions regarding the structures supposed to generate the EEG activities of interest. For these assumptions to be used appropriately, reliable links between EEG activities and the underlying brain structures must be established. Because of volume conduction effects and the mixture of activities they induce, these links are difficult to establish with scalp potential recordings. We present different examples showing how the Laplacian transformation, acting as an efficient source separation method, allowed to establish more reliable links between EEG activities and brain generators and, ultimately, with mental operations. The nature of those links depends on the depth of inferences that can vary from weak to strong. Along this continuum, we show that 1) while the effects of experimental manipulation can appear widely distributed with scalp potentials, Laplacian transformation allows to reveal several generators contributing (in different manners) to these modulations, 2) amplitude variations within the same set of generators can generate spurious differences in scalp potential topographies, often interpreted as reflecting different source configurations. In such a case, Laplacian transformation provides much more similar topographies, evidencing the same generator(s) set, and 3) using the LRP as an index of response activation most often produces ambiguous results, Laplacian-transformed response-locked ERPs obtained over motor areas allow resolving these ambiguities. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

    Science.gov (United States)

    Wang, Deng; Miao, Duoqian; Blohm, Gunnar

    2012-01-01

    Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

  6. Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

    Science.gov (United States)

    Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

    2017-07-01

    Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

  7. Time frequency analysis of olfactory induced EEG-power change.

    Directory of Open Access Journals (Sweden)

    Valentin Alexander Schriever

    Full Text Available The objective of the present study was to investigate the usefulness of time-frequency analysis (TFA of olfactory-induced EEG change with a low-cost, portable olfactometer in the clinical investigation of smell function.A total of 78 volunteers participated. The study was composed of three parts where olfactory stimuli were presented using a custom-built olfactometer. Part I was designed to optimize the stimulus as well as the recording conditions. In part II EEG-power changes after olfactory/trigeminal stimulation were compared between healthy participants and patients with olfactory impairment. In Part III the test-retest reliability of the method was evaluated in healthy subjects.Part I indicated that the most effective paradigm for stimulus presentation was cued stimulus, with an interstimulus interval of 18-20s at a stimulus duration of 1000ms with each stimulus quality presented 60 times in blocks of 20 stimuli each. In Part II we found that central processing of olfactory stimuli analyzed by TFA differed significantly between healthy controls and patients even when controlling for age. It was possible to reliably distinguish patients with olfactory impairment from healthy individuals at a high degree of accuracy (healthy controls vs anosmic patients: sensitivity 75%; specificity 89%. In addition we could show a good test-retest reliability of TFA of chemosensory induced EEG-power changes in Part III.Central processing of olfactory stimuli analyzed by TFA reliably distinguishes patients with olfactory impairment from healthy individuals at a high degree of accuracy. Importantly this can be achieved with a simple olfactometer.

  8. Oscillatory EEG correlates of arithmetic strategies: A training study

    Directory of Open Access Journals (Sweden)

    Roland H. Grabner

    2012-10-01

    Full Text Available There has been a long tradition of research on mathematics education showing that children and adults use different strategies to solve arithmetic problems. Neurophysiological studies have recently begun to investigate the brain correlates of these strategies. The existing body of data, however, reflect static end points of the learning process and do not provide information on how brain activity changes in response to training or intervention. In this study, we explicitly address this issue by training participants in using fact retrieval strategies. We also investigate whether brain activity related to arithmetic fact learning is domain-specific or whether this generalizes to other learning materials, such as the solution of figural-spatial problems. Twenty adult students were trained on sets of two-digit multiplication problems and figural-spatial problems. After the training, they were presented with the trained and untrained problems while their brain activity was recorded by means of electroencephalography (EEG . In both problem types, the training resulted in accuracies over 90 % and significant decreases in solution times. Analyses of the oscillatory EEG data also revealed training effects across both problem types. Specifically, we observed training-related activity increases in the theta band (3-6 Hz and decreases in the lower alpha band (8-10 Hz, especially over parieto-occipital and parietal brain regions. These results provide the first evidence that a short term fact retrieval training results in significant changes in oscillatory EEG activity. These findings further corroborate the role of the theta band in the retrieval of semantic information from memory and suggest that theta activity is not only sensitive to fact retrieval in mental arithmetic but also in other domains.

  9. EEG Mind Controlled Smart Prosthetic Arm – A Comprehensive Study

    Directory of Open Access Journals (Sweden)

    Taha Beyrouthy

    2017-06-01

    Full Text Available Recently, the field of prosthetics has seen many accomplishments especially with the integration of technological advancements. In this paper, different arm types (robotic, surgical, bionic, prosthetic and static are analyzed in terms of resistance, usage, flexibility, cost and potential. Most of these techniques have some problems; they are extremely expensive, hard to install and maintain and may require surgery. Therefore, our work introduces the initial design of an EEG mind controlled smart prosthetic arm. The arm is controlled by the brain commands, obtained from an electroencephalography (EEG headset, and equipped with a network of smart sensors and actuators that give the patient intelligent feedback about the surrounding environment and the object in contact. This network provides the arm with normal hand functionality, smart reflexes and smooth movements. Various types of sensors are used including temperature, pressure, ultrasonic proximity sensors, accelerometers, potentiometers, strain gauges and gyroscopes. The arm is completely 3D printed built from various lightweight and high strength materials that can handle high impacts and fragile elements as well. Our project requires the use of nine servomotors installed at different places in the arm. Therefore, the static and dynamic modes of servomotors are analyzed. The total cost of the project is estimated to be relatively cheap compared to other previously built arms. Many scenarios are analyzed corresponding to the actions that the prosthetic arm can perform, and an algorithm is created to match these scenarios. Experimental results show that the proposed EEG Mind-controlled Arm is a promising alternative for current solutions that require invasive and expensive surgical procedures.

  10. Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

    Science.gov (United States)

    Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

    2017-12-01

    Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

  11. Effects of positive emotion, extraversion, and dopamine on cognitive stability-flexibility and frontal EEG asymmetry.

    Science.gov (United States)

    Wacker, Jan

    2018-01-01

    The influence of positive emotions on the balance between cognitive stability and flexibility has been suggested to (a) differ among various positive emotional/motivational states (e.g., of varying approach motivation intensity), and (b) be mediated by brain dopamine (DA). Frontal EEG alpha asymmetry (ASY) is considered an indicator of approach motivational states and may be modulated by DA. The personality trait of extraversion is strongly linked to positive emotions and is now thought to reflect DA-based individual differences in incentive/approach motivation. The present study independently manipulated positive emotion (high approach wanting-expectancy [WE] vs. low approach warmth-liking [WL]) and dopamine (placebo vs. DA D2 blocker sulpiride) to examine their effects on both cognitive stability-flexibility and emotion-related ASY changes. The results showed numerically lower stability-flexibility in WE versus WL under placebo and a complete reversal of this effect under the D2 blocker, no differentiation between WE and WL groups in terms of emotion-related ASY change, but an association between self-reported WE and WL and ASY changes toward left and right frontal cortical activity, respectively. Finally, extraversion was positively associated with both stability-flexibility and ASY changes toward left frontal cortical activity under placebo, and these associations were completely reversed under the D2 blocker. The results (a) support a dopaminergic basis for frontal EEG asymmetry, extraversion, and the modulating effect of positive emotions on stability-flexibility, and (b) extend previous reports of cognitive differences between introverts and extraverts. © 2017 Society for Psychophysiological Research.

  12. On a Possible Relationship between Linguistic Expertise and EEG Gamma Band Phase Synchrony

    Science.gov (United States)

    Reiterer, Susanne; Pereda, Ernesto; Bhattacharya, Joydeep

    2011-01-01

    Recent research has shown that extensive training in and exposure to a second language can modify the language organization in the brain by causing both structural and functional changes. However it is not yet known how these changes are manifested by the dynamic brain oscillations and synchronization patterns subserving the language networks. In search for synchronization correlates of proficiency and expertise in second language acquisition, multivariate EEG signals were recorded from 44 high and low proficiency bilinguals during processing of natural language in their first and second languages. Gamma band (30–45 Hz) phase synchronization (PS) was calculated mainly by two recently developed methods: coarse-graining of Markov chains (estimating global phase synchrony, measuring the degree of PS between one electrode and all other electrodes), and phase lag index (PLI; estimating bivariate phase synchrony, measuring the degree of PS between a pair of electrodes). On comparing second versus first language processing, global PS by coarse-graining Markov chains indicated that processing of the second language needs significantly higher synchronization strength than first language. On comparing the proficiency groups, bivariate PS measure (i.e., PLI) revealed that during second language processing the low proficiency group showed stronger and broader network patterns than the high proficiency group, with interconnectivities between a left fronto-parietal network. Mean phase coherence analysis also indicated that the network activity was globally stronger in the low proficiency group during second language processing. PMID:22125542

  13. EEG, PET, SPET and MRI in intractable childhood epilepsies: possible surgical correlations.

    Science.gov (United States)

    Fois, A; Farnetani, M A; Balestri, P; Buoni, S; Di Cosmo, G; Vattimo, A; Guazzelli, M; Guzzardi, R; Salvadori, P A

    1995-12-01

    Magnetic resonance imaging (MRI), single photon emission tomography (SPET), and positron emission tomography (PET) using [18F]fluorodeoxyglucose were used in combination with scalp and scalp-video EEGs in a group of 30 pediatric patients with drug resistant epilepsy (DRE) in order to identify patients who could benefit from neurosurgical approach. Seizures were classified according to the consensus criteria of The International League Against Epilepsy. In three patients infantile spasms (IS) were diagnosed; 13 subjects were affected by different types of generalized seizures, associated with complex partial seizures (CPS) in three. In the other 14 patients partial seizures, either simple (SPS) or complex, were present. A localized abnormality was demonstrated in one patient with IS and in three patients with generalized seizures. Of the group of 14 subjects with CPS, MRI and CT were normal in 7, but SPET or PET indicated focal hypoperfusion or hypometabolism concordant with the localization of the EEG abnormalities. In 5 of the other 7 patients anatomical and functional imaging and EEG findings were concordant for a localized abnormality. It can be concluded that functional imaging combined with scalp EEGs appears to be superior to the use of only CT and MRI for selecting children with epilepsy in whom a surgical approach can be considered, in particular when CPS resistant to therapy are present.

  14. Electrocortical (EEG correlates of music and states of consciousness

    Directory of Open Access Journals (Sweden)

    Lazar Skaric

    2007-11-01

    Full Text Available The study of the perception of music is a paramount example of multidisciplinary research. In spite of a lot of theoretical and experimental efforts to understand musical processing, attempts to localize musical abilities in particular brain regions were largely unsuccessful, save for the difference between musicians and non musicians, especially in hemispheric specialization and in EEG correlational dimensions. Having in mind that human emotional response to music and to art in general is limbic dependent, this motivated us to address our question to a similar possible neurobiological origin of musicogenic altered states of consciousness and its possible EEG correlates, “resonantly” induced by deep spiritual music. For example, as in sound-induced altered states of consciousness cultivated in some Eastern yogic practices. The musicogenic states of consciousness are evaluated within a group of 6 adults, upon the influence of 4 types of spiritual music. The most prominent changes in theta or alpha frequency bands were induced in two subjects, upon the influence of Indian spiritual music, Bhajan.

  15. 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.

  16. Early EEG for outcome prediction of postanoxic coma : Prospective cohort study with cost-minimization analysis

    NARCIS (Netherlands)

    Sondag, Lotte; Ruijter, Barry J.; Tjepkema-Cloostermans, Marleen C.; Beishuizen, Albertus; Bosch, Frank H.; van Til, Janine A.; van Putten, Michel J.A.M.; Hofmeijer, Jeannette

    2017-01-01

    Background: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis.

  17. 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…

  18. Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

    Science.gov (United States)

    Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

    2016-01-01

    Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

  19. Correlation between EEG abnormalities and symptoms of autism spectrum disorder (ASD).

    Science.gov (United States)

    Yasuhara, Akihiro

    2010-11-01

    Children with ASD often suffer from epilepsy and paroxysmal EEG abnormality. Purposes of this study are the confirmation of incidence of epileptic seizures and EEG abnormalities in children with autism using a high performance digital EEG, to examine the nature of EEG abnormalities such as locus or modality, and to determine if the development of children with ASD, who have experienced developmental delay, improves when their epilepsy has been treated and maintained under control. A total of 1014 autistic children that have been treated and followed-up for more than 3 years at Yasuhara Children's Clinic in Osaka, Japan, were included in this study. Each participant's EEG had been recorded approximately every 6 months under sleep conditions. Epilepsy was diagnosed in 37% (375/1014) of the study participants. Almost all patients diagnosed with epilepsy presented with symptomatic epilepsy. The data showed that the participants with lower IQ had a higher incidence of epileptic seizures. Epileptic EEG discharges occurred in 85.8% (870/1014) of the patients. There was also a very high incidence of spike discharges in participants whose intellectual quotient was very low or low. Epileptic seizure waves most frequently developed from the frontal lobe (65.6%), including the front pole (Fp1 and Fp2), frontal part (F3, F4, F7 and F8) and central part (C3, Cz and C4). The occurrence rate of spike discharges in other locations, including temporal lobe (T3, T4, T5, T6), parietal lobe (P3, Pz, P4), occipital lobe (O1, O2) and multifocal spikes was less than 10%. These results support the notion that there is a relationship between ASD and dysfunction of the mirror neuron system. The management of seizure waves in children diagnosed with ASD may result in improves function and reduction of autistic symptoms. Copyright © 2010 Elsevier B.V. All rights reserved.

  20. 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

  1. 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

  2. [EEG alpha indices in dependence on the menstrual cycle phase and salivary progesterone].

    Science.gov (United States)

    Bazanova, O M; Kondratenko, A V; Kuz'minova, O I; Muravleva, K B; Petrova, S E

    2014-01-01

    The effects of the neurohumoral status on the EEG alpha - activity indices were studied in a within-subject design with 78 women aged 18-27 years during 1-2 menstrual cycle. Psychometric and EEG indices of alpha waves basal body temperature, saliva progesterone and cortisol level were monitored every 2-3 days. Menstrual and follicular recording sessions occurred before the ovulatory temperature rise, luteal recording session--after increasing progesterone level more than 20% respect to previous day and premenstrual sessions after decreasing progesterone level more that 20% respect to previous day. The design consisted of rest and task periods EEG, EMG and ECG recordings. Half the subjects began during their menstrual phase and half began during their luteal phase. All 5 phases were compared for differences between psychometric features EEG alpha activity, EMG and ECG baseline resting levels, as well as for reactivity to cognitive task. The results showed menstrual phase differences in all psychometric and alpha EEG indices. The cognitive fluency, alpha peak frequency, alpha band width, power in alpha-2 frequency range are maximal at luteal, alpha visual activation and reactivity to cognitive task performance--at follicular phase. The hypothesis that the EEG alpha activity depends on the hormonal status supported by the positive association salivary progesterone level with the alpha peak frequency, power in the alpha-2 band and negative--with the power of the alpha-1 band. According these results, we conclude that psycho-physiological recording sessions with women might be provided with a glance to phase of menstrual cycle.

  3. National French survey on tele-transmission of EEG recordings: More than a simple technological challenge.

    Science.gov (United States)

    Sauleau, Paul; Despatin, Jane; Cheng, Xufei; Lemesle, Martine; Touzery-de Villepin, Anne; N'Guyen The Tich, Sylvie; Kubis, Nathalie

    2016-04-01

    Assessment of current practice and the need for tele-transmission and remote interpretation of EEG in France. Transmission of EEG to a distant center could be a promising solution to the problem of decreasing availability of neurophysiologists for EEG interpretation, in order to provide equity within health care services in France. This practice should logically follow the legal framework of telemedicine and the recommendations that were recently edited by the Société de neurophysiologie clinique de langue française (SNCLF) and the Ligue française contre l'épilepsie (LCFE). A national survey was designed and performed under the auspices of the SNCLF. This survey reveals that there is an important gap between the official recommendations and the "reality on the ground". These local organizations were mainly established through the impulse of individual initiatives, rarely driven by health regulatory authorities and sometimes far from legal frameworks. For the majority, they result from a need to improve medical care, especially in pediatrics and neonatology, and to ensure continuity of care. When present, tele-transmission of EEG is often only partially satisfactory, since many technical procedures have to be improved. Conversely, the lack of tele-transmission of EEG would penalize medical care for some patients. The survey shows both the wealth of local initiatives and the fragility of most existing networks, emphasizing the need for better cooperation between regulatory authorities and health care professionals to establish or improve the transmission of EEG in France. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  4. Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

    Science.gov (United States)

    Sadeh, Boaz; Yovel, Galit

    2014-01-01

    Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator's coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes. PMID:24893706

  5. Extracting visual evoked potentials from EEG data recorded during fMRI-guided transcranial magnetic stimulation.

    Science.gov (United States)

    Sadeh, Boaz; Yovel, Galit

    2014-05-12

    Transcranial Magnetic Stimulation (TMS) is an effective method for establishing a causal link between a cortical area and cognitive/neurophysiological effects. Specifically, by creating a transient interference with the normal activity of a target region and measuring changes in an electrophysiological signal, we can establish a causal link between the stimulated brain area or network and the electrophysiological signal that we record. If target brain areas are functionally defined with prior fMRI scan, TMS could be used to link the fMRI activations with evoked potentials recorded. However, conducting such experiments presents significant technical challenges given the high amplitude artifacts introduced into the EEG signal by the magnetic pulse, and the difficulty to successfully target areas that were functionally defined by fMRI. Here we describe a methodology for combining these three common tools: TMS, EEG, and fMRI. We explain how to guide the stimulator's coil to the desired target area using anatomical or functional MRI data, how to record EEG during concurrent TMS, how to design an ERP study suitable for EEG-TMS combination and how to extract reliable ERP from the recorded data. We will provide representative results from a previously published study, in which fMRI-guided TMS was used concurrently with EEG to show that the face-selective N1 and the body-selective N1 component of the ERP are associated with distinct neural networks in extrastriate cortex. This method allows us to combine the high spatial resolution of fMRI with the high temporal resolution of TMS and EEG and therefore obtain a comprehensive understanding of the neural basis of various cognitive processes.

  6. Hemodynamic response to Interictal Epileptiform Discharges addressed by personalized EEG-fNIRS recordings

    Directory of Open Access Journals (Sweden)

    Giovanni ePellegrino

    2016-03-01

    Full Text Available Objective: We aimed at studying the hemodynamic response (HR to Interictal Epileptic Discharges (IEDs using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG and functional Near InfraRed Spectroscopy (fNIRS recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (7 patients, followed b