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Sample records for human electroencephalogram eeg

  1. Electroencephalogram (EEG) (For Parents)

    Science.gov (United States)

    ... Old Feeding Your 1- to 2-Year-Old EEG (Electroencephalogram) KidsHealth > For Parents > EEG (Electroencephalogram) A A A What's in this article? ... Child If You Have Questions en español Electroencefalograma (EEG) What It Is An electroencephalogram (EEG) is a ...

  2. Electroencephalogram (EEG) (For Parents)

    Science.gov (United States)

    ... Old Feeding Your 1- to 2-Year-Old EEG (Electroencephalogram) KidsHealth > For Parents > EEG (Electroencephalogram) Print A A A What's in this ... Child If You Have Questions en español Electroencefalograma (EEG) What It Is An electroencephalogram (EEG) is a ...

  3. Human Emotion Detection via Brain Waves Study by Using Electroencephalogram (EEG

    Directory of Open Access Journals (Sweden)

    W.O. A.S. Wan Ismail

    2016-12-01

    Full Text Available Human emotion is very difficult to determine just by looking at the face and also the behavior of a person. This research was conducted to detect or identify human emotion via the study of brain waves. In addition, the research aims to develop computer software that can detect human emotions quickly and easily. This study aims at EEG signals of relationship and human emotions. The main objective of this recognition is to develop "mind-implementation of Robots". While the research methodology is divided into four; (i both visibility and EEG data were used to extract the date at the same time from the respondent, (ii the process of complete data record includes the capture of images using the camera and EEG, (iii pre-processing, classification and feature extraction is done at the same time, (iv the features extracted is classified using artificial intelligence techniques to emotional faces. Researchers expect the following results; (i studies brain waves for the purpose of emotions, (ii the study of human emotion with facial emotions and to relate the brain waves, (iii. In conclusion, this study is very useful for doctors in hospitals and police departments for criminal investigation. As a result of this study, it also helps to develop a software package.

  4. 21 CFR 882.1855 - Electroencephalogram (EEG) telemetry system.

    Science.gov (United States)

    2010-04-01

    ... signals by means of radio or telephone transmission systems. (b) Classification. Class II (performance... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Electroencephalogram (EEG) telemetry system. 882... Electroencephalogram (EEG) telemetry system. (a) Identification. An electroencephalogram (EEG) telemetry...

  5. Fluctuation Analysis of Human Electroencephalogram

    CERN Document Server

    Hwa, R C; Hwa, Rudolph C.; Ferree, Thomas C.

    2001-01-01

    The scaling behaviors of the human electroencephalogram (EEG) time series are studied using detrended fluctuation analysis. Two scaling regions are found in nearly every channel for all subjects examined. The scatter plot of the scaling exponents for all channels (up to 129) reveals the complicated structure of a subject's brain activity. Moment analyses are performed to extract the gross features of all the scaling exponents, and another universal scaling behavior is identified. A one-parameter description is found to characterize the fluctuation properties of the nonlinear behaviors of the brain dynamics.

  6. 21 CFR 882.1420 - Electroencephalogram (EEG) signal spectrum analyzer.

    Science.gov (United States)

    2010-04-01

    ....1420 Electroencephalogram (EEG) signal spectrum analyzer. (a) Identification. An electroencephalogram (EEG) signal spectrum analyzer is a device used to display the frequency content or power spectral... analyzer. 882.1420 Section 882.1420 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH...

  7. 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.

  8. Scaling Properties of Fluctuations in Human Electroencephalogram

    CERN Document Server

    Hwa, R C; Hwa, Rudolph C.; Ferree, Thomas C.

    2002-01-01

    The fluctuation properties of the human electroencephalogram (EEG) time series are studied using detrended fluctuation analysis. For all 128 channels in each of 18 subjects studied, it is found that the standard deviation of the fluctuations exhibits scaling behaviors in two regions. Topographical plots of the scaling exponents reveal the spatial structure of the nonlinear electrical activities recorded on the scalp. Moment analyses are performed to extract the gross features of all the scaling exponents. The correlation between the two scaling exponents in each channel is also examined. It is found that two indices can characterize the overall properties of the fluctuation behaviors of the brain dynamics for every subject and that they vary widely across the subjects.

  9. Regional differences in cortical electroencephalogram (EEG) slow wave activity and interhemispheric EEG asymmetry in the fur seal

    National Research Council Canada - National Science Library

    LYAMIN, OLEG I; PAVLOVA, IVETTA F; KOSENKO, PETER O; MUKHAMETOV, LEV M; SIEGEL, JEROME M

    2012-01-01

    Slow wave sleep (SWS) in the northern fur seal ( Callorhinus ursinus ) is characterized by a highly expressed interhemispheric electroencephalogram (EEG) asymmetry, called ‘unihemispheric’ or ‘asymmetrical’ SWS...

  10. Scale-invariance of human EEG signals in sleep

    CERN Document Server

    Cai, S M; Wang, B H; Yang, H J; Zhou, P L; Zhou, T; Cai, Shi-Min; Jiang, Zhao-Hui; Wang, Bing-Hong; Yang, Hui-Jie; Zhou, Pei-Ling; Zhou, Tao

    2007-01-01

    We investigate the dynamical properties of electroencephalogram (EEG) signals of human in sleep. By using a modified random walk method, We demonstrate that the scale-invariance is embedded in EEG signals after a detrending procedure. Further more, we study the dynamical evolution of probability density function (PDF) of the detrended EEG signals by nonextensive statistical modeling. It displays scale-independent property, which is markedly different from the turbulent-like scale-dependent PDF evolution.

  11. Predictive value of sequential electroencephalogram (EEG) in neonates with seizures and its relation to neurological outcome.

    Science.gov (United States)

    Khan, Richard Lester; Nunes, Magda Lahorgue; Garcias da Silva, Luis Fernando; da Costa, Jaderson Costa

    2008-02-01

    The aim of this study was to evaluate the relationship of sequential neonatal electroencephalography (EEG) and neurological outcome in neonates with seizures to identify polysomnographic features predictive of outcome. Sequential EEGs recordings of 58 neonates that belonged to 2 historical cohorts of newborns with seizures from the same neonatal intensive care unit and who had follow-up at the Neurodevelopment Clinic of the Hospital São Lucas, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS) in Porto Alegre, Brazil, were analyzed and classified into 4 groups: normal-normal, abnormal-normal, abnormal-abnormal, normal-abnormal. In patients with more than 2 recordings, during the neonatal period, the first EEG was compared with the following more abnormal. A total of 58 pairs of 2 sequential EEGs were analyzed. Considering the first EEG, a statistically significant difference was observed between the relationship of the result of this exam, if it was abnormal, with developmental delay (P = .030) and postnatal death (P = .030). Abnormal background activity was also related to neurodevelopment delay (P = .041). EEG sequences abnormal-abnormal and normal-abnormal significantly correlated to the outcome epilepsy ( P = .015). Abnormal sequential background activity was associated with neurodevelopment delay (P = .006) and epilepsy (P = .041). The burst suppression pattern when present in any EEG correlated with epilepsy (P = .013) and postnatal death (P = .034). Sequential abnormal background patterns in the first and second EEG increased the risk for epilepsy (relative risk [RR] = 1.8; 95% confidence interval [CI] = 1.03-3.0) and neurodevelopment delay (RR = 2.20; 95% CI = 1.3-3.0). Abnormal background activity only in the second electroencephalogram increased the risk for neurodevelopment delay (RR = 2.20; 95% CI = 1.3-3.0). All the neonates (n = 33) with seizures related to probable hypoxic ischemic encephalopathy had abnormalities in the first EEG (P

  12. New revelations about Hans Berger, father of the electroencephalogram (EEG), and his ties to the Third Reich.

    Science.gov (United States)

    Zeidman, Lawrence A; Stone, James; Kondziella, Daniel

    2014-07-01

    Hans Berger was a German neuropsychiatrist and head of the neurology department at the University of Jena, who discovered the human electroencephalogram (EEG). Many sources state that Berger was forced into retirement and suicide by the Nazis because he was at odds with the regime. In fact, Berger helped select his Nazi successor Berthold Kihn (complicit in "euthanasia" murders), financially supported the Nazi Schutzstaffel (SS), and was a willing participant on Nazi genetic health higher courts that reviewed appeals for forced sterilizations of neuropsychiatric patients. His motivations could be related to avoiding Nazi harassment, indoctrination by Nazi ideology, or less likely, career opportunism. His actions stand in contrast to colleagues who partially resisted the Nazis, and hopefully will serve as an example to future generations of neurologists regarding the danger of allowing one's professional standing to be used as a tool to support the policies of tyranny and oppression.

  13. A non-invasive technique for measuring the electroencephalogram of broiler chickens in a fast way: the 'chicken EEG clamp' (CHEC)

    NARCIS (Netherlands)

    Coenen, A.M.L.; Prinz, S.; Oijen, G.B.M. van; Bessei, W.

    2007-01-01

    A device was developed to measure in a fast way the electroencephalogram (EEG) of broiler chickens in a non-invasive way. The 'chicken EEG clamp' (CHEC) consists of a framework with two pointed electrodes, fitting as a clamp around the chicken's head. The EEG is recorded by the two active electrodes

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

    Directory of Open Access Journals (Sweden)

    Fitri Aprilianty

    2016-06-01

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

  15. Block Sparse Compressed Sensing of Electroencephalogram (EEG Signals by Exploiting Linear and Non-Linear Dependencies

    Directory of Open Access Journals (Sweden)

    Hesham Mahrous

    2016-02-01

    Full Text Available This paper proposes a compressive sensing (CS method for multi-channel electroencephalogram (EEG signals in Wireless Body Area Network (WBAN applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV problem, the Block Sparse Bayesian Learning-BO (BSBL-BO method has been shown to yield good results. This method exploits the block sparsity and the intra-correlation (i.e., the linear dependency within the measurement vector of a single channel. For the multichannel case, known as the multi-measurement vector (MMV problem, the Spatio-Temporal Sparse Bayesian Learning (STSBL-EM method has been proposed. This method learns the joint correlation structure in the multichannel signals by whitening the model in the temporal and the spatial domains. Our proposed method represents the multi-channels signal data as a vector that is constructed in a specific way, so that it has a better block sparsity structure than the conventional representation obtained by stacking the measurement vectors of the different channels. To reconstruct the multichannel EEG signals, we modify the parameters of the BSBL-BO algorithm, so that it can exploit not only the linear but also the non-linear dependency structures in a vector. The modified BSBL-BO is then applied on the vector with the better sparsity structure. The proposed method is shown to significantly outperform existing SMV and also MMV methods. It also shows significant lower compression errors even at high compression ratios such as 10:1 on three different datasets.

  16. Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

    CERN Document Server

    Wingeier, B M; Silberstein, R B; Wingeier, Brett M.; Nunez, Paul L.; Silberstein, Richard B.

    2001-01-01

    We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.

  17. Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

    OpenAIRE

    Wingeier, Brett M.; Nunez, Paul L.; Silberstein, Richard B.

    2000-01-01

    We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.

  18. Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Mohamed Rizon

    2010-01-01

    Full Text Available Problem statement: The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG. Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International 10-10 system. We firstly applied Spatial Filtering technique to remove the noises and artifacts from the EEG signals. Three wavelet functions ("db8", "sym8" and "coif5" were used to decompose the EEG signal into five different frequency bands namely: delta, theta, alpha, beta and gamma. A set of new statistical features related to energy were extracted from the EEG frequency bands to construct the feature vector for classifying the emotions. Two simple linear classifiers (K Nearest Neighbor (KNN and Linear Discriminant Analysis (LDA were used for mapping the feature vector into corresponding emotions. Furthermore, we compared the efficacy of emotion classification with a reduced set of channels (24 channels for evaluating the reliability of the emotion recognition system. Results: In this study, 62 channels outperform 24 channels by giving the maximum average classification accuracy of 79.65% using KNN and 78.52% using LDA. Conclusion: In this study we presented an approach to discrete emotion recognition based on the processing of EEG signals. The preliminary results resented in this study address the classifiability of human emotions using original and reduced set of EEG channels. The results presented in this study indicated that, statistical features extracted from time-frequency analysis (wavelet transform works well in the context of discrete emotion classification.

  19. A RECURRENT ELMAN NEURAL NETWORK - BASED APPROACH TO DETECT THE PRESENCE OF EPILEPTIC ATTACK IN ELECTROENCEPHALOGRAM (EEG SIGNALS

    Directory of Open Access Journals (Sweden)

    Mr.S.Sundaram

    2014-10-01

    Full Text Available Epileptic attack persons are detected largely on the analysis of Electroencephalogram (EEG signals. The EEG signals recordings generate very bulk data which require a skilled and careful analysis. This method can be automated based on Elman Neural Network by using a time frequency domain characteristics of EEG signal called Approximate Entropy (ApEn. This method consists of EEG collection of data, extraction and classification. EEG data from normal persons and epileptic affected persons was collected, digitized and then fed into the Elman neural network. This proposed system proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn as the input feature. Approximate Entropy (ApEn [1] is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the Approximate Entropy drops sharply during an epileptic attack[2]and this fact is used in the proposed system. Type of a neural network namely, Elman neural network is considered in this paper. The experimental results portray that this proposed approach efficiently detects the presence of epileptic seizures[3] in EEG signals and showed a reasonable accuracy.

  20. A genetic study of the human low-voltage electroencephalogram.

    Science.gov (United States)

    Anokhin, A; Steinlein, O; Fischer, C; Mao, Y; Vogt, P; Schalt, E; Vogel, F

    1992-01-01

    The studied phenotype, the low-voltage electroencephalogram (LVEEG), is characterized by the absence of an alpha rhythm from the resting EEG. In previous studies, evidence was found for a simple autosomal-dominant mode of inheritance of the LVEEG. Such a polymorphism in brain function can be used as a research model for the stepwise elucidation of the molecular mechanism involved in those aspects of neuronal activity that are reflected in the EEG. Linkage with the variable number of tandem repeats (VNTR) marker CMM6 (D20S19) and localization of an LVEEG (EEGV1) gene on 20q have previously been reported, and genetic heterogeneity has been demonstrated. This latter result has been corroborated by studying new marker (MS214). The phenotype of the LVEEG is described here in greater detail. Its main characteristic is the absence of rhythmic alpha activity, especially in occipital leads, whereas other wave forms such as beta or theta waves may be present. Analysis of 17 new families (some of them large), together with 60 previously described nuclear families, supports the genetic hypothesis of an autosomal-dominant mode of inheritance. Problems connected with the analysis of linkage heterogeneity, exclusion mapping, and the study of multipoint linkage are discussed. A possible explanation of the localization of LVEEG in the close vicinity of another gene influencing synchronization of the normal EEG, the gene for benign neonatal epilepsie, is given.

  1. Shannon entropies of the distributions of various electroencephalograms from epileptic humans

    CERN Document Server

    Tuncay, Caglar

    2009-01-01

    1) Harmonic oscillations (HO) in numerous electroencephalograms (EEG) from different humans are introduced. 2) The probability density functions (PDF, p(X)) of the EEG voltages (X) are normal (Gauss) for OO whereas, the plots for the distributions of HO (pure) are convex. Gaussians for OO may turn to be convex as HO become dominant in MO or vice versa. However, distributions of the most of the data are found normal which means that most of the EEG oscillations consist of OO (or MO). 3) Shannon entropies (information measures) of the distributions of the data from different brain regions in the ictal intervals or inter-ictal intervals are calculated for each individual recording and compared. The averages of Shannon entropies over the individual recordings during the ictal intervals come out bigger than those from the inter-ictal intervals. These averages are found to be bigger for the data from epileptogenic brain areas than those recorded from non epileptogenic ones in different intervals.

  2. The effect of mobile phone electromagnetic fields on the alpha rhythm of human electroencephalogram.

    Science.gov (United States)

    Croft, R J; Hamblin, D L; Spong, J; Wood, A W; McKenzie, R J; Stough, C

    2008-01-01

    Mobile phones (MP) emit low-level electromagnetic fields that have been reported to affect neural function in humans; however, demonstrations of such effects have not been conclusive. The purpose of the present study was to test one of the strongest findings in the literature; that of increased "alpha" power in response to MP-type radiation. Healthy participants (N = 120) were tested using a double-blind counterbalanced crossover design, with each receiving a 30-min Active and a 30-min Sham Exposure 1 week apart, while electroencephalogram (EEG) data were recorded. Resting alpha power (8-12 Hz) was then derived as a function of time, for periods both during and following exposure. Non-parametric analyses were employed as data could not be normalized. Previous reports of an overall alpha power enhancement during the MP exposure were confirmed (relative to Sham), with this effect larger at ipsilateral than contralateral sites over posterior regions. No overall change to alpha power was observed following exposure cessation; however, there was less alpha power contralateral to the exposure source during this period (relative to ipsilateral). Employing a strong methodology, the current findings support previous research that has reported an effect of MP exposure on EEG alpha power.

  3. 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…

  4. 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

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

    Directory of Open Access Journals (Sweden)

    Naoyuki eSato

    2013-05-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

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

    Directory of Open Access Journals (Sweden)

    B.Sabarigiri

    2014-05-01

    Full Text Available the embedding of low cost electroencephalogram (EEG sensors in wireless headsets gives improved authentication based on their brain wave signals has become a practical opportunity. In this paper signal acquisition along with effective multi-channel selection from a specific area of the brain and denoising using AWICA methods are proposed for EEG based personal identification. At this point, to develop identification system the steps are as follows. (i the high-quality device with the least numbers of channels are essential for the EEG signal acquisition and Selecting the equipment and verdict the best portions on the scalp is the primary step. (ii Scrutiny of the acquired EEG signals and de-noising from EMG, ECG, EOG Signals and power line artifacts using AWICA (iii Obtain the features from the Enhanced EEG signals by Wavelet Transform (WT and LS-SVM Classification in the MATLAB Environment. Based on the outcome, there is possibility for implementation of an EEG based Practical biometric system.

  8. An analysis of electroencephalogram (EEG) On Children’s mental retardation QUAN Yan%精神发育迟滞患儿的脑电图分析

    Institute of Scientific and Technical Information of China (English)

    全琰

    2013-01-01

      目的:通过观察和分析精神发育迟滞患儿脑电图(EEG)的表现,探讨脑电图检查对精神发育迟滞的诊断意义。方法:回顾性分析55例临床确诊为精神发育迟滞患儿的 EEG 改变。结果:精神发育迟滞脑电图有背景脑波频率偏慢,弥漫性高幅δ波或θ波,睡眠纺锤波改变,痫样放电及持续性低电压,异常率73%。结论:EEG 客观反映了精神发育迟滞患儿的脑功能情况,对儿童精神发育迟滞具有重要的诊断价值。%Objective:To discuss the diagnostic value of electroencephalogram (EEG) on Children’s mental retardation with the analysis of its EEG presentations. Method:Retrospective analyzes the EEG of 55 cases of mental retardation child patients. Results: EEG findings of mental retardation were characterized by the slow background rhythm, the diffuse high delta wave or theta wave, the variation of sleep spindle, the epileptiform discharges and the sustained low voltage. The abnormal rate was 73%.Conclusion: EEG reflects the mental function of MR Child patients objectively, and it is valuable in the diagnosis of mental retardation.

  9. Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

    Directory of Open Access Journals (Sweden)

    Chai Tong Yuen

    2013-05-01

    Full Text Available This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.

  10. Adaptive filtering of electroencephalogram signals using the empirical-modes method

    Science.gov (United States)

    Grubov, V. V.; Runnova, A. E.; Koronovskii, A. A.; Hramov, A. E.

    2017-07-01

    A new method for the removal of physiological artifacts in the experimental signals of human electroencephalograms (EEGs) has been developed. The method is based on decomposition of the signal in terms of empirical modes. The algorithm involves EEG signal decomposition in terms of empirical modes, searching for modes with artifacts, removing these modes, and restoration of the EEG signal. The method was tested on experimental data and showed high efficiency in the removal of various physiological artifacts in EEGs.

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

    Directory of Open Access Journals (Sweden)

    Yuan-Pin eLin

    2014-03-01

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

  12. A data-driven model of the generation of human EEG based on a spatially distributed stochastic wave equation

    OpenAIRE

    Galka, Andreas; Ozaki, Tohru; Muhle, Hiltrud; Stephani, Ulrich; Siniatchkin, Michael

    2008-01-01

    We discuss a model for the dynamics of the primary current density vector field within the grey matter of human brain. The model is based on a linear damped wave equation, driven by a stochastic term. By employing a realistically shaped average brain model and an estimate of the matrix which maps the primary currents distributed over grey matter to the electric potentials at the surface of the head, the model can be put into relation with recordings of the electroencephalogram (EEG). Through ...

  13. 头痛患儿180例脑电图分析%Electroencephalogram( EEG ) characteristics and clinical analysis of pediatric headache

    Institute of Scientific and Technical Information of China (English)

    张贺齐; 董静

    2012-01-01

    Objective To explore the value of electroencephalogram (EEG) in children with headache.Methods 180 children with headache were carried out EEG recording,and the electrical activity of the brain was recorded and analyzed.Results In 180 cases of children,EEG was normal in 56 cases,abnormalities in 124 cases,the abnormal rate was 68.89%.The mild abnormalities was 78 cases,in a proportion to 43.33% in the total record:moderate abnormalities was 14 cases,in a proportion to 7.78% in the total reccrd;high-grede,abnormalities was 2 cases,in a proportion to 1.11% ;5 patients was in critical state,in a proportion to 2.78% ;epileptiform activity was 25 cases,in a proportion to 13.89%.97 normal cases with intermittent headache,abnormal rate was 46.1 1%,which was significantly lower than the attack headache ( x2 =15.16,P < 0.05 ).Conclusion Headache in children with EEG examination had important significance.%目的 探讨脑电图(EEG)检查对小儿头痛病因的诊断和鉴别诊断的价值.方法 对180例头痛患儿进行EEG描记,记录其脑电活动情况并分析.结果 180例患儿中,EEG表现正常56例,异常124例,异常率68.89%,其中轻度异常78例,占43.33%;中度异常14例,占7.78%;高度异常2例,占1.11%;临界状态5例,占2,78%;痫样活动25例,占13.89%.头痛间歇期正常97例,异常率为46.11%,显著低于发作期(x2 =15.16,P<0.05).结论 对头痛患儿行EEC检查有重要意义.

  14. Regional electroencephalogram (EEG) alpha power and asymmetry in older adults: a study of short-term test-retest reliability.

    Science.gov (United States)

    Mathewson, Karen J; Hashemi, Ali; Sheng, Bruce; Sekuler, Allison B; Bennett, Patrick J; Schmidt, Louis A

    2015-01-01

    Although regional alpha power and asymmetry measures have been widely used as indices of individual differences in emotional processing and affective style in younger populations, there have been relatively few studies that have examined these measures in older adults. Here, we examined the short-term test-retest reliability of resting regional alpha power (7.5-12.5 Hz) and asymmetry in a sample of 38 active, community-dwelling older adults (M age = 71.2, SD = 6.5 years). Resting electroencephalogram recordings were made before and after a perceptual computer task. Pearson and intra-class correlations indicated acceptable test-retest reliability for alpha power and asymmetry measures in all regions. Interestingly, alpha asymmetry appeared to be less affected by the task than was alpha power. Findings suggest that alpha asymmetry may reflect more enduring, "trait-like" characteristics, while alpha power may reflect more "state-like" processes in older adults.

  15. 婴儿痉挛的预后与EEG和头颅CT结果的关系%The relationship between the electroencephalogram(EEG),head computerized tomography and the prognosis of spasm in infants

    Institute of Scientific and Technical Information of China (English)

    李正秀; 俞曙星; 董鸿雁

    2002-01-01

    Objective To investigate relationship between prognosis of infant spasm and electroencephalogram(EEG) and head CT.Method 47 infants underwent EEG and head CT.Follow up was performed to compare the prognosis during different periods.Result Among 31 infants with abnormal head CT,2 infants were cured,17 were improved and effective rate was 61.3% . Among 16 patients with normal head CT,6 were cured,8 were improved,and effective rate was 87.5% . Among 34 infants with high rhythm disorder,8 were cured,21 were improved,effective rate was 85.29% . For 13 infants with abnormal EEG of other types,no infants were cured,4 were improved,and effective rate was 30.8% .Conclusion Changed head CT not various EEG has no significant effect on prognosis of infant spasm(P >0.05).Prognosis is favorable in infants with high rhythm disorder(P<0.01).

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  17. Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset

    OpenAIRE

    Lin, Yuan-Pin; Wang, Yijun; Jung, Tzyy-Ping

    2014-01-01

    Background Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals’ EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an onlin...

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

    Directory of Open Access Journals (Sweden)

    Bratislav Mišić

    2011-06-01

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

  19. A data-driven model of the generation of human EEG based on a spatially distributed stochastic wave equation.

    Science.gov (United States)

    Galka, Andreas; Ozaki, Tohru; Muhle, Hiltrud; Stephani, Ulrich; Siniatchkin, Michael

    2008-06-01

    We discuss a model for the dynamics of the primary current density vector field within the grey matter of human brain. The model is based on a linear damped wave equation, driven by a stochastic term. By employing a realistically shaped average brain model and an estimate of the matrix which maps the primary currents distributed over grey matter to the electric potentials at the surface of the head, the model can be put into relation with recordings of the electroencephalogram (EEG). Through this step it becomes possible to employ EEG recordings for the purpose of estimating the primary current density vector field, i.e. finding a solution of the inverse problem of EEG generation. As a technique for inferring the unobserved high-dimensional primary current density field from EEG data of much lower dimension, a linear state space modelling approach is suggested, based on a generalisation of Kalman filtering, in combination with maximum-likelihood parameter estimation. The resulting algorithm for estimating dynamical solutions of the EEG inverse problem is applied to the task of localising the source of an epileptic spike from a clinical EEG data set; for comparison, we apply to the same task also a non-dynamical standard algorithm.

  20. Acquistion of High Resolution Electroencephalogram Systems for Advancing Brain-Machine Interaction Research

    Science.gov (United States)

    2015-12-21

    SECURITY CLASSIFICATION OF: The goal of this project is to acquire high performance Electroencephalogram (EEG) systems that enable real-time...measurement of human brain activities at high spatial-temporal resolution in both laboratory and real-life environments. Our long-term vision is to develop...state-of-the-art Biosemi Active Two EEG device to enable real-time measurement of brain 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13

  1. Understanding Intentions of Others Reflects Evoked Responses in the Human Mirror Neuron System: Evidence From Combined fMRI and EEG Repetition Suppression

    Science.gov (United States)

    2008-12-01

    VEPs ) and fMRI experiment in healthy human individuals while they were performing an intention inference task embedded in a one-back repetition... VEP data acquisition Continuous electroencephalogram (EEG) was recorded from 128 AgCl carbon-fiber coated electrodes using an Electric Geodesic...in addition to an automated threshold rejection criterion of 100µV. After off-line artifact rejections, VEPs were computed covering 500 ms after the

  2. Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

    Science.gov (United States)

    Lin, Yuan-Pin; Wang, Yijun; Jung, Tzyy-Ping

    2014-08-09

    Bridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking. This study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment. Despite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s). SSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications.

  3. Household wireless electroencephalogram hat

    Science.gov (United States)

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

    2012-06-01

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

  4. [On using EEG autointerval-grams for analysing reactions to periodical light stimulation].

    Science.gov (United States)

    Solomatov, V F; Sviatogor, I A; Shuvaev, V T

    2010-01-01

    We have analyzed autointerval-grams of the extremums of human electroencephalograms, registered during periodical light stimulation. We have compared them with the visual analysis of EEG and EEG's testing by sinusoids. We have shown that in most cases building interval-grams is as good or even better than two other methods, as it allows us to discover reactions and obtain additional information.

  5. EEG

    Science.gov (United States)

    ... when you are awake, and slower in certain stages of sleep. There are also normal patterns to these waves. ... An EEG test is very safe. The flashing lights or fast breathing ( hyperventilation ) required during the test ...

  6. Proposal for electroencephalogram standardization in aircrew selection.

    Science.gov (United States)

    Velis, Demetrios N

    2005-02-01

    Current diagnostic electroencephalogram (EEG) investigations in aircrew selection and certification lack both standardization and reference to universally applicable criteria for their effective use. Extrapolation from clinical EEG studies may not be appropriate. Recent studies on serial EEGs in aircrew are lacking, whereas follow-up of individuals who failed certification is nonexistent. Population-based EEG studies in healthy subjects are generally underpowered to establish the significance of pathological EEG findings. Advanced digital video/EEG recording, in combination with standardization of data exchange formats and automated detection of pathological grapho-elements, is cost effective when carried out for extended periods, e.g., during flight simulator sessions. Extensive databases of serial video/EEG records in aircrew may thus be easily obtained and validated over time. Prognostic inferences on the significance of pathological EEG discharges may subsequently be derived from these databases.

  7. EEG PHASE RESET OF THE DEFAULT MODE NETWORK

    OpenAIRE

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

    2014-01-01

    Objectives: The purpose of this study was to explore phase reset of 3-dimensional current sources located in Brodmann areas located in the human default mode network (DMN) using Low Resolution Electromagnetic Tomography (LORETA) of the human electroencephalogram (EEG). Methods: The EEG was recorded from 19 scalp locations from 70 healthy normal subjects ranging in age from 13 to 20 years. A time point by time point computation of LORETA current sources were computed for 14 Brodman areas c...

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

    Directory of Open Access Journals (Sweden)

    Ludger eTebartz Van Elst

    2016-02-01

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

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

    OpenAIRE

    Lepage, Kyle Q.; Kramer, Mark Nathan; Chu, Catherine Jean

    2014-01-01

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

  10. The Clinical Significance of Transcranial Doppler(TCD)and Electroencephalogram(EEG)in Patients With Vertigo%眩晕患者经颅多普勒及脑电图的临床意义

    Institute of Scientific and Technical Information of China (English)

    张立霞

    2015-01-01

    目的:探讨眩晕患者经颅多普勒及脑电图的临床意义。方法随机抽取100例经颅内普勒及脑电图检测的眩晕患者的有关资料,并进行相应的分析。结果100例眩晕患者经过经颅多普勒检测显示有72例表现异常,约占总人数的72%,其主要表现为患者活动泛化与增强;另有28例患者经过脑电图检测显示异常,约占总人数的28%,主要具有所检查血管内血液流速增快、减慢,两侧不对称,频谱异常等特征。结论通过对眩晕患者进行经颅多普勒和脑电图的检测后,能有效发现其身体内异常现象,增加对脑部眩晕患者具体病症原因的诊断确定性,具有非常重要的临床意义。%Objective To evaluate the clinical significance on patients with dizziness do transcranial doppler(TCD)and electroencephalogram (eeg)examination. MethodsRandomly selected 100 cases of intracranial doppler and eeg detection in patients with vertigo of relevant information,and the corresponding analysis. ResultsA total of 100 patients with vertigo by transcranial doppler(TCD)detection shows that there were 72 cases of abnormal performance,accounting for about 72% of the total,the main performance for activity in patients with generalization and enhancement. Another 28 cases patients after tests showed abnormal,accounts for about 28% of the total,were the main check by endovascular blood velocity increase fast,slow,on both sides of the asymmetry,spectrum anomaly characteristics.Conclusion Through the vertigo patients with transcranial doppler(TCD)and electroencephalogram(eeg)after test, found its body anomalies effectively,increase the diagnosis of patients with dizziness brain specific disease cause,has very important clinical significance.

  11. Neural decoding of expressive human movement from scalp electroencephalography (EEG

    Directory of Open Access Journals (Sweden)

    Zachery Ryan Hernandez

    2014-04-01

    Full Text Available Although efforts to characterize human movement through EEG have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA dancers. Each dancer performed whole body functional movements of three Action types: movements devoid of expressive qualities ('Neutral', non-expressive movements while thinking about specific expressive qualities ('Think’, and enacted expressive movements ('Do'. The expressive movement qualities that were used in the 'Think' and 'Do' actions consisted of a sequence of eight Laban Efforts as defined by LMA - a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2 – 4 Hz EEG as input to a machine learning algorithm that computed locality-preserving Fisher’s discriminant analysis (LFDA for dimensionality reduction followed by Gaussian mixture models (GMMs to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort (giving a total of 17 classes. Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore movements.

  12. Neural decoding of expressive human movement from scalp electroencephalography (EEG)

    Science.gov (United States)

    Cruz-Garza, Jesus G.; Hernandez, Zachery R.; Nepaul, Sargoon; Bradley, Karen K.; Contreras-Vidal, Jose L.

    2014-01-01

    Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities (“Neutral”), non-expressive movements while thinking about specific expressive qualities (“Think”), and enacted expressive movements (“Do”). The expressive movement qualities that were used in the “Think” and “Do” actions consisted of a sequence of eight Laban Effort qualities as defined by LMA—a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2–4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of

  13. Neural decoding of expressive human movement from scalp electroencephalography (EEG).

    Science.gov (United States)

    Cruz-Garza, Jesus G; Hernandez, Zachery R; Nepaul, Sargoon; Bradley, Karen K; Contreras-Vidal, Jose L

    2014-01-01

    Although efforts to characterize human movement through electroencephalography (EEG) have revealed neural activities unique to limb control that can be used to infer movement kinematics, it is still unknown the extent to which EEG can be used to discern the expressive qualities that influence such movements. In this study we used EEG and inertial sensors to record brain activity and movement of five skilled and certified Laban Movement Analysis (LMA) dancers. Each dancer performed whole body movements of three Action types: movements devoid of expressive qualities ("Neutral"), non-expressive movements while thinking about specific expressive qualities ("Think"), and enacted expressive movements ("Do"). The expressive movement qualities that were used in the "Think" and "Do" actions consisted of a sequence of eight Laban Effort qualities as defined by LMA-a notation system and language for describing, visualizing, interpreting and documenting all varieties of human movement. We used delta band (0.2-4 Hz) EEG as input to a machine learning algorithm that computed locality-preserving Fisher's discriminant analysis (LFDA) for dimensionality reduction followed by Gaussian mixture models (GMMs) to decode the type of Action. We also trained our LFDA-GMM models to classify all the possible combinations of Action Type and Laban Effort quality (giving a total of 17 classes). Classification accuracy rates were 59.4 ± 0.6% for Action Type and 88.2 ± 0.7% for Laban Effort quality Type. Ancillary analyses of the potential relations between the EEG and movement kinematics of the dancer's body, indicated that motion-related artifacts did not significantly influence our classification results. In summary, this research demonstrates that EEG has valuable information about the expressive qualities of movement. These results may have applications for advancing the understanding of the neural basis of expressive movements and for the development of neuroprosthetics to restore

  14. Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Al-Kadi

    2013-05-01

    Full Text Available Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.

  15. Evolution of electroencephalogram signal analysis techniques during anesthesia.

    Science.gov (United States)

    Al-Kadi, Mahmoud I; Reaz, Mamun Bin Ibne; Ali, Mohd Alauddin Mohd

    2013-05-17

    Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device.

  16. Evolution of Electroencephalogram Signal Analysis Techniques during Anesthesia

    Science.gov (United States)

    Al-Kadi, Mahmoud I.; Reaz, Mamun Bin Ibne; Ali, Mohd Alauddin Mohd

    2013-01-01

    Biosignal analysis is one of the most important topics that researchers have tried to develop during the last century to understand numerous human diseases. Electroencephalograms (EEGs) are one of the techniques which provides an electrical representation of biosignals that reflect changes in the activity of the human brain. Monitoring the levels of anesthesia is a very important subject, which has been proposed to avoid both patient awareness caused by inadequate dosage of anesthetic drugs and excessive use of anesthesia during surgery. This article reviews the bases of these techniques and their development within the last decades and provides a synopsis of the relevant methodologies and algorithms that are used to analyze EEG signals. In addition, it aims to present some of the physiological background of the EEG signal, developments in EEG signal processing, and the effective methods used to remove various types of noise. This review will hopefully increase efforts to develop methods that use EEG signals for determining and classifying the depth of anesthesia with a high data rate to produce a flexible and reliable detection device. PMID:23686141

  17. Phase space and power spectral approaches for EEG-based automatic sleep-wake classification in humans: a comparative study using short and standard epoch lengths.

    Science.gov (United States)

    Brignol, Arnaud; Al-Ani, Tarik; Drouot, Xavier

    2013-03-01

    Sleep disorders in humans have become a public health issue in recent years. Sleep can be analysed by studying the electroencephalogram (EEG) recorded during a night's sleep. Alternating between sleep-wake stages gives information related to the sleep quality and quantity since this alternating pattern is highly affected during sleep disorders. Spectral composition of EEG signals varies according to sleep stages, alternating phases of high energy associated to low frequency (deep sleep) with periods of low energy associated to high frequency (wake and light sleep). The analysis of sleep in humans is usually made on periods (epochs) of 30-s length according to the original Rechtschaffen and Kales sleep scoring manual. In this work, we propose a new phase space-based (mainly based on Poincaré plot) algorithm for automatic classification of sleep-wake states in humans using EEG data gathered over relatively short-time periods. The effectiveness of our approach is demonstrated through a series of experiments involving EEG data from seven healthy adult female subjects and was tested on epoch lengths ranging from 3-s to 30-s. The performance of our phase space approach was compared to a 2-dimensional state space approach using the power spectral (PS) in two selected human-specific frequency bands. These powers were calculated by dividing integrated spectral amplitudes at selected human-specific frequency bands. The comparison demonstrated that the phase space approach gives better performance in the case of short as well as standard 30-s epoch lengths.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2013-12-15

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

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

    Directory of Open Access Journals (Sweden)

    Koch Klaus

    2011-08-01

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

  20. Chaos and Fractal Analysis of Electroencephalogram Signals during Different Imaginary Motor Movement Tasks

    Science.gov (United States)

    Soe, Ni Ni; Nakagawa, Masahiro

    2008-04-01

    This paper presents the novel approach to evaluate the effects of different motor activation tasks of the human electroencephalogram (EEG). The applications of chaos and fractal properties that are the most important tools in nonlinear analysis are been presented for four tasks of EEG during the real and imaginary motor movement. Three subjects, aged 23-30 years, participated in the experiment. Correlation dimension (D2), Lyapunov spectrum (λi), and Lyapunov dimension (DL) are been estimated to characterize the movement related EEG signals. Experimental results show that these nonlinear measures are good discriminators of EEG signals. There are significant differences in all conditions of subjective task. The fractal dimension appeared to be higher in movement conditions compared to the baseline condition. It is concluded that chaos and fractal analysis could be powerful methods in investigating brain activities during motor movements.

  1. Smartphone Household Wireless Electroencephalogram Hat

    Directory of Open Access Journals (Sweden)

    Harold Szu

    2013-01-01

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

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

    DEFF Research Database (Denmark)

    Guérit, J-M; Amantini, A; Amodio, P

    2009-01-01

    contribution to all other experts. A complete consensus has been reached when submitting the manuscript. RESULTS: What the group considered as the best classification systems for EEG and EP abnormalities in the ICU is first presented. CN tests are useful for diagnosis (epilepsy, brain death, and neuromuscular...... disorders), prognosis (anoxic ischemic encephalopathy, head trauma, and neurologic disturbances of metabolic and toxic origin), and follow-up, in the adult, paediatric, and neonatal ICU. Regarding prognosis, a clear distinction is made between these tests whose abnormalities are indicative of an ominous...

  3. 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.

  4. Effects Of Mobile Phones Radiation On The EEG And EMG Of Human Users

    Directory of Open Access Journals (Sweden)

    Fathy El-Komey

    2005-09-01

    Full Text Available This study focuses on the effect of mobile phone radiation emissions on the human electroencephalograph (EEG and electromyogram activity (EMG. EEG and EMG recordings from 50 (male and female awake subjects were taken during exposure to radiation emissions from a mobile phone. Our results demonstrated that stimulation effects became apparent on EEG at first, and changes varied strongly at the end of the experiment to depression. EEG and EMG showed interesting changes.The results suggested that cellular phones may reversibly influence the human brain, as their use induced abnormal slow waves in EEG of awake persons.

  5. 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.

  6. Spectral Asymmetry and Higuchi's Fractal Dimension Measures of Depression Electroencephalogram

    OpenAIRE

    Maie Bachmann; Jaanus Lass; Anna Suhhova; Hiie Hinrikus

    2013-01-01

    This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control...

  7. Persistence of cortical sensory processing during absence seizures in human and an animal model: evidence from EEG and intracellular recordings.

    Directory of Open Access Journals (Sweden)

    Mathilde Chipaux

    Full Text Available Absence seizures are caused by brief periods of abnormal synchronized oscillations in the thalamocortical loops, resulting in widespread spike-and-wave discharges (SWDs in the electroencephalogram (EEG. SWDs are concomitant with a complete or partial impairment of consciousness, notably expressed by an interruption of ongoing behaviour together with a lack of conscious perception of external stimuli. It is largely considered that the paroxysmal synchronizations during the epileptic episode transiently render the thalamocortical system incapable of transmitting primary sensory information to the cortex. Here, we examined in young patients and in the Genetic Absence Epilepsy Rats from Strasbourg (GAERS, a well-established genetic model of absence epilepsy, how sensory inputs are processed in the related cortical areas during SWDs. In epileptic patients, visual event-related potentials (ERPs were still present in the occipital EEG when the stimuli were delivered during seizures, with a significant increase in amplitude compared to interictal periods and a decrease in latency compared to that measured from non-epileptic subjects. Using simultaneous in vivo EEG and intracellular recordings from the primary somatosensory cortex of GAERS and non-epileptic rats, we found that ERPs and firing responses of related pyramidal neurons to whisker deflection were not significantly modified during SWDs. However, the intracellular subthreshold synaptic responses in somatosensory cortical neurons during seizures had larger amplitude compared to quiescent situations. These convergent findings from human patients and a rodent genetic model show the persistence of cortical responses to sensory stimulations during SWDs, indicating that the brain can still process external stimuli during absence seizures. They also demonstrate that the disruption of conscious perception during absences is not due to an obliteration of information transfer in the thalamocortical system

  8. Identification of epileptiform patterns in electroencephalogram

    Science.gov (United States)

    Popov, Anton O.; Fesechko, Volodymyr O.; Kanaykin, Alexey M.

    2006-03-01

    The paper concerns the analysis of electroencephalogram (EEG) signals with the purpose of revealing particular waveforms in the signal - the epileptiform oscillation's complexes, which are of great importance in epileptology. We consider the pattern recognition as equivalent to EEG visual analysis and advance the template matching approach to the identification of the complexes in the signal. New type of epileptiform oscillation's complexes' template consists of generic complex and parameters of its deformations. The methodology of constructing these templates is proposed. Direct applicabilty of the proposed template creating methodology to adaptive classification of EEG complexes is highlighted. The performance of the template is evaluated with simulated and real EEG data. Experimental application of the template resulted in correct identification of 62 of 81 epileptiform oscillation's complexes from the sample signals with moderated number of false positive identifications.

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

    Science.gov (United States)

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

    2016-03-01

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

  10. Interobserver reliability of visual interpretation of electroencephalograms in children with newly diagnosed seizures

    NARCIS (Netherlands)

    Stroink, H; Schimsheimer, RJ; de Weerd, AW; Geerts, AT; Arts, WF; Peeters, EA; Brouwer, OF; van Donselaar, CA

    2006-01-01

    The reliability of visual interpretation of electroencephalograms (EEG) is of great importance in assessing the value of this diagnostic tool. We prospectively obtained 50 standard EEGs and 61 EEGs after partial sleep deprivation from 93 children (56 males, 37 females) with a mean age of 6 years 10

  11. Electroencephalogram and Heart Rate Measures of Working Memory at 5 and 10 Months of Age

    Science.gov (United States)

    Cuevas, Kimberly; Bell, Martha Ann; Marcovitch, Stuart; Calkins, Susan D.

    2012-01-01

    We recorded electroencephalogram (EEG; 6-9 Hz) and heart rate (HR) from infants at 5 and 10 months of age during baseline and performance on the looking A-not-B task of infant working memory (WM). Longitudinal baseline-to-task comparisons revealed WM-related increases in EEG power (all electrodes) and EEG coherence (medial frontal-occipital…

  12. The effects of sleep deprivation in humans: topographical electroencephalogram changes in non-rapid eye movement (NREM) sleep versus REM sleep.

    Science.gov (United States)

    Marzano, Cristina; Ferrara, Michele; Curcio, Giuseppe; De Gennaro, Luigi

    2010-06-01

    Studies on homeostatic aspects of sleep regulation have been focussed upon non-rapid eye movement (NREM) sleep, and direct comparisons with regional changes in rapid eye movement (REM) sleep are sparse. To this end, evaluation of electroencephalogram (EEG) changes in recovery sleep after extended waking is the classical approach for increasing homeostatic need. Here, we studied a large sample of 40 healthy subjects, considering a full-scalp EEG topography during baseline (BSL) and recovery sleep following 40 h of wakefulness (REC). In NREM sleep, the statistical maps of REC versus BSL differences revealed significant fronto-central increases of power from 0.5 to 11 Hz and decreases from 13 to 15 Hz. In REM sleep, REC versus BSL differences pointed to significant fronto-central increases in the 0.5-7 Hz and decreases in the 8-11 Hz bands. Moreover, the 12-15 Hz band showed a fronto-parietal increase and that at 22-24 Hz exhibited a fronto-central decrease. Hence, the 1-7 Hz range showed significant increases in both NREM sleep and REM sleep, with similar topography. The parallel change of NREM sleep and REM sleep EEG power is related, as confirmed by a correlational analysis, indicating that the increase in frequency of 2-7 Hz possibly subtends a state-aspecific homeostatic response. On the contrary, sleep deprivation has opposite effects on alpha and sigma activity in both states. In particular, this analysis points to the presence of state-specific homeostatic mechanisms for NREM sleep, limited to REM sleep and NREM sleep seem to share some homeostatic mechanisms in response to sleep deprivation, as indicated mainly by the similar direction and topography of changes in low-frequency activity.

  13. Quantitative analysis of spatial sampling error in the infant and adult electroencephalogram.

    Science.gov (United States)

    Grieve, Philip G; Emerson, Ronald G; Isler, Joseph R; Stark, Raymond I

    2004-04-01

    The purpose of this report was to determine the required number of electrodes to record the infant and adult electroencephalogram (EEG) with a specified amount of spatial sampling error. We first developed mathematical theory that governs the spatial sampling of EEG data distributed on a spherical approximation to the scalp. We then used a concentric sphere model of current flow in the head to simulate realistic EEG data. Quantitative spatial sampling error was calculated for the simulated EEG, with additive measurement noise, for 64, 128, and 256 electrodes equally spaced over the surface of the sphere corresponding to the coverage of the human scalp by commercially available "geodesic" electrode arrays. We found the sampling error for the infant to be larger than that for the adult. For example, a sampling error of less than 10% for the adult was obtained with a 64-electrode array but a 256-electrode array was needed for the infant to achieve the same level of error. With the addition of measurement noise, with power 10 times less than that of the EEG, the sampling error increased to 25% for both the infant and adult, for these numbers of electrodes. These results show that accurate measurement of the spatial properties of the infant EEG requires more electrodes than for the adult.

  14. Extraction of SSVEP signals of a capacitive EEG helmet for human machine interface.

    Science.gov (United States)

    Oehler, Martin; Neumann, Peter; Becker, Matthias; Curio, Gabriel; Schilling, Meinhard

    2008-01-01

    The use of capacitive electrodes for measuring EEG eliminates the preparation procedure known from classical noninvasive EEG measurements. The insulated interface to the brain signals in combination with steady-state visual evoked potentials (SSVEP) enables a zero prep human machine interface triggered by brain signals. This paper presents a 28-channel EEG helmet system based on our capacitive electrodes measuring and analyzing SSVEPs even through scalp hair. Correlation analysis is employed to extract the stimulation frequency of the EEG signal. The system is characterized corresponding to the available detection time with different subjects. As demonstration of the use of capacitive electrodes for SSVEP measurements, preliminary online Brain-Computer Interface (BCI) results of the system are presented. Detection times lie about a factor of 3 higher than in galvanic EEG SSVEP measurements, but are low enough to establish a proper communication channel for Human Machine Interface (HMI).

  15. On the feasibility of concurrent human TMS-EEG-fMRI measurements.

    Science.gov (United States)

    Peters, Judith C; Reithler, Joel; Schuhmann, Teresa; de Graaf, Tom; Uludag, Kâmil; Goebel, Rainer; Sack, Alexander T

    2013-02-01

    Simultaneously combining the complementary assets of EEG, functional MRI (fMRI), and transcranial magnetic stimulation (TMS) within one experimental session provides synergetic results, offering insights into brain function that go beyond the scope of each method when used in isolation. The steady increase of concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI studies further underlines the added value of such multimodal imaging approaches. Whereas concurrent EEG-fMRI enables monitoring of brain-wide network dynamics with high temporal and spatial resolution, the combination with TMS provides insights in causal interactions within these networks. Thus the simultaneous use of all three methods would allow studying fast, spatially accurate, and distributed causal interactions in the perturbed system and its functional relevance for intact behavior. Concurrent EEG-fMRI, TMS-EEG, and TMS-fMRI experiments are already technically challenging, and the three-way combination of TMS-EEG-fMRI might yield additional difficulties in terms of hardware strain or signal quality. The present study explored the feasibility of concurrent TMS-EEG-fMRI studies by performing safety and quality assurance tests based on phantom and human data combining existing commercially available hardware. Results revealed that combined TMS-EEG-fMRI measurements were technically feasible, safe in terms of induced temperature changes, allowed functional MRI acquisition with comparable image quality as during concurrent EEG-fMRI or TMS-fMRI, and provided artifact-free EEG before and from 300 ms after TMS pulse application. Based on these empirical findings, we discuss the conceptual benefits of this novel complementary approach to investigate the working human brain and list a number of precautions and caveats to be heeded when setting up such multimodal imaging facilities with current hardware.

  16. Enhanced dynamic complexity in the human EEG during creative thinking.

    Science.gov (United States)

    Mölle, M; Marshall, L; Lutzenberger, W; Pietrowsky, R; Fehm, H L; Born, J

    1996-04-12

    This study shows that divergent thinking, considered the general process underlying creative production, can be distinguished from convergent, analytical thought based on the dimensional complexity of ongoing electroencephalographic (EEG) activity. EEG complexity over the central and posterior cortex was higher while subjects solved tasks of divergent than convergent thinking, and also higher than during mental relaxation. Over the frontal cortex, EEG complexity was comparable during divergent thinking and mental relaxation, but reduced during convergent thinking. Results indicate that the basic process underlying the generation of novel ideas expresses itself in a strong increase in the EEG's complexity, reflecting higher degrees of freedom in the competitive interactions among cortical neuron assemblies. Frontocortical EEG complexity being comparable with that during mental relaxation, speaks for a loosened attentional control during creative thinking.

  17. EEG Studies with Young Children.

    Science.gov (United States)

    Flohr, John W.; Miller, Daniel C.; deBeus, Roger

    2000-01-01

    Describes how electroencephalogram (EEG) data are collected and how brain function is measured. Discusses studies on the effects of music experiences with adult subjects and studies focusing on the effects of music training on EEG activity of children and adolescents. Considers the implications of the studies and the future directions of this…

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

    Science.gov (United States)

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

    2016-01-01

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

  19. Interleaving distribution of multifractal strength of 16-channel EEG signals

    Institute of Scientific and Technical Information of China (English)

    WANG Wei; NING Xinbao; WANG Jun; ZHANG Sheng; CHEN Jie; LI Lejia

    2003-01-01

    Multifractal characteristics of 16-channel human electroencephalogram (EEG) signals under eye-closed rest are analyzed for the first time. The result shows that the EEGs from the different sites on the scalp have different multifractal characteristics and the multifractal strength value Δα exhibits a kind of interleaving and left-right opposite distribution on scalp. This distribution rule is consistent with the localization of function and the lateralization theory in physiology. SoΔα can become an effective parameter to describe the brain potential character. And such a Δα stable distribution rule on sites of the scalp means a classic cerebral cortex active state.

  20. Fractal EEG analysis with Higuchi's algorithm of low-frequency noise exposition on humans

    Science.gov (United States)

    Panuszka, Ryszard; Damijan, Zbigniew; Kasprzak, Cezary

    2004-05-01

    Authors used methods based on fractal analysis of EEG signal to assess the influence of low-frequency sound field on the human brain electro-potentials. The relations between LFN (low-frequency noise) and change in fractal dimension EEG signal were measured with stimulations tones. Three types of LFN stimuli were presented; each specified dominant frequency and sound-pressure levels (7 Hz at 120 dB, 18 Hz at 120 dB, and 40 Hz at 110 dB). Standard EEG signal was recorded before, during, and after subject's exposure for 35 min. LFN. Applied to the analysis fractal dimension of EEG-signal Higuchis algorithm. Experiments show LFN influence on complexity of EEG-signal with calculated Higuchi's algorithm. Observed increase of mean value of Higuchi's fractal dimension during exposition to LFN.

  1. Technical standards for digital electroencephalogram recording in epilepsy practice

    Directory of Open Access Journals (Sweden)

    Nayak Dinesh

    2007-01-01

    Full Text Available With the advent of digital technology in the recording of the electroencephalogram (EEG in the last decade, analogue paper-EEG machines have all but disappeared. While there are several advantages of digital EEG over its analog counterpart, like being paperless and therefore easy to store and the ability to change montages and filter settings during review, there is wide disparity in the standards of EEG recording, display and reporting in laboratories across the country. Colorful brain maps conveying little meaning are usually appended to reports. This article reviews the minimum standards that must be observed for recording digital EEG as recommended by the International Federation of Clinical Neurophysiology (IFCN and illustrates the importance of use of appropriate derivations, montages, filters and gains during recording and review of digital EEG in the context of evaluation of patients with suspected epilepsy.

  2. Long-range temporal correlations in the EEG bursts of human preterm babies.

    Directory of Open Access Journals (Sweden)

    Caroline Hartley

    Full Text Available The electrical activity in the very early human preterm brain, as recorded by scalp EEG, is mostly discontinuous and has bursts of high-frequency oscillatory activity nested within slow-wave depolarisations of high amplitude. The temporal organisation of the occurrence of these EEG bursts has not been previously investigated. We analysed the distribution of the EEG bursts in 11 very preterm (23-30 weeks gestational age human babies through two estimates of the Hurst exponent. We found long-range temporal correlations (LRTCs in the occurrence of these EEG bursts demonstrating that even in the very immature human brain, when the cerebral cortical structure is far from fully developed, there is non-trivial temporal structuring of electrical activity.

  3. Non-auditory Effect of Noise Pollution and Its Risk on Human Brain Activity in Different Audio Frequency Using Electroencephalogram Complexity.

    Science.gov (United States)

    Allahverdy, Armin; Jafari, Amir Homayoun

    2016-10-01

    Noise pollution is one of the most harmful ambiance disturbances. It may cause many deficits in ability and activity of persons in the urban and industrial areas. It also may cause many kinds of psychopathies. Therefore, it is very important to measure the risk of this pollution in different area. This study was conducted in the Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences from June to September of 2015, in which, different frequencies of noise pollution were played for volunteers. 16-channel EEG signal was recorded synchronously, then by using fractal dimension and relative power of Beta sub-band of EEG, the complexity of EEG signals was measured. As the results, it is observed that the average complexity of brain activity is increased in the middle of audio frequency range and the complexity map of brain activity changes in different frequencies, which can show the effects of frequency changes on human brain activity. The complexity of EEG is a good measure for ranking the annoyance and non-auditory risk of noise pollution on human brain activity.

  4. Normalization of visual evoked potentials using underlying electroencephalogram levels improves amplitude reproducibility in rats.

    Science.gov (United States)

    You, Yuyi; Thie, Johnson; Klistorner, Alexander; Gupta, Vivek K; Graham, Stuart L

    2012-03-15

    The visual evoked potential (VEP) is a frequently used noninvasive measurement of visual function. However, high-amplitude variability has limited its potential for evaluating axonal damage in both laboratory and clinical research. This study was conducted to improve the reliability of VEP amplitude measurement in rats by using electroencephalogram (EEG)-based signal correction. VEPs of Sprague-Dawley rats were recorded on three separate days within 2 weeks. The original VEP traces were normalized by EEG power spectrum, which was evaluated by Fourier transform. A comparison of intersession reproducibility and intersubject variability was made between the original and corrected signals. Corrected VEPs showed lower amplitude intersession within-subject SD (Sw), coefficient of variation (CoV), and repeatability (R(95)) than the original signals (P < 0.001). The intraclass correlation coefficient (ICC) of the corrected traces (0.90) was also better than the original potentials (0.82). For intersubject variability, the EEG-based normalization improved the CoV from 44.64% to 30.26%. A linear correlation was observed between the EEG level and the VEP amplitude (r = 0.71, P < 0.0001). Underlying EEG signals should be considered in measuring the VEP amplitude. In this study, a useful technique was developed for VEP data processing that could also be used for other cortical evoked potential recordings and for clinical VEP interpretation in humans.

  5. A Review on the Computational Methods for Emotional State Estimation from the Human EEG

    Science.gov (United States)

    Kim, Min-Ki; Kim, Miyoung; Oh, Eunmi

    2013-01-01

    A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.   PMID:23634176

  6. Wavelet Variance Analysis of EEG Based on Window Function

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yuan-zhuang; YOU Rong-yi

    2014-01-01

    A new wavelet variance analysis method based on window function is proposed to investigate the dynamical features of electroencephalogram (EEG).The ex-prienmental results show that the wavelet energy of epileptic EEGs are more discrete than normal EEGs, and the variation of wavelet variance is different between epileptic and normal EEGs with the increase of time-window width. Furthermore, it is found that the wavelet subband entropy (WSE) of the epileptic EEGs are lower than the normal EEGs.

  7. Using Invariant Translation to Denoise Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Janett Walters-Williams

    2011-01-01

    Full Text Available Problem statement: Because of the distance between the skull and the brain and their different resistivitys, Electroencephalogram (EEG recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdiagnosis, as such it is necessary to remove them before further analysis and processing can be done. Denoising is often done with Independent Component Analysis algorithms but of late Wavelet Transform has been utilized. Approach: In this study we utilized one of the newer Wavelet Transform methods, Translation-Invariant, to deny EEG signals. Different EEG signals were used to verify the method using the MATLAB software. Results were then compared with those of renowned ICA algorithms Fast ICA and Radical and evaluated using the performance measures Mean Square Error (MSE, Percentage Root Mean Square Difference (PRD and Signal to Noise Ratio (SNR. Results: Experiments revealed that Translation-Invariant Wavelet Transform had the smallest MSE and PRD while having the largest SNR. Conclusion/Recommendations: This indicated that it performed superior to the ICA algorithms producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain.

  8. EEG-fMRI integration for the study of human brain function.

    Science.gov (United States)

    Jorge, João; van der Zwaag, Wietske; Figueiredo, Patrícia

    2014-11-15

    Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have proved to be extremely valuable tools for the non-invasive study of human brain function. Moreover, due to a notable degree of complementarity between the two modalities, the combination of EEG and fMRI data has been actively sought in the last two decades. Although initially focused on epilepsy, EEG-fMRI applications were rapidly extended to the study of healthy brain function, yielding new insights into its underlying mechanisms and pathways. Nevertheless, EEG and fMRI have markedly different spatial and temporal resolutions, and probe neuronal activity through distinct biophysical processes, many aspects of which are still poorly understood. The remarkable conceptual and methodological challenges associated with EEG-fMRI integration have motivated the development of a wide range of analysis approaches over the years, each relying on more or less restrictive assumptions, and aiming to shed further light on the mechanisms of brain function along with those of the EEG-fMRI coupling itself. Here, we present a review of the most relevant EEG-fMRI integration approaches yet proposed for the study of brain function, supported by a general overview of our current understanding of the biophysical mechanisms coupling the signals obtained from the two modalities.

  9. Effect of Hypothermia on Amplitude-Integrated Electroencephalogram in Infants With Asphyxia

    NARCIS (Netherlands)

    Thoresen, Marianne; Hellstrom-Westas, Lena; Liu, Xun; de Vries, Linda S.

    2010-01-01

    OBJECTIVES: Amplitude-integrated electroencephalogram (aEEG) at METHODS: Seventy-four infants were recruited by using the CoolCap entry criteria, and their outcomes were assessed by using the Bayley Scales of Infant Development II at 18 months. The aEEG was recorded for 72 hours. Patterns and voltag

  10. Technical standards for recording and interpretation of neonatal electroencephalogram in clinical practice

    NARCIS (Netherlands)

    P.J. Cherian (Joseph); R.M.C. Swarte (Renate); G.H. Visser (Gerhard Henk)

    2009-01-01

    textabstractNeonatal electroencephalogram (EEG), though often perceived as being difficult to record and interpret, is relatively easy to study due to the immature nature of the brain, which expresses only a few well-defined set of patterns. The EEG interpreter needs to be aware of the maturational

  11. Effects of sleep deprivation on gamma oscillation of waking human EEG

    Institute of Scientific and Technical Information of China (English)

    Ning Li; Yan Wang; Mingshi Wang; Haiying Liu

    2008-01-01

    This study compares and evaluates the effect of sleep deprivation (SD) on human brain cognition by analyzing the recorded EEG data under normal and 24 h sleep deprived states.EEG auditory event-related potentials were collected from 14 healthy volunteers,and the statistical values of wavelet-transformed EEG in gamma band were decomposed by parallel factor analysis (PARAFAC) to identify where the differences appeared in the time,frequency and spatial domains.The results showed that the changes of brain states caused by SD appeared around 40 Hz at about 400 ms after stimulation on prefrontal and frontal lobes.Negative effects of SD on neuronal activity and oscillation were observed,The analysis of the EEG data by the wavelet transform and PARAFAC can be an integrated way to estimate the change of brain states in the three domains.

  12. Trend figures assist with untrained emergency electroencephalogram interpretation.

    Science.gov (United States)

    Kobayashi, Katsuhiro; Yunoki, Kosuke; Zensho, Kazumasa; Akiyama, Tomoyuki; Oka, Makio; Yoshinaga, Harumi

    2015-05-01

    Acute electroencephalogram (EEG) findings are important for diagnosing emergency patients with suspected neurological disorders, but they can be difficult for untrained medical staff to interpret. In this research, we will develop an emergency EEG trend figure that we hypothesize will be more easily understood by untrained staff compared with the raw original traces. For each of several EEG patterns (wakefulness, sleep, seizure activity, and encephalopathy), trend figures incorporating information on both amplitude and frequency were built. The accuracy of untrained reviewers' interpretation was compared with that of the raw EEG trace interpretation. The rate of correct answers was significantly higher in response to the EEG trend figures than to the raw traces showing wakefulness, sleep, and encephalopathy, but there was no difference when seizure activity patterns were viewed. The rates of misjudging normal or abnormal findings were significantly lower with the trend figures in the wakefulness pattern; in the other patterns, misjudgments were equally low for the trend figures and the raw traces. EEG trend figures improved the accuracy with which untrained medical staff interpreted emergency EEGs. Emergency EEG figures that can be understood intuitively with minimal training might improve the accuracy of emergency EEG interpretation. However, additional studies are required to confirm these results because there may be many types of clinical EEGs that are difficult to interpret. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

  13. Computerized epileptiform transient detection in the scalp electroencephalogram: obstacles to progress and the example of computerized ECG interpretation.

    Science.gov (United States)

    Halford, Jonathan J

    2009-11-01

    Computerized detection of epileptiform transients (ETs), also called spikes and sharp waves, in the electroencephalogram (EEG) has been a research goal for the last 40years. A reliable method for detecting ETs could improve efficiency in reviewing long EEG recordings and assist physicians in interpreting routine EEGs. Computer algorithms developed so far for detecting ETs are not as reliable as human expert interpreters, mostly due to the large number of false positive detections. Typical methods for ET detection include measuring waveform morphology, detecting signal non-stationarity, and power spectrum analysis. Some progress has been made by using more advanced algorithmic approaches including wavelet analysis, artificial neural networks, and dipole analysis. Comparing the performance of different algorithms is difficult since each study uses its own EEG test dataset. In order to overcome this problem, European researchers in the field of computerized electrocardiogram interpretation organized a large multi-center research workgroup to create a standardized dataset of ECG recordings which were interpreted by a large group of cardiologists. EEG researchers need to follow this as a model and seek funding for the creation of a standardized EEG research dataset to develop ET detection algorithms and certify commercial software.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-05-18

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

  15. An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions

    Directory of Open Access Journals (Sweden)

    Gwin Joseph T

    2012-06-01

    Full Text Available Abstract Background Electroencephalography (EEG combined with independent component analysis enables functional neuroimaging in dynamic environments including during human locomotion. This type of functional neuroimaging could be a powerful tool for neurological rehabilitation. It could enable clinicians to monitor changes in motor control related cortical dynamics associated with a therapeutic intervention, and it could facilitate noninvasive electrocortical control of devices for assisting limb movement to stimulate activity dependent plasticity. Understanding the relationship between electrocortical dynamics and muscle activity will be helpful for incorporating EEG-based functional neuroimaging into clinical practice. The goal of this study was to use independent component analysis of high-density EEG to test whether we could relate electrocortical dynamics to lower limb muscle activation in a constrained motor task. A secondary goal was to assess the trial-by-trial consistency of the electrocortical dynamics by decoding the type of muscle action. Methods We recorded 264-channel EEG while 8 neurologically intact subjects performed isometric and isotonic, knee and ankle exercises at two different effort levels. Adaptive mixture independent component analysis (AMICA parsed EEG into models of underlying source signals. We generated spectrograms for all electrocortical source signals and used a naïve Bayesian classifier to decode exercise type from trial-by-trial time-frequency data. Results AMICA captured different electrocortical source distributions for ankle and knee tasks. The fit of single-trial EEG to these models distinguished knee from ankle tasks with 80% accuracy. Electrocortical spectral modulations in the supplementary motor area were significantly different for isometric and isotonic tasks (p  Conclusions Independent component analysis of EEG reveals unique spatial and spectro-temporal electrocortical properties for different

  16. Spectral asymmetry and Higuchi's fractal dimension measures of depression electroencephalogram.

    Science.gov (United States)

    Bachmann, Maie; Lass, Jaanus; Suhhova, Anna; Hinrikus, Hiie

    2013-01-01

    This study was aimed to compare two electroencephalogram (EEG) analysis methods, spectral asymmetry index (SASI) and Higuchi's fractal dimension (HFD), for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P 0.05). The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.

  17. Human memory retention and recall processes. A review of EEG and fMRI studies.

    Science.gov (United States)

    Amin, Hafeezullah; Malik, Aamir S

    2013-10-01

    Human memory is an important concept in cognitive psychology and neuroscience. Our brain is actively engaged in functions of learning and memorization. Generally, human memory has been classified into 2 groups: short-term/working memory, and long-term memory. Using different memory paradigms and brain mapping techniques, psychologists and neuroscientists have identified 3 memory processes: encoding, retention, and recall. These processes have been studied using EEG and functional MRI (fMRI) in cognitive and neuroscience research. This study reviews previous research reported for human memory processes, particularly brain behavior in memory retention and recall processes with the use of EEG and fMRI. We discuss issues and challenges related to memory research with EEG and fMRI techniques.

  18. Error-related EEG patterns during tactile human-machine interaction

    NARCIS (Netherlands)

    Lehne, M.; Ihme, K.; Brouwer, A.M.; Erp, J.B.F. van; Zander, T.O.

    2009-01-01

    Recently, the use of brain-computer interfaces (BCIs) has been extended from active control to passive detection of cognitive user states. These passive BCI systems can be especially useful for automatic error detection in human-machine systems by recording EEG potentials related to human error proc

  19. Fractal Dimension in Epileptic EEG Signal Analysis

    Science.gov (United States)

    Uthayakumar, R.

    Fractal Analysis is the well developed theory in the data analysis of non-linear time series. Especially Fractal Dimension is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity. Fractal dimension is a suitable tool for analyzing the nonlinear behaviour and state of the many chaotic systems. Particularly in analysis of chaotic time series such as electroencephalograms (EEG), this feature has been used to identify and distinguish specific states of physiological function.Epilepsy is the main fatal neurological disorder in our brain, which is analyzed by the biomedical signal called Electroencephalogram (EEG). The detection of Epileptic seizures in the EEG Signals is an important tool in the diagnosis of epilepsy. So we made an attempt to analyze the EEG in depth for knowing the mystery of human consciousness. EEG has more fluctuations recorded from the human brain due to the spontaneous electrical activity. Hence EEG Signals are represented as Fractal Time Series.The algorithms of fractal dimension methods have weak ability to the estimation of complexity in the irregular graphs. Divider method is widely used to obtain the fractal dimension of curves embedded into a 2-dimensional space. The major problem is choosing initial and final step length of dividers. We propose a new algorithm based on the size measure relationship (SMR) method, quantifying the dimensional behaviour of irregular rectifiable graphs with minimum time complexity. The evidence for the suitability (equality with the nature of dimension) of the algorithm is illustrated graphically.We would like to demonstrate the criterion for the selection of dividers (minimum and maximum value) in the calculation of fractal dimension of the irregular curves with minimum time complexity. For that we design a new method of computing fractal dimension (FD) of biomedical waveforms. Compared to Higuchi's algorithm, advantages of this method include

  20. The Combined Technique for Detection of Artifacts in Clinical Electroencephalograms of Sleeping Newborns

    OpenAIRE

    Schetinin, Vitaly; Schult, Joachim

    2005-01-01

    In this paper we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle and noise artifacts and as a consequence some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible ...

  1. 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.

  2. [Human traveling wave EEG during voluntary movement of the hand].

    Science.gov (United States)

    Belov, D R; Stepanova, P A; Kolodiazhnyĭ, S F

    2014-01-01

    The traveling wave trajectories connected with the movements of the right hand were revealed. Above sensomotor cortex 28 electrodes were set as a rectangle--4 rows with 7 electrodes in each one. 2D center-out reaching task was used. The target appeared on the screen edge through the random intervals 0.5-2.5 s equiprobably at the left, on the right, from above or from below. The task was to touch the target with the joystick-operated cursor displacing the cursor in one of the sides from the center to edge. EEG from the target occurrence till cursor contact with it was analyzed. Leading on phase of spontaneous EEG waves in the local area of the left sensomotor cortex and in the centre of back-parietal cortex during cursor movement downwards (the hand with joystick moves to oneself) comparing to rest state and movements in three other directions is revealed. The over time smoothing of data concerning phase alignment reveals hidden constant components in EEG resembling evoked potentials.

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

    Science.gov (United States)

    Michels, Lars; Lüchinger, Rafael; Koenig, Thomas; Martin, Ernst; Brandeis, Daniel

    2012-01-01

    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 synchronization and that

  4. 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

  5. Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique.

    Directory of Open Access Journals (Sweden)

    Todd Zorick

    Full Text Available Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties inherent in such signals. Similarly, fractal analysis of EEG signals has shown scaling behaviors that may not be consistent with pure monofractal processes. In this study, we hypothesized that scalp-recorded human EEG signals may be better modeled as an underlying multifractal process. We utilized the Physionet online database, a publicly available database of human EEG signals as a standardized reference database for this study. Herein, we report the use of multifractal detrended fluctuation analysis on human EEG signals derived from waking and different sleep stages, and show evidence that supports the use of multifractal methods. Next, we compare multifractal detrended fluctuation analysis to a previously published multifractal technique, wavelet transform modulus maxima, using EEG signals from waking and sleep, and demonstrate that multifractal detrended fluctuation analysis has lower indices of variability. Finally, we report a preliminary investigation into the use of multifractal detrended fluctuation analysis as a pattern classification technique on human EEG signals from waking and different sleep stages, and demonstrate its potential utility for automatic classification of different states of consciousness. Therefore, multifractal detrended fluctuation analysis may be a useful pattern classification technique to distinguish among different states of brain function.

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

    Science.gov (United States)

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

    2003-01-01

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

  7. Decoding individual finger movements from one hand using human EEG signals.

    Directory of Open Access Journals (Sweden)

    Ke Liao

    Full Text Available Brain computer interface (BCI is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG signals, while it remains unclear whether noninvasive electroencephalography (EEG signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA. These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26% in all subjects (p<0.05. The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

  8. Decoding individual finger movements from one hand using human EEG signals.

    Science.gov (United States)

    Liao, Ke; Xiao, Ran; Gonzalez, Jania; Ding, Lei

    2014-01-01

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

  9. EEG correlates of spatial orientation in the human retrosplenial complex.

    Science.gov (United States)

    Lin, C-T; Chiu, T-C; Gramann, K

    2015-10-15

    Studies on spatial navigation reliably demonstrate that the retrosplenial complex (RSC) plays a pivotal role for allocentric spatial information processing by transforming egocentric and allocentric spatial information into the respective other spatial reference frame (SRF). While more and more imaging studies investigate the role of the RSC in spatial tasks, high temporal resolution measures such as electroencephalography (EEG) are missing. To investigate the function of the RSC in spatial navigation with high temporal resolution we used EEG to analyze spectral perturbations during navigation based on allocentric and egocentric SRF. Participants performed a path integration task in a clearly structured virtual environment providing allothetic information. Continuous EEG recordings were decomposed by independent component analysis (ICA) with subsequent source reconstruction of independent time source series using equivalent dipole modeling. Time-frequency transformation was used to investigate reference frame-specific orientation processes during navigation as compared to a control condition with identical visual input but no orientation task. Our results demonstrate that navigation based on an egocentric reference frame recruited a network including the parietal, motor, and occipital cortices with dominant perturbations in the alpha band and theta modulation in frontal cortex. Allocentric navigation was accompanied by performance-related desynchronization of the 8-13 Hz frequency band and synchronization in the 12-14 Hz band in the RSC. The results support the claim that the retrosplenial complex is central to translating egocentric spatial information into allocentric reference frames. Modulations in different frequencies with different time courses in the RSC further provide first evidence of two distinct neural processes reflecting translation of spatial information based on distinct reference frames and the computation of heading changes.

  10. Demonstration of long-distance hazard-free wearable EEG monitoring system using mobile phone visible light communication.

    Science.gov (United States)

    Rachim, Vega Pradana; Jiang, Yubing; Lee, Hyeon-Seok; Chung, Wan-Young

    2017-01-23

    A wearable electroencephalogram (EEG) is a small mobile device used for long-term brain monitoring systems. Applications of these systems include fatigue monitoring, mental/emotional monitoring, and brain-computer interfaces. However, the usage of wireless wearable EEG systems is limited due to the risks posed by the wireless RF communication radiation in a long-term exposure to the human brain. A novel microwave radiation-free system was developed by integrating visible light communication technology into a wearable EEG device. In this work, we investigated the system's performance in transmitting EEG data at different illuminance level and proposed an algorithm that functions at low illuminance levels for increased transmission distance. Using a 30 Hz smartphone camera, the proposed system was able to transmit 2.4 kbps of error-free EEG data up to 4 meter, which is equal to ~300 lux using an aspheric focus lens.

  11. Rapidly Learned Identification of Epileptic Seizures from Sonified EEG

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    Psyche eLoui

    2014-10-01

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

  12. EEG beta suppression and low gamma modulation are different elements of human upright walking

    NARCIS (Netherlands)

    Seeber, M.; Scherer, R.; Wagner, J.; Solis Escalante, T.; Müller-Putz, G.R.

    2014-01-01

    Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG) recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source

  13. Analysis on Electroencephalogram(EEG) in Patients with Mental Disorder Due to Chronic Alcoholism%慢性酒精中毒性精神障碍患者脑电图分析

    Institute of Scientific and Technical Information of China (English)

    洪美娜; 吴民吉; 庄镇欣; 周佩心; 刘满芬; 李佩宜

    2013-01-01

    Objective To study the relation EEG changes and course of chronic alcoholism. Methods 48 male patients with chronic alcoholism were examined with EEG . Results 60.4%of alcohole patients were found to have abnormal EEG significantly higher than normal control group. The date had statistical meaning. The common abnomality included arrhythmia, increased slow wave. Conclusion Chronic alcoholism can cause extensive cerebral damage as manifestated by the EEG changes. The ef ect on EEG was related to the dosage and time of drinking alcohol.%目的探讨慢性酒精中毒的脑电图改变。方法对48例慢性酒精中毒性精神障碍患者进行脑电图检查。结果显示异常率为60.4%,明显高于对照组10%,差异非常显著(P﹤0.01)。饮酒时间长、饮酒量大,脑电图异常率高。脑电图主要异常可分为慢波型和失律型。结论提示慢性酒精中毒对脑功能的影响及其脑组织的损害是弥漫性的。对脑电图的影响程度与饮酒时间、饮酒量大小有关。

  14. Electroencephalogram and Alzheimer’s Disease: Clinical and Research Approaches

    Directory of Open Access Journals (Sweden)

    Anthoula Tsolaki

    2014-01-01

    Full Text Available Alzheimer’s disease (AD is a neurodegenerative disorder that is characterized by cognitive deficits, problems in activities of daily living, and behavioral disturbances. Electroencephalogram (EEG has been demonstrated as a reliable tool in dementia research and diagnosis. The application of EEG in AD has a wide range of interest. EEG contributes to the differential diagnosis and the prognosis of the disease progression. Additionally such recordings can add important information related to the drug effectiveness. This review is prepared to form a knowledge platform for the project entitled “Cognitive Signal Processing Lab,” which is in progress in Information Technology Institute in Thessaloniki. The team tried to focus on the main research fields of AD via EEG and recent published studies.

  15. Electroencephalography (EEG) and event-related potentials (ERPs) with human participants.

    Science.gov (United States)

    Light, Gregory A; Williams, Lisa E; Minow, Falk; Sprock, Joyce; Rissling, Anthony; Sharp, Richard; Swerdlow, Neal R; Braff, David L

    2010-07-01

    Understanding the basic neural processes that underlie complex higher-order cognitive operations and functional domains is a fundamental goal of cognitive neuroscience. Electroencephalography (EEG) is a non-invasive and relatively inexpensive method for assessing neurophysiological function that can be used to achieve this goal. EEG measures the electrical activity of large, synchronously firing populations of neurons in the brain with electrodes placed on the scalp. This unit outlines the basics of setting up an EEG experiment with human participants, including equipment, and a step-by-step guide to applying and preparing an electrode cap. Also included are support protocols for two event-related potential (ERP) paradigms, P50 suppression, and mismatch negativity (MMN), which are measures of early sensory processing. These paradigms can be used to assess the integrity of early sensory processing in normal individuals and clinical populations, such as individuals with schizophrenia.

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

    Directory of Open Access Journals (Sweden)

    Celina A. Reis Paula

    2017-01-01

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

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

    Science.gov (United States)

    Chaudhary, Umair J; Centeno, Maria; Thornton, Rachel C; Rodionov, Roman; Vulliemoz, Serge; McEvoy, Andrew W; Diehl, Beate; Walker, Matthew C; Duncan, John S; Carmichael, David W; Lemieux, Louis

    2016-01-01

    Accurately characterising the brain networks involved in seizure activity may have important implications for our understanding of epilepsy. Intracranial EEG-fMRI can be used to capture focal epileptic events in humans with exquisite electrophysiological sensitivity and allows for identification of brain structures involved in this phenomenon over the entire brain. We investigated ictal BOLD networks using the simultaneous intracranial EEG-fMRI (icEEG-fMRI) in a 30 year-old male undergoing invasive presurgical evaluation with bilateral depth electrode implantations in amygdalae and hippocampi for refractory temporal lobe epilepsy. One spontaneous focal electrographic seizure was recorded. The aims of the data analysis were firstly to map BOLD changes related to the ictal activity identified on icEEG and secondly to compare different fMRI modelling approaches. Visual inspection of the icEEG showed an onset dominated by beta activity involving the right amygdala and hippocampus lasting 6.4 s (ictal onset phase), followed by gamma activity bilaterally lasting 14.8 s (late ictal phase). The fMRI data was analysed using SPM8 using two modelling approaches: firstly, purely based on the visually identified phases of the seizure and secondly, based on EEG spectral dynamics quantification. For the visual approach the two ictal phases were modelled as 'ON' blocks convolved with the haemodynamic response function; in addition the BOLD changes during the 30 s preceding the onset were modelled using a flexible basis set. For the quantitative fMRI modelling approach two models were evaluated: one consisting of the variations in beta and gamma bands power, thereby adding a quantitative element to the visually-derived models, and another based on principal components analysis of the entire spectrogram in attempt to reduce the bias associated with the visual appreciation of the icEEG. BOLD changes related to the visually defined ictal onset phase were revealed in the medial and

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

    Science.gov (United States)

    Chaudhary, Umair J.; Centeno, Maria; Thornton, Rachel C.; Rodionov, Roman; Vulliemoz, Serge; McEvoy, Andrew W.; Diehl, Beate; Walker, Matthew C.; Duncan, John S.; Carmichael, David W.; Lemieux, Louis

    2016-01-01

    Accurately characterising the brain networks involved in seizure activity may have important implications for our understanding of epilepsy. Intracranial EEG-fMRI can be used to capture focal epileptic events in humans with exquisite electrophysiological sensitivity and allows for identification of brain structures involved in this phenomenon over the entire brain. We investigated ictal BOLD networks using the simultaneous intracranial EEG-fMRI (icEEG-fMRI) in a 30 year-old male undergoing invasive presurgical evaluation with bilateral depth electrode implantations in amygdalae and hippocampi for refractory temporal lobe epilepsy. One spontaneous focal electrographic seizure was recorded. The aims of the data analysis were firstly to map BOLD changes related to the ictal activity identified on icEEG and secondly to compare different fMRI modelling approaches. Visual inspection of the icEEG showed an onset dominated by beta activity involving the right amygdala and hippocampus lasting 6.4 s (ictal onset phase), followed by gamma activity bilaterally lasting 14.8 s (late ictal phase). The fMRI data was analysed using SPM8 using two modelling approaches: firstly, purely based on the visually identified phases of the seizure and secondly, based on EEG spectral dynamics quantification. For the visual approach the two ictal phases were modelled as ‘ON’ blocks convolved with the haemodynamic response function; in addition the BOLD changes during the 30 s preceding the onset were modelled using a flexible basis set. For the quantitative fMRI modelling approach two models were evaluated: one consisting of the variations in beta and gamma bands power, thereby adding a quantitative element to the visually-derived models, and another based on principal components analysis of the entire spectrogram in attempt to reduce the bias associated with the visual appreciation of the icEEG. BOLD changes related to the visually defined ictal onset phase were revealed in the medial

  19. Adaptation of hybrid human-computer interaction systems using EEG error-related potentials.

    Science.gov (United States)

    Chavarriaga, Ricardo; Biasiucci, Andrea; Forster, Killian; Roggen, Daniel; Troster, Gerhard; Millan, Jose Del R

    2010-01-01

    Performance improvement in both humans and artificial systems strongly relies in the ability of recognizing erroneous behavior or decisions. This paper, that builds upon previous studies on EEG error-related signals, presents a hybrid approach for human computer interaction that uses human gestures to send commands to a computer and exploits brain activity to provide implicit feedback about the recognition of such commands. Using a simple computer game as a case study, we show that EEG activity evoked by erroneous gesture recognition can be classified in single trials above random levels. Automatic artifact rejection techniques are used, taking into account that subjects are allowed to move during the experiment. Moreover, we present a simple adaptation mechanism that uses the EEG signal to label newly acquired samples and can be used to re-calibrate the gesture recognition system in a supervised manner. Offline analysis show that, although the achieved EEG decoding accuracy is far from being perfect, these signals convey sufficient information to significantly improve the overall system performance.

  20. [French guidelines on electroencephalogram].

    Science.gov (United States)

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

    2014-12-01

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

  1. Detecting stable phase structures in EEG signals to classify brain activity amplitude patterns

    Institute of Scientific and Technical Information of China (English)

    Yusely RUIZ; Guang LI; Walter J. FREEMAN; Eduardo GONZALEZ

    2009-01-01

    Obtaining an electrocorticograms (ECoG) signal requires an invasive procedure in which brain activity is recorded from the cortical surface. In contrast, obtaining electroencephalograms (EEG) recordings requires the non-invasive procedure of recording the brain activity from the scalp surface, which allows EEG recordings to be performed more easily on healthy humans. In this work, a technique previously used to study spatial-temporal patterns of brain activity on animal ECoG was adapted for use on EEG. The main issues are centered on solving the problems introduced by the increment on the interelectrode distance and the procedure to detect stable frames. The results showed that spatial patterns of beta and gamma activity can also be extracted from the EEG signal by using stable frames as time markers for feature extraction. This adapted technique makes it possible to take advantage of the cognitive and phenomenological awareness of a normal healthy subject.

  2. 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.

  3. Binge Drinking Effects on EEG in Young Adult Humans

    Directory of Open Access Journals (Sweden)

    Kelly E. Courtney

    2010-05-01

    Full Text Available Young adult (N = 96 university students who varied in their binge drinking history were assessed by electroencephalography (EEG recording during passive viewing. Groups consisted of male and female non-binge drinkers (>1 to 5/4 drinks/ounces in under two hours, low-binge drinkers (5/4–7/6 drinks/ounces in under two hours, and high-binge drinkers (≥ 10 drinks/ounces in under two hours, who had been drinking alcohol at their respective levels for an average of 3 years. The non- and low-binge drinkers exhibited less spectral power than the high-binge drinkers in the delta (0–4 Hz and fast-beta (20–35 Hz bands. Binge drinking appears to be associated with a specific pattern of brain electrical activity in young adults that may reflect the future development of alcoholism.

  4. Synthesized quantitative assessment of human mental fatigue with EEG and HRV

    Science.gov (United States)

    Han, Qingpeng; Wang, Li; Wang, Ping; Wen, Bangchun

    2005-12-01

    The electroencephalograph (EEG) signals and heart rate variable (HRV) signals, which are relative to human body mental stress, are analyzed with the nonlinear dynamics and chaos. Based on calculated three nonlinear parameters, a synthesized quantitative criterion is proposed to assess the body's mental fatigue states. Firstly, the HRV and α wave of EEG from original signals are extracted based on wavelet transform technique. Then, the Largest Lyapunov Exponents, Complexity and Approximate Entropy, are calculated for both HRV and α wave. The three nonlinear parameters reflect quantitatively human physiological activities and can be used to evaluate the mental workload degree. Based on the computation and statistical analysis of practical EEG and HRV data, a synthesized quantitative assessment criterion is induced for mental fatigues with three nonlinear parameters of the above two rhythms. For the known 10 measured data of EEG and HRV signals, the assessment results are obtained with the above laws for different metal fatigue states. To compare with the practical cases, the identification accuracy of mental fatigue or not is up to 100 percent. Furthermore, the accuracies of weak fatigue, middle fatigue and serious fatigue mental workload are all relatively higher; they are about 94.44, 88.89, and 83.33 percent, respectively.

  5. 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.

  6. Burst suppression on amplitude-integrated electroencephalogram may be induced by midazolam : a report on three cases

    NARCIS (Netherlands)

    ter Horst, HJ; Brouwer, OF; Bos, AF

    2004-01-01

    Continuous amplitude-integrated electroencephalogram (aEEG) recording with a cerebral function monitor is a useful tool to evaluate prognoses following perinatal asphyxia in term infants. Drugs may change the pattern of the conventional EEG. This report presents three infants treated with midazolam

  7. Burst suppression on amplitude-integrated electroencephalogram may be induced by midazolam : a report on three cases

    NARCIS (Netherlands)

    ter Horst, HJ; Brouwer, OF; Bos, AF

    Continuous amplitude-integrated electroencephalogram (aEEG) recording with a cerebral function monitor is a useful tool to evaluate prognoses following perinatal asphyxia in term infants. Drugs may change the pattern of the conventional EEG. This report presents three infants treated with midazolam

  8. Norfloxacin-Induced Electroencephalogram Alteration and Seizures in Rats Are Not Triggered by Enhanced Levels of Intracerebral Glutamate†

    Science.gov (United States)

    Chenel, Marylore; Limosin, Anne; Marchand, Sandrine; Paquereau, Joël; Mimoz, Olivier; Couet, William

    2003-01-01

    Pharmacokinetic-pharmacodynamic modeling of the electroencephalogram (EEG) effect was combined with intracerebral glutamate determinations using microdialysis for rats receiving norfloxacin intravenously (150 mg/kg of body weight). The EEG effect (accompanied by tremors and seizures) was consistently observed without glutamate level modifications. Therefore, norfloxacin-inducted seizures are not triggered by intracerebral glutamate level enhancement. PMID:14576142

  9. Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson's Disease.

    Science.gov (United States)

    Liu, Guotao; Zhang, Yanping; Hu, Zhenghui; Du, Xiuquan; Wu, Wanqing; Xu, Chenchu; Wang, Xiangyang; Li, Shuo

    2017-01-01

    In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.

  10. Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson's Disease

    Science.gov (United States)

    Liu, Guotao; Zhang, Yanping; Hu, Zhenghui; Xu, Chenchu; Wang, Xiangyang; Li, Shuo

    2017-01-01

    In this study, a new combination scheme has been proposed for detecting Parkinson's disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy. PMID:28316861

  11. Chaos based encryption system for encrypting electroencephalogram signals.

    Science.gov (United States)

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

    2014-05-01

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

  12. Detection of eye blink artifacts from single prefrontal channel electroencephalogram.

    Science.gov (United States)

    Chang, Won-Du; Cha, Ho-Seung; Kim, Kiwoong; Im, Chang-Hwan

    2016-02-01

    Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.

  13. The combined technique for detection of artifacts in clinical electroencephalograms of sleeping newborns.

    Science.gov (United States)

    Schetinin, Vitaly; Schult, Joachim

    2004-03-01

    In this paper, we describe a new method combining the polynomial neural network and decision tree techniques in order to derive comprehensible classification rules from clinical electroencephalograms (EEGs) recorded from sleeping newborns. These EEGs are heavily corrupted by cardiac, eye movement, muscle, and noise artifacts and, as a consequence, some EEG features are irrelevant to classification problems. Combining the polynomial network and decision tree techniques, we discover comprehensible classification rules while also attempting to keep their classification error down. This technique is shown to out-perform a number of commonly used machine learning technique applied to automatically recognize artifacts in the sleep EEGs.

  14. Interrelations and circadian changes of electroencephalogram frequencies under baseline conditions and constant sleep pressure in the rat.

    Science.gov (United States)

    Yasenkov, R; Deboer, T

    2011-04-28

    Similar to the nap-protocols applied in humans, the repeated short-sleep deprivation protocol in rats stabilizes slow-wave activity (SWA, 0.5-4 Hz) in the non-rapid eye movement (NREM) sleep electroencephalogram (EEG), thus reflecting a constant sleep pressure or sleep homeostatic level, whereas higher frequencies (7-25 Hz) in these conditions preserve their daily rhythm, therefore demonstrating a strong input from an endogenous circadian clock. How different EEG frequencies in rapid eye movement (REM) sleep and waking respond to these constant conditions, how they interrelate to each other within the different vigilance states, and which component of sleep regulation (homeostatic or circadian) is involved, remain unknown. To answer these questions, we applied power spectral analysis and correlation analysis to 1 Hz bin EEG frequency data for different vigilance states in freely moving rats in constant darkness, under baseline conditions and during the repeated short-sleep deprivation protocol. Our analysis suggests that (1) 0.5-5 Hz frequencies in NREM sleep and higher frequencies in REM sleep (above 19 Hz) and waking (above 10 Hz) are sleep-dependent, and thus seem to be under control of the sleep homeostat, while (2) faster frequencies in the NREM sleep EEG (7-25 Hz) and 3-7 Hz activity in the REM sleep EEG are under strong influence of the endogenous circadian clock. Theta activity in waking (5-7 Hz) seems to reflect both circadian and behavior dependent influences. NREM sleep EEG frequencies between 9 and 14 Hz showed both homeostatic and circadian components in their behavior. Thus, frequencies in the EEG of the different vigilance states seem to represent circadian and homeostatic components of sleep regulatory mechanisms, where REM sleep and waking frequency ranges behave similarly to each other and differently from NREM sleep frequencies.

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

    Directory of Open Access Journals (Sweden)

    Marzia De Lucia

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

  16. Familiarity effects in EEG-based emotion recognition

    National Research Council Canada - National Science Library

    Thammasan, Nattapong; Moriyama, Koichi; Fukui, Ken-ichi; Numao, Masayuki

    2017-01-01

    Although emotion detection using electroencephalogram (EEG) data has become a highly active area of research over the last decades, little attention has been paid to stimulus familiarity, a crucial subjectivity issue...

  17. Electroencephalogram evidence for the activation of human mirror neuron system during the observation of intransitive shadow and line drawing actions

    Institute of Scientific and Technical Information of China (English)

    Huaping Zhu; Yaoru Sun; Fang Wang

    2013-01-01

    Previous studies have demonstrated that hand shadows may activate the motor cortex associated with the mirror neuron system in human brain. However, there is no evidence of activity of the human mirror neuron system during the observation of intransitive movements by shadows and line drawings of hands. This study examined the suppression of electroencephalography mu waves hand actions, hand shadow actions and actions made by line drawings of hands. The results showed significant desynchronization of the mu rhythm ("mu suppression") across the sensorimotor cortex (recorded at C3, Cz and C4), the frontal cortex (recorded at F3, Fz and F4) and the central and right posterior parietal cortex (recorded at Pz and P4) under all three conditions. Our experimental findings suggest that the observation of "impoverished hand actions", such as intransitive movements of shadows and line drawings of hands, is able to activate widespread cortical areas related to the putative human mirror neuron system.

  18. Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback

    CERN Document Server

    Zotev, Vadim; Yuan, Han; Misaki, Masaya; Bodurka, Jerzy

    2014-01-01

    Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation of the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results...

  19. Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis

    OpenAIRE

    Sidorov, Michael S.; Deck, Gina M.; Dolatshahi, Marjan; Thibert, Ronald L.; Bird, Lynne M.; Chu, Catherine J.; Philpot, Benjamin D.

    2017-01-01

    Background: Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker. Methods: We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state...

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

    Directory of Open Access Journals (Sweden)

    Sarcinelli-Filho Mario

    2008-03-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

  2. A new measure to characterize multifractality of sleep electroencephalogram

    Institute of Scientific and Technical Information of China (English)

    MA Qianli; NING Xinbao; WANG Jun; BIAN Chunhua

    2006-01-01

    Traditional methods for nonlinear dynamic analysis, such as correlation dimension,Lyapunov exponent, approximate entropy, detrended fluctuation analysis, using a single parameter, cannot fully describe the extremely sophisticated behavior of electroencephalogram (EEG). The multifractal formalism reveals more "hidden" information of EEG by using singularity spectrum to characterize its nonlinear dynamics. In this paper, the zero-crossing time intervals of sleep EEG were studied using multifractal analysis. A new multifractal measure △asα was proposed to describe the asymmetry of singularity spectrum, and compared with the singularity strength range △α that was normally used as a degree indicator of multifractality. One-way analysis of variance and multiple comparison tests showed that the new measure we proposed gave better discrimination of sleep stages, especially in the discrimination between sleep and awake, and between sleep stages 3and 4.

  3. Learning dynamics from nonstationary time series: Analysis of electroencephalograms

    Science.gov (United States)

    Gribkov, Dmitrii; Gribkova, Valentina

    2000-06-01

    We propose an empirical modeling technique for a nonstationary time series analysis. Proposed methods include a high-dimensional (N>3) dynamical model construction in the form of delay differential equations, a nonparametric method of respective time delay calculation, the detection of quasistationary regions of the process by reccurence analysis in the space of model coefficients, and final fitting of the model to quasistationary segments of observed time series. We also demonstrate the effectiveness of our approach for nonstationary signal classification in the space of model coefficients. Applying the empirical modeling technique to electroencephalogram (EEG) records analysis, we find evidence of high-dimensional nonlinear dynamics in quasistationary EEG segments. Reccurence analysis of model parameters reveals long-term correlations in nonstationary EEG records. Using the dynamical model as a nonlinear filter, we find that different emotional states of subjects can be clearly distinguished in the space of model coefficients.

  4. [Mental fatigue electroencephalogram signals analysis based on singular system].

    Science.gov (United States)

    Zhang, Chong; Yu, Xiaolin; Yang, Yong; Xu, Lei

    2014-10-01

    In the present paper, the contribution of the largest principal component and the number of principal component needed for accumulative contribution 95% are selected as indices of electroencephalogram (EEG) in mental fatigue state in order to investigate the relationship between these parameters and mental fatigue. The experimental results showed that the contribution of the largest principal component of EEG signals increased in the prefrontal, frontal and central areas, while the number of principal component needed for accumulative contribution decreased by 95% with the increasing mental fatigue level. The parameters of singular system of EEG signals can be regarded as useful features for the estimation of mental fatigue and have larger application value in the study of mental fatigue.

  5. Comparison of Sleep-Wake Classification using Electroencephalogram and Wrist-worn Multi-modal Sensor Data

    OpenAIRE

    Sano, Akane; Picard, Rosalind W.

    2014-01-01

    This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results. As results, EEG features showed 83% while features from a wrist wearable sensor showed 74% and ...

  6. Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

    OpenAIRE

    Yu, Yi; Zhao, Yun; Si, Yajing; Ren, Qiongqiong; Ren, Wu; JING, CHANGQIN; Zhang, Hongxing

    2016-01-01

    Background In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia. Methods The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded f...

  7. Complexity analysis of electroencephalogram in patients with Alzheimer’s disease and mild cognitive impairment

    Institute of Scientific and Technical Information of China (English)

    徐梅松

    2013-01-01

    Objective To investigate the Lemple-Zie complexity (LZC) characteristics in patients with Alzheimer’s disease (AD) ,mild cognitive impairment (MCI) and normal elderly,and the possibility of differentiating AD,MCI and normal elderly by LZC.Methods Electroencephalogram (EEG) of 30 AD patients,30 MCI patients and 20normal elderly with eyes closed in rest state were recorded.In acquired EEG data,2 048 points (10.14 s) of

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

    Directory of Open Access Journals (Sweden)

    Gernot G Supp

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

  9. The Quantification of EEG Continuity by 24-hour Recordings in Intraventricular Hemorrhaging and Periventricular Leukomalacia : Its Prognostic Value in Comparison with Conventional EEG Recordings

    OpenAIRE

    Goto, Kazuya; Ogawa, Teruyuki; Sonoda, Hirotomi

    1995-01-01

    Investigation was made of the prognostic value of quantified 24-hour electroencephalogram (EEG) continuity by 24-hour EEG recordings early in the postnatal period as compared follow-up, conventional EEG recordings up to fullterm was evaluated for low-birth weight infants with intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL). Of twelve infants, 24-hour EEG recordings were performed on eight, and a total of 58 follow-up EEG recordings were obtained from the twelve. The 2...

  10. [Development and Design of Portable Sleep Electroencephalogram Monitoring System].

    Science.gov (United States)

    Li, Hui; Ye, Datian; Peng, Cheng

    2015-06-01

    The growing rate of public health problem for increasing number of people afflicted with poor sleep quality suggests the importance of developing portable sleep electroencephalogram (EEG) monitoring systems. The system could record the overnight EEG signal, classify sleep stages automatically, and grade the sleep quality. We in our laboratory collected the signals in an easy way using a single channel with three electrodes which were placed in frontal position in case of the electrode drop-off during sleep. For a test, either silver disc electrodes or disposable medical electrocardiographic electrodes were used. Sleep EEG recorded by the two types of electrodes was compared to each other so as to find out which type was more suitable. Two algorithms were used for sleep EEG processing, i. e. amplitude-integrated EEG (aEEG) algorithm and sample entropy algorithm. Results showed that both algorithms could perform sleep stage classification and quality evaluation automatically. The present designed system could be used to monitor overnight sleep and provide quantitative evaluation.

  11. Electroencephalogram effects of armodafinil: comparison with behavioral alertness.

    Science.gov (United States)

    Conrado, Daniela J; Bewernitz, Michael; Ding, Mingzhou; Cibula, Jean; Seubert, Christoph; Sy, Sherwin K B; Eisenschenk, Stephan; Derendorf, Hartmut

    2013-10-01

    Development of central nervous system-acting drugs would be enhanced by suitable biomarkers that reflect the targeted pathophysiologic brain state. The electroencephalogram (EEG) has several characteristics of an ideal biomarker and can be promptly adapted to pre-clinical and clinical testing. The aim of this study was to evaluate EEG as a measure of the wakefulness-promoting effect of armodafinil in sleep deprived healthy subjects. Armodafinil pharmacodynamics were simultaneously assessed by EEG- and behavioral-based measures including a well-established measure of alertness. Using two quantitative EEG-based measures-power spectral and event-related brain activity analyses-we observed that armodafinil mitigated the slowing of brain activity and the decrease of the event-related brain activity caused by sleep deprivation. Armodafinil-induced changes in EEG are in agreement and explain up to 73.1% of the armodafinil-induced changes in alertness. Our findings suggest that EEG can serve as a marker of the wakefulness-promoting drug effect.

  12. EEG-fMRI based information theoretic characterization of the human perceptual decision system.

    Directory of Open Access Journals (Sweden)

    Dirk Ostwald

    Full Text Available The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG and functional magnetic resonance imaging data (fMRI (Ostwald et al. (2010, NeuroImage 49: 498-516. We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain.

  13. Data acquisition instrument for EEG based on embedded system

    Science.gov (United States)

    Toresano, La Ode Husein Z.; Wijaya, Sastra Kusuma; Prawito, Sudarmaji, Arief; Syakura, Abdan; Badri, Cholid

    2017-02-01

    An electroencephalogram (EEG) is a device for measuring and recording the electrical activity of brain. The EEG data of signal can be used as a source of analysis for human brain function. The purpose of this study was to design a portable multichannel EEG based on embedded system and ADS1299. The ADS1299 is an analog front-end to be used as an Analog to Digital Converter (ADC) to convert analog signal of electrical activity of brain, a filter of electrical signal to reduce the noise on low-frequency band and a data communication to the microcontroller. The system has been tested to capture brain signal within a range of 1-20 Hz using the NETECH EEG simulator 330. The developed system was relatively high accuracy of more than 82.5%. The EEG Instrument has been successfully implemented to acquire the brain signal activity using a PC (Personal Computer) connection for displaying the recorded data. The final result of data acquisition has been processed using OpenBCI GUI (Graphical User Interface) based through real-time process for 8-channel signal acquisition, brain-mapping and power spectral decomposition signal using the standard FFT (Fast Fourier Transform) algorithm.

  14. Analysis of electroencephalogram(EEG)of 21 children with acute tetramine poisoning%21例儿童急性毒鼠强中毒的脑电图分析

    Institute of Scientific and Technical Information of China (English)

    张界宋

    2003-01-01

    目的探讨儿童急性毒鼠强中毒(ATP)脑电图(EEG)变化规律及临床应用价值. 方法回顾性分析21例ATP儿童的临床及EEG资料. 结果 18例患儿EEG有中度以上的异常,其中12例有癫痫样放电.治疗后复查的12例中正常或好转10例,2例无变化.ATP患儿EEG表现为其基本频率明显慢于正常同龄组脑波频率,EEG表现为在广泛慢波背景上出现阵发性棘波、棘慢波.EEG异常程度与中毒程度高度关联.随着临床症状好转,EEG也明显好转. 结论 ATP患儿EEG异常率很高.EEG异常程度与中毒程度有高度关联,其动态演变可以反映病情的转归.

  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. 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.

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

    Science.gov (United States)

    Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

    2017-10-01

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

  18. Frontal Electroencephalogram Asymmetry during Affective Processing in Children with Down Syndrome: A Pilot Study

    Science.gov (United States)

    Conrad, N. J.; Schmidt, L. A.; Niccols, A.; Polak, C. P.; Riniolo, T. C.; Burack, J. A.

    2007-01-01

    Background: Although the pattern of frontal electroencephalogram (EEG) asymmetry during the processing of emotion has been examined in many studies of healthy adults and typically developing infants and children, no published work has used these theoretical and methodological approaches to study emotion processing in children with Down syndrome.…

  19. Electroencephalogram and Heart Rate Regulation to Familiar and Unfamiliar People in Children with Autism Spectrum Disorders

    Science.gov (United States)

    Van Hecke, Amy Vaughan; Lebow, Jocelyn; Bal, Elgiz; Lamb, Damon; Harden, Emily; Kramer, Alexis; Denver, John; Bazhenova, Olga; Porges, Stephen W.

    2009-01-01

    Few studies have examined whether familiarity of partner affects social responses in children with autism. This study investigated heart rate regulation (respiratory sinus arrhythmia [RSA]: The myelinated vagus nerve's regulation of heart rate) and temporal-parietal electroencephalogram (EEG) activity while nineteen 8- to 12-year-old children with…

  20. Remote monitoring of electroencephalogram, electrocardiogram, and behavior during controlled atmosphere stunning in broilers: Implications for welfare

    NARCIS (Netherlands)

    Coenen, A.M.L.; Lankhaar, J.A.C.; Lowe, J.C.; McKeegan, D.

    2009-01-01

    This study examined the welfare implications of euthanizing broilers with 3 gas mixtures relevant to the commercial application of controlled atmosphere stunning (CAS). Birds were implanted/equipped with electrodes to measure brain activity (electroencephalogram, EEG) and heart rate. These signals w

  1. Category-Selectivity in Human Visual Cortex Follows Cortical Topology: A Grouped icEEG Study.

    Directory of Open Access Journals (Sweden)

    Cihan Mehmet Kadipasaoglu

    Full Text Available Neuroimaging studies suggest that category-selective regions in higher-order visual cortex are topologically organized around specific anatomical landmarks: the mid-fusiform sulcus (MFS in the ventral temporal cortex (VTC and lateral occipital sulcus (LOS in the lateral occipital cortex (LOC. To derive precise structure-function maps from direct neural signals, we collected intracranial EEG (icEEG recordings in a large human cohort (n = 26 undergoing implantation of subdural electrodes. A surface-based approach to grouped icEEG analysis was used to overcome challenges from sparse electrode coverage within subjects and variable cortical anatomy across subjects. The topology of category-selectivity in bilateral VTC and LOC was assessed for five classes of visual stimuli-faces, animate non-face (animals/body-parts, places, tools, and words-using correlational and linear mixed effects analyses. In the LOC, selectivity for living (faces and animate non-face and non-living (places and tools classes was arranged in a ventral-to-dorsal axis along the LOS. In the VTC, selectivity for living and non-living stimuli was arranged in a latero-medial axis along the MFS. Written word-selectivity was reliably localized to the intersection of the left MFS and the occipito-temporal sulcus. These findings provide direct electrophysiological evidence for topological information structuring of functional representations within higher-order visual cortex.

  2. 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.

  3. Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition

    OpenAIRE

    Gajic, D.; Djurovic, Z.; Di Gennaro, S.; Gustafsson, Fredrik

    2014-01-01

    The electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. Long-term EEG recordings of an epileptic patient contain a huge amount of EEG data. The detection of epileptic activity is, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data, usually performed by an expert. This paper describes an automated classification of EEG signals for the detection of epileptic seizures using wavelet transform and statistical pat...

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

    Science.gov (United States)

    Dijk, D. J.

    1999-01-01

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

  5. Nitrous oxide as a humane method for piglet euthanasia: Behavior and electroencephalography (EEG).

    Science.gov (United States)

    Rault, Jean-Loup; Kells, Nikki; Johnson, Craig; Dennis, Rachel; Sutherland, Mhairi; Lay, Donald C

    2015-11-01

    The search for humane methods to euthanize piglets is critical to address public concern that current methods are not optimal. Blunt force trauma is considered humane but esthetically objectionable. Carbon dioxide (CO2) is used but criticized as aversive. This research sought to: 1) evaluate the aversiveness of inhaling nitrous oxide (N2O; 'laughing gas') using an approach-avoidance test relying on the piglet's perspective, and 2) validate its humaneness to induce loss of consciousness by electroencephalography (EEG). The gas mixtures tested were N2O and air (90%:10%; '90 N'); N2O, oxygen and air (60%:30%:10%; '60 N'); and CO2 and air (90%:10%; '90 C'). Experiment 1 allowed piglets to walk freely between one chamber filled with air and another prefilled with 60 N or 90 N. All piglets exposed to 60 N lasted for the 10 min test duration whereas all piglets exposed to 90 N had to be removed within 5 min because they fell recumbent and unresponsive and then started to flail. Experiment 2 performed the same test except the gas chamber held N2O prefilled at 25%, 50%, or 75% or CO2 prefilled at 7%, 14%, or 21%. The test was terminated more quickly at higher concentrations due to the piglets' responses. Time spent ataxic was greater in the middle concentration gradients. Flailing behavior tended to correlate with increasing concentrations of CO2 but not N2O. Experiment 3, using the minimal anesthesia model, showed that both 90 N and 90 C induced isoelectric EEG, in 71 and 59 s respectively, but not 60 N within 15 min. The EEG results together with the observed behavioral changes reflect differences in the animal's perceptive experience. The implications for animal welfare are that N2O is much less aversive than CO2, and 90% N2O can euthanize piglets.

  6. Preliminary results of mental workload and task engagement assessment using electroencephalogram in a space suit.

    Science.gov (United States)

    Rabbi, Ahmed F; Zony, Abongwa N; de Leon, Pablo; Fazel-Rezai, Reza

    2012-01-01

    In this paper, we present preliminary results of subject's mental workload and task engagement assessment in an experimental space suit. We have quantified the mental workload and task engagement based on changes in electroencephalogram (EEG). EEG signals were collected from subjects scalp using a commercial wireless EEG device in two experimental conditions - when subjects did not wear space suit (control condition) and when subjects wore space suit. Brain state changes were estimated and compared with the direct responses for different tasks and different conditions. We found that the spacesuit experiment introduced a greater mental workload where subject's stress levels were higher than control experiment.

  7. Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases.

    Science.gov (United States)

    Li, Jing; Liu, Xianzeng; Ouyang, Gaoxiang

    2016-07-01

    We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with absence epilepsy are sampled, filtered, selected, and dissected from seizure-free, preseizure, and seizure phases. A total of 112 two-second 19-channel EEG epochs from 10 patients were selected from each phase. For each epoch, multiscale permutation entropy of the EEG data was calculated. The feature dimensionality was reduced by linear discriminant analysis to obtain a more discriminative and compact representation. Finally, a relevance feedback technique, that is, direct biased discriminant analysis, was applied to 68 randomly selected queries over nine iterations. This study is a first attempt to apply the statistical analysis of relevance feedback to the distinction of different EEG activity states in absence epilepsy. The average precision in the top 10 returned results was 97.5%, and the standard deviation suggested that embedding relevance feedback can effectively distinguish different seizure phases in absence epilepsy. The experimental results indicate that relevance feedback may be an effective tool for the prediction of different activity states in human absence epilepsy. The simultaneous analysis of multichannel EEG signals provides a powerful tool for the exploration of abnormal electrical brain activity in patients with epilepsy.

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

    Science.gov (United States)

    Bell, Martha Ann; Cuevas, Kimberly

    2012-01-01

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

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

    Science.gov (United States)

    Bell, Martha Ann; Cuevas, Kimberly

    2012-01-01

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

  10. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  11. EEG processing and its application in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Wang Jing; Xu Guanghua; Xie Jun; Zhang Feng; Li Lili; Han Chengcheng; Li Yeping; Sun Jingjing

    2013-01-01

    Electroencephalogram (EEG) is an efficient tool in exploring human brains.It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI).The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI.First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed.Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton' s rings are introduced.Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated.As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.

  12. Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control

    Science.gov (United States)

    Huang, Dandan; Lin, Peter; Fei, Ding-Yu; Chen, Xuedong; Bai, Ou

    2009-08-01

    This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive EEG in order to control a discrete two-dimensional cursor movement for a potential multidimensional brain-computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at an average accuracy of 85.5 ± 4.65%; the subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multidimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

  13. Neonatal EEG classification using atomic decomposition

    OpenAIRE

    Belur Nagaraj, Sunil

    2015-01-01

    The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classifica...

  14. Prompt recognition of brain states by their EEG signals

    DEFF Research Database (Denmark)

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

    1997-01-01

    Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases not previo......Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...... not previously seen by the system on the basis of 1 sec long EEGs....

  15. Using Complexity Measure to Characterize Information Transmission of Human Brain Cortex

    Institute of Scientific and Technical Information of China (English)

    徐京华; 吴祥宝

    1994-01-01

    The information transmission among various parts of the cortex are computed with the the-ory of mutual information from the data of the electroencephalogram(EEG)time series of normal humansubjects.The intensities of these transmissions are characterized by the"complexity"measures.These mea-sures have revealed to be sensitively related to the functional conditions of human beings.

  16. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  17. Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search

    Directory of Open Access Journals (Sweden)

    M. Sreeshakthy

    2016-01-01

    Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.

  18. 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

  19. 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

  20. Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

    Directory of Open Access Journals (Sweden)

    Jérôme Van Zaen

    Full Text Available Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

  1. Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

    Science.gov (United States)

    Van Zaen, Jérôme; Murray, Micah M; Meuli, Reto A; Vesin, Jean-Marc

    2013-01-01

    Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

  2. TMS-EEG signatures of GABAergic neurotransmission in the human cortex.

    Science.gov (United States)

    Premoli, Isabella; Castellanos, Nazareth; Rivolta, Davide; Belardinelli, Paolo; Bajo, Ricardo; Zipser, Carl; Espenhahn, Svenja; Heidegger, Tonio; Müller-Dahlhaus, Florian; Ziemann, Ulf

    2014-04-16

    Combining transcranial magnetic stimulation (TMS) and electroencephalography (EEG) constitutes a powerful tool to directly assess human cortical excitability and connectivity. TMS of the primary motor cortex elicits a sequence of TMS-evoked EEG potentials (TEPs). It is thought that inhibitory neurotransmission through GABA-A receptors (GABAAR) modulates early TEPs (TMS), whereas GABA-B receptors (GABABR) play a role for later TEPs (at ∼100 ms after TMS). However, the physiological underpinnings of TEPs have not been clearly elucidated yet. Here, we studied the role of GABAA/B-ergic neurotransmission for TEPs in healthy subjects using a pharmaco-TMS-EEG approach. In Experiment 1, we tested the effects of a single oral dose of alprazolam (a classical benzodiazepine acting as allosteric-positive modulator at α1, α2, α3, and α5 subunit-containing GABAARs) and zolpidem (a positive modulator mainly at the α1 GABAAR) in a double-blind, placebo-controlled, crossover study. In Experiment 2, we tested the influence of baclofen (a GABABR agonist) and diazepam (a classical benzodiazepine) versus placebo on TEPs. Alprazolam and diazepam increased the amplitude of the negative potential at 45 ms after stimulation (N45) and decreased the negative component at 100 ms (N100), whereas zolpidem increased the N45 only. In contrast, baclofen specifically increased the N100 amplitude. These results provide strong evidence that the N45 represents activity of α1-subunit-containing GABAARs, whereas the N100 represents activity of GABABRs. Findings open a novel window of opportunity to study alteration of GABAA-/GABAB-related inhibition in disorders, such as epilepsy or schizophrenia.

  3. Spectral Asymmetry and Higuchi’s Fractal Dimension Measures of Depression Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Maie Bachmann

    2013-01-01

    Full Text Available This study was aimed to compare two electroencephalogram (EEG analysis methods, spectral asymmetry index (SASI and Higuchi’s fractal dimension (HFD, for detection of depression. Linear SASI method is based on evaluation of the balance of powers in two EEG frequency bands in one channel selected higher and lower than the alpha band spectrum maximum. Nonlinear HFD method calculates fractal dimension directly in the time domain. The resting EEG signals of 17 depressive patients and 17 control subjects were used as a database for calculations. SASI values were positive for depressive and negative for control group (P0.05. The results indicated that the linear EEG analysis method SASI and the nonlinear HFD method both demonstrated a good sensitivity for detection of characteristic features of depression in a single-channel EEG.

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

    DEFF Research Database (Denmark)

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

    1991-01-01

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

  5. Transcranial alternating current stimulation enhances individual alpha activity in human EEG.

    Directory of Open Access Journals (Sweden)

    Tino Zaehle

    Full Text Available Non-invasive electrical stimulation of the human cortex by means of transcranial direct current stimulation (tDCS has been instrumental in a number of important discoveries in the field of human cortical function and has become a well-established method for evaluating brain function in healthy human participants. Recently, transcranial alternating current stimulation (tACS has been introduced to directly modulate the ongoing rhythmic brain activity by the application of oscillatory currents on the human scalp. Until now the efficiency of tACS in modulating rhythmic brain activity has been indicated only by inference from perceptual and behavioural consequences of electrical stimulation. No direct electrophysiological evidence of tACS has been reported. We delivered tACS over the occipital cortex of 10 healthy participants to entrain the neuronal oscillatory activity in their individual alpha frequency range and compared results with those from a separate group of participants receiving sham stimulation. The tACS but not the sham stimulation elevated the endogenous alpha power in parieto-central electrodes of the electroencephalogram. Additionally, in a network of spiking neurons, we simulated how tACS can be affected even after the end of stimulation. The results show that spike-timing-dependent plasticity (STDP selectively modulates synapses depending on the resonance frequencies of the neural circuits that they belong to. Thus, tACS influences STDP which in turn results in aftereffects upon neural activity.The present findings are the first direct electrophysiological evidence of an interaction of tACS and ongoing oscillatory activity in the human cortex. The data demonstrate the ability of tACS to specifically modulate oscillatory brain activity and show its potential both at fostering knowledge on the functional significance of brain oscillations and for therapeutic application.

  6. Value of long-term electroencephalogram in diagnosing epilepsy

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    BACKGROUND: Routine electroencephalogram (EEG) usually cannot accurately reflect the discharge of epileptic patients due to the short examination, and long-term EEG can make up the shortcoming.OBJECTIVE: To comparatively analyze the long-term EEG of epileptic and non-epileptic patients, and investigate the values of long-term EEG in the diagnosis and differential diagnosis of epilepsy.DESIGN: A case-controlled study.SETTING: Ningjin County People's Hospital.PARTICIPANTS: Totally 122 patients with epilepsy (epilepsy group) were selected from the EEG room of Ningjin County People's Hospital from January 2000 to December 2006, including 76 males and 44 females,7 months to 78 years of age, the disease course ranged from 7 days to 7.5 years, and they all according with the standards for epilepsy set by the International Association for Epilepsy in 1997. Meanwhile, 118 patients with non-epileptic paroxysmal diseases were selected as the control group, including 71 males and 47 females, 2.5 - 87 years of age, the disease course ranged from 3 days to 7.5 years. Informed contents were obtained from all the subjects.METHODS: OXFORD GATE WAY 2000 16-lead portable EEG recorder was used for 24-hour electroencephalographic procedure. The patients could move normally during the monitoring, their activities,sleeping conditions, time and manifestations of seizures were recorded in details. In the next day, EEG at wake was recorded for 10 minutes, followed by 3-minute hyperventilation and open/close eye induction test,the phases of non-rapid eye movement ( Ⅰ - Ⅳ) and rapid eye movement were performed using EEG at sleep according to the international EEG standard. The abnormal rates of EEG epileptic discharge at wake and sleep at different sites were calculated.MAIN OUTCOME MEASURES: Abnormal rate of long-term EEG at wake and sleep in both groups;Epileptic discharge at different sleeping phases in both groups; Abnormal rates of EEG epileptic discharge at wake and sleep at

  7. 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 frequ

  8. Qualitative and Quantitative Characteristics of the Electroencephalogram in Normal Horses during Administration of Inhaled Anesthesia.

    Science.gov (United States)

    Williams, D C; Brosnan, R J; Fletcher, D J; Aleman, M; Holliday, T A; Tharp, B; Kass, P H; LeCouteur, R A; Steffey, E P

    2016-01-01

    The effects of anesthesia on the equine electroencephalogram (EEG) after administration of various drugs for sedation, induction, and maintenance are known, but not that the effect of inhaled anesthetics alone for EEG recording. To determine the effects of isoflurane and halothane, administered as single agents at multiple levels, on the EEG and quantitative EEG (qEEG) of normal horses. Six healthy horses. Prospective study. Digital EEG with video and quantitative EEG (qEEG) were recorded after the administration of one of the 2 anesthetics, isoflurane or halothane, at 3 alveolar doses (1.2, 1.4 and 1.6 MAC). Segments of EEG during controlled ventilation (CV), spontaneous ventilation (SV), and with peroneal nerve stimulation (ST) at each MAC multiple for each anesthetic were selected, analyzed, and compared. Multiple non-EEG measurements were also recorded. Specific raw EEG findings were indicative of changes in the depth of anesthesia. However, there was considerable variability in EEG between horses at identical MAC multiples/conditions and within individual horses over segments of a given epoch. Statistical significance for qEEG variables differed between anesthetics with bispectral index (BIS) CV MAC and 95% spectral edge frequency (SEF95) SV MAC differences in isoflurane only and median frequency (MED) differences in SV MAC with halothane only. Unprocessed EEG features (background and transients) appear to be beneficial for monitoring the depth of a particular anesthetic, but offer little advantage over the use of changes in mean arterial pressure for this purpose. Copyright © 2015 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

  9. EEG beta suppression and low gamma modulation are different elements of human upright walking.

    Science.gov (United States)

    Seeber, Martin; Scherer, Reinhold; Wagner, Johanna; Solis-Escalante, Teodoro; Müller-Putz, Gernot R

    2014-01-01

    Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG) recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source modeling to high-density (120 channels) EEG recordings using individual anatomy reconstructed from structural magnetic resonance imaging scans. First, we analyzed amplitude differences between the conditions, walking and upright standing. Second, we investigated amplitude modulations related to the gait phase. During active walking upper μ (10-12 Hz) and β (18-30 Hz) oscillations were suppressed [event-related desynchronization (ERD)] compared to upright standing. Significant β ERD activity was located focally in central sensorimotor areas for 9/10 subjects. Additionally, we found that low γ (24-40 Hz) amplitudes were modulated related to the gait phase. Because there is a certain frequency band overlap between sustained β ERD and gait phase related modulations in the low γ range, these two phenomena are superimposed. Thus, we observe gait phase related amplitude modulations at a certain ERD level. We conclude that sustained μ and β ERD reflect a movement related state change of cortical excitability while gait phase related modulations in the low γ represent the motion sequence timing during gait. Interestingly, the center frequencies of sustained β ERD and gait phase modulated amplitudes were identified to be different. They may therefore be caused by different neuronal rhythms, which should be taken under consideration in future studies.

  10. Estimating mental fatigue based on electroencephalogram and heart rate variability

    Science.gov (United States)

    Zhang, Chong; Yu, Xiaolin

    2010-01-01

    The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue. Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.

  11. Nonlinear dynamics and quantitative EEG analysis.

    Science.gov (United States)

    Jansen, B H

    1996-01-01

    Quantitative, computerized electroencephalogram (EEG) analysis appears to be based on a phenomenological approach to EEG interpretation, and is primarily rooted in linear systems theory. A fundamentally different approach to computerized EEG analysis, however, is making its way into the laboratories. The basic idea, inspired by recent advances in the area of nonlinear dynamics and chaos theory, is to view an EEG as the output of a deterministic system of relatively simple complexity, but containing nonlinearities. This suggests that studying the geometrical dynamics of EEGs, and the development of neurophysiologically realistic models of EEG generation may produce more successful automated EEG analysis techniques than the classical, stochastic methods. A review of the fundamentals of chaos theory is provided. Evidence supporting the nonlinear dynamics paradigm to EEG interpretation is presented, and the kind of new information that can be extracted from the EEG is discussed. A case is made that a nonlinear dynamic systems viewpoint to EEG generation will profoundly affect the way EEG interpretation is currently done.

  12. Early clinical signs in neonates with hypoxic ischemic encephalopathy predict an abnormal amplitude-integrated electroencephalogram at age 6 hours

    OpenAIRE

    Horn, Alan R; Swingler, George H; Myer, Landon; Linley, Lucy L; Raban, Moegammad S; Joolay, Yaseen; Harrison, Michael C; Chandrasekaran, Manigandan; Rhoda, Natasha R; Robertson, Nicola J.

    2013-01-01

    Background An early clinical score predicting an abnormal amplitude-integrated electroencephalogram (aEEG) or moderate-severe hypoxic ischemic encephalopathy (HIE) may allow rapid triage of infants for therapeutic hypothermia. We aimed to determine if early clinical examination could predict either an abnormal aEEG at age 6 hours or moderate-severe HIE presenting within 72 hours of birth. Methods Sixty infants ≥ 36 weeks gestational age were prospectively enrolled following suspected intrapar...

  13. Detecting epileptic seizure activity in the EEG by independent component analysis

    NARCIS (Netherlands)

    Hoeve, Maarten-Jan; van der Zwaag, B.J.; van Burik, M.J.; Slump, Cornelis H.; Jones, Richard

    Manually reviewing EEG (Electroencephalogram) recordings, for detection of electrographical patterns, is a time consuming business. Therefore, the ability to automate the classification of interesting electrographical patterns is a good supplement to the wide range of detection algorithms currently

  14. High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research

    Science.gov (United States)

    Lachaux, Jean-Philippe; Axmacher, Nikolai; Mormann, Florian; Halgren, Eric; Crone, Nathan E.

    2013-01-01

    Human intracranial EEG (iEEG) recordings are primarily performed in epileptic patients for presurgical mapping. When patients perform cognitive tasks, iEEG signals reveal high-frequency neural activities (HFA, between around 40 Hz and 150 Hz) with exquisite anatomical, functional and temporal specificity. Such HFA were originally interpreted in the context of perceptual or motor binding, in line with animal studies on gamma-band (‘40Hz’) neural synchronization. Today, our understanding of HFA has evolved into a more general index of cortical processing: task-induced HFA reveals, with excellent spatial and time resolution, the participation of local neural ensembles in the task-at-hand, and perhaps the neural communication mechanisms allowing them to do so. This review promotes the claim that studying HFA with iEEG provides insights into the neural bases of cognition that cannot be derived as easily from other approaches, such as fMRI. We provide a series of examples supporting that claim, drawn from studies on memory, language and default-mode networks, and successful attempts of real-time functional mapping. These examples are followed by several guidelines for HFA research, intended for new groups interested by this approach. Overall, iEEG research on HFA should play an increasing role in cognitive neuroscience in humans, because it can be explicitly linked to basic research in animals. We conclude by discussing the future evolution of this field, which might expand that role even further, for instance through the use of multi-scale electrodes and the fusion of iEEG with MEG and fMRI. PMID:22750156

  15. Delta rhythmicity is a reliable EEG biomarker in Angelman syndrome: a parallel mouse and human analysis.

    Science.gov (United States)

    Sidorov, Michael S; Deck, Gina M; Dolatshahi, Marjan; Thibert, Ronald L; Bird, Lynne M; Chu, Catherine J; Philpot, Benjamin D

    2017-01-01

    Clinicians have qualitatively described rhythmic delta activity as a prominent EEG abnormality in individuals with Angelman syndrome, but this phenotype has yet to be rigorously quantified in the clinical population or validated in a preclinical model. Here, we sought to quantitatively measure delta rhythmicity and evaluate its fidelity as a biomarker. We quantified delta oscillations in mouse and human using parallel spectral analysis methods and measured regional, state-specific, and developmental changes in delta rhythms in a patient population. Delta power was broadly increased and more dynamic in both the Angelman syndrome mouse model, relative to wild-type littermates, and in children with Angelman syndrome, relative to age-matched neurotypical controls. Enhanced delta oscillations in children with Angelman syndrome were present during wakefulness and sleep, were generalized across the neocortex, and were more pronounced at earlier ages. Delta rhythmicity phenotypes can serve as reliable biomarkers for Angelman syndrome in both preclinical and clinical settings.

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

    DEFF Research Database (Denmark)

    Stahlhut, Carsten

    be confused with each other as noise is present in the EEG recordings. Moreover, we examine how errors in the forward model affect the source confusion. The primary aim of this thesis is to provide sharper EEG brain images by improving current inverse methods. In this relation we focus the attention on two......This thesis presents electroencephalography (EEG) brain imaging by covering topics as empirical evaluation of source confusion, probabilistic inverse methods, and source analysis performed on infant EEG data. In terms of source confusion we inspect how current sources within the brain may...... topics in EEG source reconstruction, namely, the forward progation model (describing the mapping from the current sources within the brain to the sensors at the scalp) and the temporal patterns present in the EEG. As forward models may suffer from a number of errors including the geometrical...

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

    OpenAIRE

    E. Parvinnia; M. Sabeti; M. Zolghadri Jahromi; Boostani, R

    2014-01-01

    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 near...

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

    Energy Technology Data Exchange (ETDEWEB)

    Geissler, E

    2007-07-01

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

  19. Nonlinear analysis of EEG for epileptic seizures

    Energy Technology Data Exchange (ETDEWEB)

    Hively, L.M.; Clapp, N.E.; Daw, C.S.; Lawkins, W.F. [Oak Ridge National Lab., TN (United States); Eisenstadt, M.L. [Knoxville Neurology Clinic, St. Mary`s Medical Center, Knoxville, TN (United States)

    1995-04-01

    We apply chaotic time series analysis (CTSA) to human electroencephalogram (EEG) data. Three epoches were examined: epileptic seizure, non-seizure, and transition from non-seizure to seizure. The CTSA tools were applied to four forms of these data: raw EEG data (e-data), artifact data (f-data) via application of a quadratic zero-phase filter of the raw data, artifact-filtered data (g- data) and that was the residual after subtracting f-data from e-data, and a low-pass-filtered version (h-data) of g-data. Two different seizures were analyzed for the same patient. Several nonlinear measures uniquely indicate an epileptic seizure in both cases, including an abrupt decrease in the time per wave cycle in f-data, an abrupt increase in the Kolmogorov entropy and in the correlation dimension for e-h data, and an abrupt increase in the correlation dimension for e-h data. The transition from normal to seizure state also is characterized by distinctly different trends in the nonlinear measures for each seizure and may be potential seizure predictors for this patient. Surrogate analysis of e-data shows that statistically significant nonlinear structure is present during the non-seizure, transition , and seizure epoches.

  20. EEG beta suppression and low gamma modulation are different elements of human upright walking

    Directory of Open Access Journals (Sweden)

    Martin eSeeber

    2014-07-01

    Full Text Available Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source modeling to high-density (120 channels EEG recordings using individual anatomy reconstructed from structural magnetic resonance imaging (MRI scans. First, we analyzed amplitude differences between the conditions, walking and upright standing. Second, we investigated amplitude modulations related to the gait phase. During active walking upper µ (10-12Hz and β (18-30Hz oscillations were suppressed (event-related desynchronization, ERD compared to upright standing. Significant β ERD activity was located focally in central sensorimotor areas for 9/10 subjects. Additionally, we found that low γ (24-40Hz amplitudes were modulated related to the gait phase. Because there is a certain frequency band overlap between sustained β ERD and gait phase related modulations in the low γ range, these two phenomena are superimposed. Thus, we observe gait phase related amplitude modulations at a certain ERD level. We conclude that sustained µ and β ERD reflect a movement related state change of cortical excitability while gait phase related modulations in the low γ represent the motion sequence timing during gait. Interestingly, the center frequencies of sustained β ERD and gait phase modulated amplitudes were identified to be different. They may therefore be caused by different neuronal rhythms, which should be taken under consideration in future studies.

  1. EEG signal analysis: a survey.

    Science.gov (United States)

    Subha, D Puthankattil; Joseph, Paul K; Acharya U, Rajendra; Lim, Choo Min

    2010-04-01

    The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be extracted for the diagnosis of different diseases using advanced signal processing techniques. In this paper the effect of different events on the EEG signal, and different signal processing methods used to extract the hidden information from the signal are discussed in detail. Linear, Frequency domain, time - frequency and non-linear techniques like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H), different entropies, fractal dimension(FD), Higher Order Spectra (HOS), phase space plots and recurrence plots are discussed in detail using a typical normal EEG signal.

  2. Complex dynamics of epileptic EEG.

    Science.gov (United States)

    Kannathal, N; Puthusserypady, Sadasivan K; Choo Min, Lim

    2004-01-01

    Electroencephalogram (EEG) - the recorded representation of electrical activity of the brain contain useful information about the state of the brain. Recent studies indicate that nonlinear methods can extract valuable information from neuronal dynamics. We compare the dynamical properties of EEG signals of healthy subjects with epileptic subjects using nonlinear time series analysis techniques. Chaotic invariants like correlation dimension (D2) , largest Lyapunov exponent (lambda1), Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the signal. Our study showed clear differences in dynamical properties of brain electrical activity of the normal and epileptic subjects with a confidence level of more than 90%. Furthermore to support this claim fractal dimension (FD) analysis is performed. The results indicate reduction in value of FD for epileptic EEG indicating reduction in system complexity.

  3. 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.

  4. 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

  5. EEG based image encryption via quantum walks.

    Science.gov (United States)

    Rawat, N; Shin, Y; Balasingham, I

    2016-08-01

    An electroencephalogram (EEG) based image encryption combined with Quantum walks (QW) is encoded in Fresnel domain. The computational version of EEG randomizes the original plaintext whereas QW can serve as an excellent key generator due to its inherent nonlinear chaotic dynamic behavior. First, a spatially coherent monochromatic laser beam passes through an SLM, which introduces an arbitrary EEG phase-only mask. The modified beam is collected by a CCD. Further, the intensity is multiply with the QW digitally. EEG shows high sensitivity to system parameters and capable of encrypting and transmitting the data whereas QW has unpredictability, stability and non-periodicity. Only applying the correct keys, the original image can be retrieved successfully. Simulations and comparisons show the proposed method to be secure enough for image encryption and outperforms prior works. The proposed method opens the door towards introducing EEG and quantum computation into image encryption and promotes the convergence between our approach and image processing.

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

    Directory of Open Access Journals (Sweden)

    Umair J. Chaudhary

    2016-01-01

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

  7. Probability distributions of the electroencephalogram envelope of preterm infants.

    Science.gov (United States)

    Saji, Ryoya; Hirasawa, Kyoko; Ito, Masako; Kusuda, Satoshi; Konishi, Yukuo; Taga, Gentaro

    2015-06-01

    To determine the stationary characteristics of electroencephalogram (EEG) envelopes for prematurely born (preterm) infants and investigate the intrinsic characteristics of early brain development in preterm infants. Twenty neurologically normal sets of EEGs recorded in infants with a post-conceptional age (PCA) range of 26-44 weeks (mean 37.5 ± 5.0 weeks) were analyzed. Hilbert transform was applied to extract the envelope. We determined the suitable probability distribution of the envelope and performed a statistical analysis. It was found that (i) the probability distributions for preterm EEG envelopes were best fitted by lognormal distributions at 38 weeks PCA or less, and by gamma distributions at 44 weeks PCA; (ii) the scale parameter of the lognormal distribution had positive correlations with PCA as well as a strong negative correlation with the percentage of low-voltage activity; (iii) the shape parameter of the lognormal distribution had significant positive correlations with PCA; (iv) the statistics of mode showed significant linear relationships with PCA, and, therefore, it was considered a useful index in PCA prediction. These statistics, including the scale parameter of the lognormal distribution and the skewness and mode derived from a suitable probability distribution, may be good indexes for estimating stationary nature in developing brain activity in preterm infants. The stationary characteristics, such as discontinuity, asymmetry, and unimodality, of preterm EEGs are well indicated by the statistics estimated from the probability distribution of the preterm EEG envelopes. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  8. Electroencephalogram characteristics in patients with chronic fatigue syndrome

    Directory of Open Access Journals (Sweden)

    Wu T

    2016-01-01

    Full Text Available Tong Wu,1 Xianghua Qi,1 Yuan Su,2 Jing Teng,1 Xiangqing Xu11Internal Medicine-Neurology, Shandong Provincial Traditional Chinese Medical Hospital, 2School of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, Jinan, People’s Republic of ChinaObjective: To explore the electroencephalogram (EEG characteristics in patients with chronic fatigue syndrome (CFS using brain electrical activity mapping (BEAM and EEG nonlinear dynamical analysis.Methods: Forty-seven outpatients were selected over a 3-month period and divided into an observation group (24 outpatients and a control group (23 outpatients by using the non-probability sampling method. All the patients were given a routine EEG. The BEAM and the correlation dimension changes were analyzed to characterize the EEG features.Results: 1 BEAM results indicated that the energy values of δ, θ, and α1 waves significantly increased in the observation group, compared with the control group (P<0.05, P<0.01, respectively, which suggests that the brain electrical activities in CFS patients were significantly reduced and stayed in an inhibitory state; 2 the increase of δ, θ, and α1 energy values in the right frontal and left occipital regions was more significant than other encephalic regions in CFS patients, indicating the region-specific encephalic distribution; 3 the correlation dimension in the observation group was significantly lower than the control group, suggesting decreased EEG complexity in CFS patients.Conclusion: The spontaneous brain electrical activities in CFS patients were significantly reduced. The abnormal changes in the cerebral functions were localized at the right frontal and left occipital regions in CFS patients.Keywords: electrical activities, brain electrical activity mapping, nonlinear dynamical analysis

  9. Electroencephalograms in basilar artery migraine.

    Science.gov (United States)

    Parain, D; Samson-Dollfus, D

    1984-11-01

    Nine cases of 'basilar artery migraine' (BAM) have been recorded. In 8 cases, excess of beta activity was observed during the attacks and disappeared in less than 3 days. The inter-ictal EEGs were normal. Drug ingestion was excluded each time. These EEG patterns are different from those which have been reported in the literature, i.e., transitory posterior abnormal slow waves. However, case no.1 is in agreement with the literature. The expression 'BAM' probably covers different syndromes which are further discussed.

  10. Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Rajeev Sharma

    2015-02-01

    Full Text Available The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD method to extract intrinsic mode functions (IMFs. The entropy features, namely, average Shannon entropy (ShEnAvg, average Renyi’s entropy (RenEnAvg , average approximate entropy (ApEnAvg, average sample entropy (SpEnAvg and average phase entropies (S1Avg and S2Avg, are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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

    LENUS (Irish Health Repository)

    O'Sullivan, S S

    2012-02-03

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

  12. EEG Theta and Mu Oscillations during Perception of Human and Robot Actions

    Directory of Open Access Journals (Sweden)

    Burcu A. Urgen

    2013-11-01

    Full Text Available Perception of others’ actions supports important social skills, such as communication, intention understanding, and empathy. Are mechanisms of action processing in human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans so they can be used as stimuli to address such questions. Here, we recorded EEG during the observation of human and robot actions. Sensorimotor mu (8-13 Hz rhythm has been linked to the motor simulation aspect of action processing (and to human mirror neuron system, MNS and frontal theta (4-8 Hz rhythm to semantic and memory-related aspects. We explored whether these measures exhibit selectivity for biological entities: for whether the motion and/or the visual appearance of the observed agent is biological. Participants watched videos of three agents performing the same actions. The first was a Human, and had biological motion and appearance. The other two were a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical motion and appearance. Observation of all agents induced significant attenuation in the power of mu oscillations that was equivalent for all agents. Thus, mu suppression, considered an index of the activity of the MNS, did not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta activity thus appears to be sensitive to visual appearance, suggesting artificial agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience thus can allow us to explore functional properties of action processing on the one hand, and help inform the design of social robots on

  13. Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

    Institute of Scientific and Technical Information of China (English)

    You Rong-Yi; Chen Zhong

    2005-01-01

    Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.

  14. Blind source separation of multichannel electroencephalogram based on wavelet transform and ICA

    Science.gov (United States)

    You, Rong-Yi; Chen, Zhong

    2005-11-01

    Combination of the wavelet transform and independent component analysis (ICA) was employed for blind source separation (BSS) of multichannel electroencephalogram (EEG). After denoising the original signals by discrete wavelet transform, high frequency components of some noises and artifacts were removed from the original signals. The denoised signals were reconstructed again for the purpose of ICA, such that the drawback that ICA cannot distinguish noises from source signals can be overcome effectively. The practical processing results showed that this method is an effective way to BSS of multichannel EEG. The method is actually a combination of wavelet transform with adaptive neural network, so it is also useful for BBS of other complex signals.

  15. [EEG manifestations in metabolic encephalopathy].

    Science.gov (United States)

    Lin, Chou-Ching K

    2005-09-01

    Normal brain function depends on normal neuronal metabolism, which is closely related to systemic homeostasis of metabolites, such as glucose, electrolytes, amino acids and ammonia. "Metabolic encephalopathy" indicates diffuse brain dysfunction caused by various systemic derangements. Electroencephalogram (EEG) is widely used to evaluate metabolic encephalopathy since 1937, when Berger first observed slow brain activity induced by hypoglycemia. EEG is most useful in differentiating organic from psychiatric conditions, identifying epileptogenicity, and providing information about the degree of cortical or subcortical dysfunction. In metabolic encephalopathy, EEG evolution generally correlates well with the severity of encephalopathy. However, EEG has little specificity in differentiating etiologies in metabolic encephalopathy. For example, though triphasic waves are most frequently mentioned in hepatic encephalopathy, they can also be seen in uremic encephalopathy, or even in aged psychiatric patients treated with lithium. Spike-and-waves may appear in hyper- or hypo-glycemia, uremic encephalopathy, or vitamin deficiencies, etc. Common principles of EEG changes in metabolic encephalopathy are (1) varied degrees of slowing, (2) assorted mixtures of epileptic discharge, (3) high incidence of triphasic waves, and (4), as a rule, reversibility after treatment of underlying causes. There are some exceptions to the above descriptions in specific metabolic disorders and EEG manifestations are highly individualized.

  16. The genetic basis of the beta power in the resting electroencephalogram of schizophrenic patients

    OpenAIRE

    Opitz, Damaris

    2011-01-01

    This association study builds on research demonstrating that schizophrenic patients have increased beta activity compared to healthy subjects. Moreover, huge collaborative studies investigating alcoholic patients showed that the increased beta power of these patients in resting electroencephalogram (EEG) is linked to GABA-A receptor genes. More specifically research in this field identified an association of increased beta power with a single nucleotide polymorphism (SNP) rs279...

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

    Science.gov (United States)

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

    2016-06-16

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Kyungsoo Kim

    2016-06-01

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

  20. Electroencephalogram measurement using polymer-based dry microneedle electrode

    Science.gov (United States)

    Arai, Miyako; Nishinaka, Yuya; Miki, Norihisa

    2015-06-01

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

  1. Entropy Analysis as an Electroencephalogram Feature Extraction Method

    Directory of Open Access Journals (Sweden)

    P. I. Sotnikov

    2014-01-01

    Full Text Available The aim of this study was to evaluate a possibility for using an entropy analysis as an electroencephalogram (EEG feature extraction method in brain-computer interfaces (BCI. The first section of the article describes the proposed algorithm based on the characteristic features calculation using the Shannon entropy analysis. The second section discusses issues of the classifier development for the EEG records. We use a support vector machine (SVM as a classifier. The third section describes the test data. Further, we estimate an efficiency of the considered feature extraction method to compare it with a number of other methods. These methods include: evaluation of signal variance; estimation of spectral power density (PSD; estimation of autoregression model parameters; signal analysis using the continuous wavelet transform; construction of common spatial pattern (CSP filter. As a measure of efficiency we use the probability value of correctly recognized types of imagery movements. At the last stage we evaluate the impact of EEG signal preprocessing methods on the final classification accuracy. Finally, it concludes that the entropy analysis has good prospects in BCI applications.

  2. EEG theta and Mu oscillations during perception of human and robot actions.

    Science.gov (United States)

    Urgen, Burcu A; Plank, Markus; Ishiguro, Hiroshi; Poizner, Howard; Saygin, Ayse P

    2013-01-01

    The perception of others' actions supports important skills such as communication, intention understanding, and empathy. Are mechanisms of action processing in the human brain specifically tuned to process biological agents? Humanoid robots can perform recognizable actions, but can look and move differently from humans, and as such, can be used in experiments to address such questions. Here, we recorded EEG as participants viewed actions performed by three agents. In the Human condition, the agent had biological appearance and motion. The other two conditions featured a state-of-the-art robot in two different appearances: Android, which had biological appearance but mechanical motion, and Robot, which had mechanical appearance and motion. We explored whether sensorimotor mu (8-13 Hz) and frontal theta (4-8 Hz) activity exhibited selectivity for biological entities, in particular for whether the visual appearance and/or the motion of the observed agent was biological. Sensorimotor mu suppression has been linked to the motor simulation aspect of action processing (and the human mirror neuron system, MNS), and frontal theta to semantic and memory-related aspects. For all three agents, action observation induced significant attenuation in the power of mu oscillations, with no difference between agents. Thus, mu suppression, considered an index of MNS activity, does not appear to be selective for biological agents. Observation of the Robot resulted in greater frontal theta activity compared to the Android and the Human, whereas the latter two did not differ from each other. Frontal theta thus appears to be sensitive to visual appearance, suggesting agents that are not sufficiently biological in appearance may result in greater memory processing demands for the observer. Studies combining robotics and neuroscience such as this one can allow us to explore neural basis of action processing on the one hand, and inform the design of social robots on the other.

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

    Science.gov (United States)

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

    2015-01-01

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

  4. Comparison of sleep-wake classification using electroencephalogram and wrist-worn multi-modal sensor data.

    Science.gov (United States)

    Sano, Akane; Picard, Rosalind W

    2014-01-01

    This paper presents the comparison of sleep-wake classification using electroencephalogram (EEG) and multi-modal data from a wrist wearable sensor. We collected physiological data while participants were in bed: EEG, skin conductance (SC), skin temperature (ST), and acceleration (ACC) data, from 15 college students, computed the features and compared the intra-/inter-subject classification results. As results, EEG features showed 83% while features from a wrist wearable sensor showed 74% and the combination of ACC and ST played more important roles in sleep/wake classification.

  5. Emotion recognition from EEG using higher order crossings.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

    2010-03-01

    Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion

  6. Progress in EEG-Based Brain Robot Interaction Systems

    Directory of Open Access Journals (Sweden)

    Xiaoqian Mao

    2017-01-01

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

  7. Progress in EEG-Based Brain Robot Interaction Systems

    Science.gov (United States)

    Li, Mengfan; Niu, Linwei; Xian, Bin; Zeng, Ming; Chen, Genshe

    2017-01-01

    The most popular noninvasive Brain Robot Interaction (BRI) technology uses the electroencephalogram- (EEG-) based Brain Computer Interface (BCI), to serve as an additional communication channel, for robot control via brainwaves. This technology is promising for elderly or disabled patient assistance with daily life. The key issue of a BRI system is to identify human mental activities, by decoding brainwaves, acquired with an EEG device. Compared with other BCI applications, such as word speller, the development of these applications may be more challenging since control of robot systems via brainwaves must consider surrounding environment feedback in real-time, robot mechanical kinematics, and dynamics, as well as robot control architecture and behavior. This article reviews the major techniques needed for developing BRI systems. In this review article, we first briefly introduce the background and development of mind-controlled robot technologies. Second, we discuss the EEG-based brain signal models with respect to generating principles, evoking mechanisms, and experimental paradigms. Subsequently, we review in detail commonly used methods for decoding brain signals, namely, preprocessing, feature extraction, and feature classification, and summarize several typical application examples. Next, we describe a few BRI applications, including wheelchairs, manipulators, drones, and humanoid robots with respect to synchronous and asynchronous BCI-based techniques. Finally, we address some existing problems and challenges with future BRI techniques. PMID:28484488

  8. Gender differences in hemispheric organization during divergent thinking: an EEG investigation in human subjects.

    Science.gov (United States)

    Razumnikova, Olga M

    2004-05-27

    This study examined the gender-related differences in EEG patterns during the experimental condition of divergent thinking. The EEG of 36 males and 27 females was recorded from 16 scalp electrodes in rest and while students were solving a creative problem. The spectral power density along with EEG coherence estimates were analyzed in each of the six frequency bands in the 4-30 Hz range. Gender-related differences in the EEG patterns were found during successful divergent thinking. Creative men were characterized by massive increases of amplitude and interhemispheric coherence in the beta2 whereas creative women showed more local increases of the beta2 power and coherence. On the contrary, the task-induced desynchronization of the alpha1 rhythm in creative women was topographically more expanded as compared with men who demonstrated greater interhemispheric coherence than women did. Our results propose a different hemispheric organization in men and women during creative thinking.

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

    Directory of Open Access Journals (Sweden)

    Fabrizio De Vico Fallani

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

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

    CERN Document Server

    Fallani, F De Vico; Sinatra, R; Astolfi, L; Cincotti, F; Mattia, D; Wilke, C; Doud, A; Latora, V; He, B; Babiloni, F; 10.1371/journal.pone.0014187

    2011-01-01

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

  11. [Individual alpha activity of electroencephalogram and nonverbal creativity].

    Science.gov (United States)

    Bazanova, O M; Aftanas, L I

    2007-01-01

    The main objective of present correlational investigation was to clarify relationships between nonverbal creativity indices and individual electroencephalogram alpha activity indices: individual alpha peak frequency, alpha band width, magnitude of alpha desynchronization and alpha spindle indices such as duration, amplitude, variability and skewness. The EEG was recorded in 98 healthy male right-handed subjects. Scores of nonverbal creativity (i. e., fluency, originality, and flexibility) were assessed using the Torrance test of nonverbal performance. The study showed that fluency in creative performance was associated with individual alpha peak frequency and alpha spindles duration, whereas originality and plasticity--with individual alpha band width and spindle amplitude variability. The findings also show that both highest and lowest individual alpha peak frequency indices are associated with enhanced scores of originality. It is suggested that individual alpha activity indices could be presented as individual predictors of fluency, plasticity and originality of nonverbal creativity.

  12. Comparison of the effects of continuous and pulsed mobile phone like RF exposure on the human EEG.

    Science.gov (United States)

    Perentos, N; Croft, R J; McKenzie, R J; Cvetkovic, D; Cosic, I

    2007-12-01

    It is not clear yet whether Global System for Mobiles (GSM) mobile phone radiation has the ability to interfere with normal resting brain function. There have been reports that GSM exposure increases alpha band power, and does so only when the signal is modulated at low frequencies (Huber, R., Treyer, V., Borbely, A. A., Schuderer, J., Gottselig, J. M., Landolt, H.P., Werth, E., Berthold,T., Kuster, N., Buck, A and Achermann, P. Electromagnetic fields, such as those from mobile phones, alter regional cerebral blood flow and sleep and waking EEG. J Sleep Res 11, 289-295, 2002.) However, as that research employed exposure distributions that are not typical of normal GSM handset usage (deep brain areas were overexposed), it remains to be determined whether a similar result patterning would arise from a more representative exposure. In this fully counterbalanced cross-over design, we recruited 12 participants and tried to replicate the modulation linked post exposure alpha band power increase described above, but with an exposure source (dipole antenna) more closely resembling that of a real GSM handset. Exposures lasted for 15 minutes. No changes to alpha power were found for either modulated or unmodulated radiofrequency fields, and thus we failed to replicate the above results. Possible reasons for this failure to replicate are discussed, with the main reason argued to be the lower and more representative exposure distribution employed in the present study. In addition we investigated the possible GSM exposure related effects on the non-linear features of the resting electroencephalogram using the Approximate Entropy (ApEn) method of analysis. Again, no effect was demonstrated for either modulated or unmodulated radiofrequency exposures.

  13. WEBspike: A New Proposition of Deterministic Finite Automata and Parallel Algorithm Based Web Application for EEG Spike Recognition

    Directory of Open Access Journals (Sweden)

    Anup Kumar Keshri

    2013-10-01

    Full Text Available The brain signal or Electroencephalogram (EEG has been proved as one of the most important bio-signal that deals with a number of problems and disorders related to the human being. Epilepsy is one of the most commonly known disorders found in humans. The application of EEG in epilepsy related research and treatment is now a very common practice. Variety of smart tools and algorithms exist to assist the experts in taking decision related to the treatment to be provided to an epileptic patient. However, web based applications or tools are still needed that can assist those doctors and experts, who are not having such existing smart tools for EEG analysis with them. In the current work, a web based system named WEBspike has been proposed that breaks the geographical boundary in assisting doctors in taking proper and fast decision regarding the treatment of epileptic patient. The proposed system receives the EEG data from various users through internet and processes it for Epileptic Spike (ES patterns present in it. It sends back a report to the user regarding the appearance of ES pattern present in the submitted EEG data. The average spike recognition rate obtained by the system with the test files, was 99.09% on an average.

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

    NARCIS (Netherlands)

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

    2011-01-01

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

  15. Effects of Drawing on Alpha Activity: A Quantitative EEG Study with Implications for Art Therapy

    Science.gov (United States)

    Belkofer, Christopher M.; Van Hecke, Amy Vaughan; Konopka, Lukasz M.

    2014-01-01

    Little empirical evidence exists as to how materials used in art therapy affect the brain and its neurobiological functioning. This pre/post within-groups study utilized the quantitative electroencephalogram (qEEG) to measure residual effects in the brain after 20 minutes of drawing. EEG recordings were conducted before and after participants (N =…

  16. Effects of Drawing on Alpha Activity: A Quantitative EEG Study with Implications for Art Therapy

    Science.gov (United States)

    Belkofer, Christopher M.; Van Hecke, Amy Vaughan; Konopka, Lukasz M.

    2014-01-01

    Little empirical evidence exists as to how materials used in art therapy affect the brain and its neurobiological functioning. This pre/post within-groups study utilized the quantitative electroencephalogram (qEEG) to measure residual effects in the brain after 20 minutes of drawing. EEG recordings were conducted before and after participants (N =…

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

    Directory of Open Access Journals (Sweden)

    Kei Omata

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

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

    Science.gov (United States)

    Horne, Jim

    2013-11-01

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

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

    Science.gov (United States)

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

    2016-01-01

    One of the major challenges in vision research is to analyze the effect of visual stimuli on human vision. However, no relationship has been yet discovered between the structure of the visual stimulus, and the structure of fixational eye movements. This study reveals the plasticity of human fixational eye movements in relation to the ‘complex’ visual stimulus. We demonstrated that the fractal temporal structure of visual dynamics shifts towards the fractal dynamics of the visual stimulus (image). The results showed that images with higher complexity (higher fractality) cause fixational eye movements with lower fractality. Considering the brain, as the main part of nervous system that is engaged in eye movements, we analyzed the governed Electroencephalogram (EEG) signal during fixation. We have found out that there is a coupling between fractality of image, EEG and fixational eye movements. The capability observed in this research can be further investigated and applied for treatment of different vision disorders. PMID:27217194

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

    Science.gov (United States)

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

    2016-05-01

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

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

    OpenAIRE

    Bell, Martha Ann; Cuevas, Kimberly

    2012-01-01

    Developmental research is enhanced by use of multiple methodologies for examining psychological processes. The electroencephalogram (EEG) is an efficient and relatively inexpensive method for the study of developmental changes in brain-behavior relations. In this review, we highlight some of the challenges for using EEG in cognitive development research. We also list best practices for incorporating this methodology into the study of early cognitive processes. Consideration of these issues is...

  2. Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study.

    Science.gov (United States)

    Zhou, G; Liu, P; He, J; Dong, M; Yang, X; Hou, B; Von Deneen, K M; Qin, W; Tian, J

    2012-01-27

    Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.

  3. Recognition of Words from the EEG Laplacian

    CERN Document Server

    de Barros, J Acacio; de Mendonça, J P R F; Suppes, P

    2012-01-01

    Recent works on the relationship between the electro-encephalogram (EEG) data and psychological stimuli show that EEG recordings can be used to recognize an auditory stimulus presented to a subject. The recognition rate is, however, strongly affected by technical and physiological artifacts. In this work, subjects were presented seven auditory simuli in the form of English words (first, second, third, left, right, yes, and no), and the time-locked electric field was recorded with a 64 channel Neuroscan EEG system. We used the surface Laplacian operator to eliminate artifacts due to sources located at regions far from the electrode. Our intent with the Laplacian was to improve the recognition rates of auditory stimuli from the electric field. To compute the Laplacian, we used a spline interpolation from spherical harmonics. The EEG Laplacian of the electric field were average over trials for the same auditory stimulus, and with those averages we constructed prototypes and test samples. In addition to the Lapla...

  4. [An Electroencephalogram-driven Personalized Affective Music Player System: Algorithms and Preliminary Implementation].

    Science.gov (United States)

    Ma, Yong; Li, Juan; Lu, Bin

    2016-02-01

    In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience's emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.

  5. Amplitude-integrated electroencephalogram 1 h after birth in a preterm infant with cystic periventricular leukomalacia.

    Science.gov (United States)

    Kato, Toru; Okumura, Akihisa; Hayakawa, Fumio; Tsuji, Takeshi; Hayashi, Seiji; Natsume, Jun

    2013-01-01

    We report a preterm infant, who showed abnormal amplitude-integrated electroencephalogram (aEEG) findings 1 h after birth and later developed cystic periventricular leukomalacia (PVL). The patient was a girl with a gestational age of 29 weeks. She was delivered by emergency cesarean section because of placental abruption and intrauterine co-twin demise. Artificial ventilation and administration of surfactant were needed to treat respiratory distress syndrome. Her cardiovascular condition was stable with artificial ventilation. Cranial ultrasonography showed extended cystic PVL after 11 days of age. aEEG 1 h after birth showed a consistently inactive pattern that resolved completely 28 h after birth. The neurophysiological findings of this patient suggest that aEEG findings during the very early period after birth provide significant information for predicting PVL.

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

    CERN Document Server

    Timashev, Serge F; Polyakov, Yuriy S; Demin, Sergey A; Kaplan, Alexander Ya

    2011-01-01

    We apply flicker-noise spectroscopy (FNS), a time series analysis method operating on structure functions and power spectrum estimates, to study the clinical electroencephalogram (EEG) signals recorded in children/adolescents (11 to 14 years of age) with diagnosed schizophrenia-spectrum symptoms at the National Center for Psychiatric Health (NCPH) of the Russian Academy of Medical Sciences. The EEG signals for these subjects were compared with the signals for a control sample of chronically depressed children/adolescents. The purpose of the study is to look for diagnostic signs of subjects' susceptibility to schizophrenia in the FNS parameters for specific electrodes and cross-correlations between the signals simultaneously measured at different points on the scalp. Our analysis of EEG signals from scalp-mounted electrodes at locations F3 and F4, which are symmetrically positioned in the left and right frontal areas of cerebral cortex, respectively, demonstrates an essential role of frequency-phase synchroniz...

  7. Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

    Directory of Open Access Journals (Sweden)

    Chi Zhang

    2015-01-01

    Full Text Available Electroencephalogram (EEG is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA, wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

  8. Spatiotemporal Analysis of Multichannel EEG: CARTOOL

    OpenAIRE

    Denis Brunet; Murray, Micah M; Michel, Christoph M.

    2011-01-01

    This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous resul...

  9. Concurrent EEG And NIRS Tomographic Imaging Based on Wearable Electro-Optodes

    Science.gov (United States)

    2014-04-13

    simultaneous electroencephalogram ( EEG ) and functional NIR spectroscopic (fNIRS) acquisition for biological or cognitive neuroscience studies in operational...environments. The system features novel EEG /NIRS electrodes, known as electro-opodes, and miniaturized supporting hardware/software. In the past few...years, our team, composed of faculty, postdoctoral fellows and graduate students, has designed and developed dry EEG and fNIR sensors that allow non

  10. Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features.

    Science.gov (United States)

    Kashefpoor, Masoud; Rabbani, Hossein; Barekatain, Majid

    2016-01-01

    Alzheimer's disease (AD) is one of the most expensive and fatal diseases in the elderly population. Up to now, no cure have been found for AD, so early stage diagnosis is the only way to control it. Mild cognitive impairment (MCI) usually is the early stage of AD which is defined as decreasing in mental abilities such a cognition, memory, and speech not too severe to interfere daily activities. MCI diagnosis is rather hard and usually assumed as normal consequences of aging. This study proposes an accurate, mobile, and nonexpensive diagnostic approach based on electroencephalogram (EEG) signal. EEG signals were recorded using 19 electrodes positioned according to the 10-20 International system at resting eyes closed state from 16 normal and 11 MCI participants. Nineteen Spectral features are computed for each channel and examined using a correlation based algorithm to select the best discriminative features. Selected features are classified using a combination of neurofuzzy system and k-nearest neighbor classifier. Final results reach 88.89%, 100%, and 83.33% for accuracy, sensitivity, and specificity, respectively, which shows the potential of proposed method to be used as an MCI diagnostic tool, especially for screening a large population.

  11. [Clinical and electroencephalogram study of 5 children with hypothalamic hamartoma].

    Science.gov (United States)

    Otsuka, Eiko; Oguni, Hirokazu; Funatsuka, Makoto; Usugi, Tomoko; Nakayama, Tomohiro; Hayashi, Kitami; Nagaki, Shigeru; Osawa, Makiko; Ono, Yuko; Yamane, Fumitaka; Hori, Tomokatsu

    2005-09-01

    We retrospectively studied 5 children with hypothalamic hamartoma (HH) to elucidate the clinical, neuroimaging and electroencephalogram (EEG) characteristics of this disorder. In all cases, high resolution MRI scans demonstrated an intrahypothalamic mass protruding into the 3rd ventricle. An initial symptom was epileptic attack in 4 cases and precocious puberty in the remaining one. Gelastic seizures developed in 4 of 5 patients at ranging from 2 days to 11 years of age. The ictal EEGs during the gelastic seizures showed diffuse attenuation of background activity, followed by rhythmic slow discharges either diffusely or in the central area. Gamma-knife radiosurgery was performed on 2 cases whose seizures were resistant to available antiepileptic drugs. One of the 2 patients was responded significantly to this treatment, showing the disappearance of combined attacks and a marked reduction of the generalized spike-waves discharges. A more aggressive therapy, including gamma-knife radiosurgery and surgical treatment, should be considered for patients whose seizures are resistant to the medical treatment and causing deterioration of intelligence and behavioral problem.

  12. A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

    Science.gov (United States)

    Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

    2016-08-01

    Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

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

    Science.gov (United States)

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

    2010-03-01

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

  14. Continuous emotion detection using EEG signals and facial expressions

    NARCIS (Netherlands)

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

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

  15. Slow Sphering to Suppress Non-Stationaries in the EEG

    NARCIS (Netherlands)

    Reuderink, Boris; Farquhar, Jason; Poel, Mannes

    2011-01-01

    Non-stationary signals are ubiquitous in electroencephalogram (EEG) signals and pose a problem for robust application of brain-computer interfaces (BCIs). These non-stationarities can be caused by changes in neural background activity. We present a dynamic spatial filter based on time local whitenin

  16. Expression of behaviour and psychoactive drugs in the rat EEG

    NARCIS (Netherlands)

    Lier, Hester van

    2004-01-01

    Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to

  17. Expression of behaviour and psychoactive drugs in the rat EEG

    NARCIS (Netherlands)

    Lier, Hester van

    2004-01-01

    Brain activity and behaviour are related to each other. Psychoactive drugs can influence both brain activity and behaviour. In order to be able to understand the interplay between brain activity as measured by the electroencephalogram (EEG), behaviour, and psychoactive drugs, it is not sufficient to

  18. Battery-Less Electroencephalogram System Architecture Optimization

    Science.gov (United States)

    2016-12-01

    ARL-TR-7909•DEC 2016 US Army Research Laboratory Battery-Less ElectroencephalogramSystem Architecture Optimization by Peter Gadfort and Renooka...DEC 2016 US Army Research Laboratory Battery-Less ElectroencephalogramSystem Architecture Optimization by Peter GadfortSensors and Electron Devices... Architecture Optimization Peter Gadfort and Renooka Karmarkar ARL-TR-7909 Approved for public release; distribution is unlimited. 13 June 2016 – 26 September

  19. Value of electroencephalogram in prediction and diagnosis of vasospasm after intracranial aneurysm rupture.

    Science.gov (United States)

    Rivierez, M; Landau-Ferey, J; Grob, R; Grosskopf, D; Philippon, J

    1991-01-01

    The Electroencephalogram (EEG) of 151 patients whose ruptured aneurysm was confirmed by CT scan and angiography was recorded on the first day (D1) and the fifth day (D5). On D1, EEG had a prognostic value: among 46 patients with normal EEG, 72% presented neither further electrical ischaemic features nor delayed angiographic vasospasm; on the other hand, when bilateral bursts of slow waves, "axial bursts" or slow delta waves were recorded (78 cases), 97% exhibited EEG signs of ischaemia and angiographic vasospasm a few days later. These data were clearly related to the importance of the haemorrhage, specially when thick clots in the subarachnoid cisterns were found on the CT scan. On D5, EEG had a diagnostic value: focal or asymetrical bilateral delta waves occurring at that date seemed to correspond to ischaemia; among 107 patients with these electrical features, an angiographic vasospasm appeared in 96% of cases, and the importance of electrical abnormalities could be related to the degree of arterial narrowing. We conclude that EEG data are very useful in prediction as well in recognition of post-subarachnoid haemorrhage ischaemia due to vasospasm and are sufficiently precise to postpone control angiography and operation, when delayed surgery is programmed.

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

    Directory of Open Access Journals (Sweden)

    Cervantes Blanco Jorge Mauricio

    2014-07-01

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

  1. The effect of acupuncture at PC-6 on the electroencephalogram and electrocardiogram.

    Science.gov (United States)

    Kim, Min Soo; Kim, Hak Dong; Seo, Hee Don; Sawada, Kazuaki; Ishida, Makoto

    2008-01-01

    The present study aims to examine the effect of acupuncture stimulation of an acupuncture point (PC-6) and nonacupuncture point on electroencephalograms (EEGs) and electrocardiograms (ECGs). We used EEG in 10 healthy subjects to investigate cortical activation during stimulation of acupuncture points (neiguan: PC-6) and nonacupuncture points. Our most interesting finding was the marked differences of amplitude of EEG power between acupuncture points and nonacupuncture points stimulation. Wavelet transform was used as the EEG signal processing method, because it has advantages in a time domain and frequency domain characteristics analysis. EEGs were collected from 16 channels, and the alpha-wave (8-13 Hz), beta-wave (13-30 Hz), theta-wave (4-8 Hz) and delta-wave (0.5-4 Hz) were used as standards for frequency bands. According to the experiment results, EEG signals increased considerably after acupuncture stimulation; in each frequency band, the average amplitude was higher after acupuncture stimulation; ECG heart rates were faster by at least 10% after acupuncture stimulation. Consequently, it will be possible to verify the function of acupuncture stimulation on neiguan (acupuncture points; PC-6) more effectively.

  2. Decoding hand movement velocity from electroencephalogram signals during a drawing task

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    Gu Zhenghui

    2010-10-01

    Full Text Available Abstract Background Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG and electroencephalogram (EEG. Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear. Methods Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm. Results The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity. Conclusions These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.

  3. 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

    BACKGROUND: The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG...... measurement and real-time signal processing. METHOD: Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures...

  4. Computer assisted interpretation of the human EEG: improving diagnostic efficiency and consistency in clinical reviews

    NARCIS (Netherlands)

    Lodder, Shaun Sandy

    2014-01-01

    Scalp electroencephalography (EEG) measures brain activity non-invasively by using electrodes on the scalp and capturing small electrical fluctuations caused by the firing of neurons. From these recordings, a clinical neurophysiologist can study the captured patterns and waveforms and determine if a

  5. Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors

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    Hsuan-Chin Chu

    2013-08-01

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

  6. EEG reactions of the human brain in the gradient magnetic field zone of the active geological fault (pilot study)

    Science.gov (United States)

    Pobachenko, S. V.; Shitov, A. V.; Grigorjev, P. E.; Sokolov, M. V.; Zubrilkin, A. I.; Vypiraylo, D. N.; Solovjev, A. V.

    2016-12-01

    This paper presents the results of experimental studies of the dynamics of the functional state of a person within the zone of an active geological fault characterized by abnormal spatial distribution of the magnetic- field vector values. It is shown that these geophysical modifications have a pronounced effect on the fluctuations of the electrical activity of the human brain. When the person gets into a zone with abnormal levels of gradient magnetic field in the absence of any subjective sensations, a nonspecific orientation activation reaction is observed, which is characterized by a significant increase in the levels of peak performance in key functional EEG frequency bands.

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

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    Gaelle Bettus

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

  8. EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

    Science.gov (United States)

    Phothisonothai, Montri; Nakagawa, Masahiro

    In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left-and right- hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

  9. Electroencephalogram approximate entropy influenced by both age and sleep

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    Gerick M. H. Lee

    2013-12-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2016-04-01

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

  12. Prognostic Accuracy of Electroencephalograms in Preterm Infants

    DEFF Research Database (Denmark)

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

    2017-01-01

    (267 infants). Any aEEG background abnormality was a predictor of abnormal outcome. For prediction of a developmental quotient ...CONTEXT: Brain injury is common in preterm infants, and predictors of neurodevelopmental outcome are relevant. OBJECTIVE: To assess the prognostic test accuracy of the background activity of the EEG recorded as amplitude-integrated EEG (aEEG) or conventional EEG early in life in preterm infants...... for predicting neurodevelopmental outcome. DATA SOURCES: The Cochrane Library, PubMed, Embase, and the Cumulative Index to Nursing and Allied Health Literature. STUDY SELECTION: We included observational studies that had obtained an aEEG or EEG within 7 days of life in preterm infants and reported...

  13. Adenosine deaminase polymorphism affects sleep EEG spectral power in a large epidemiological sample.

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    Diego Robles Mazzotti

    Full Text Available Slow wave oscillations in the electroencephalogram (EEG during sleep may reflect both sleep need and intensity, which are implied in homeostatic regulation. Adenosine is strongly implicated in sleep homeostasis, and a single nucleotide polymorphism in the adenosine deaminase gene (ADA G22A has been associated with deeper and more efficient sleep. The present study verified the association between the ADA G22A polymorphism and changes in sleep EEG spectral power (from C3-A2, C4-A1, O1-A2, and O2-A1 derivations in the Epidemiologic Sleep Study (EPISONO sample from São Paulo, Brazil. Eight-hundred individuals were subjected to full-night polysomnography and ADA G22A genotyping. Spectral analysis of the EEG was carried out in all individuals using fast Fourier transformation of the signals from each EEG electrode. The genotype groups were compared in the whole sample and in a subsample of 120 individuals matched according to ADA genotype for age, gender, body mass index, caffeine intake status, presence of sleep disturbance, and sleep-disturbing medication. When compared with homozygous GG genotype carriers, A allele carriers showed higher delta spectral power in Stage 1 and Stages 3+4 of sleep, and increased theta spectral power in Stages 1, 2 and REM sleep. These changes were seen both in the whole sample and in the matched subset. The higher EEG spectral power indicates that the sleep of individuals carrying the A allele may be more intense. Therefore, this polymorphism may be an important source of variation in sleep homeostasis in humans, through modulation of specific components of the sleep EEG.

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

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    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

  15. 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.

  16. EEG sensor based classification for assessing psychological stress.

    Science.gov (United States)

    Begum, Shahina; Barua, Shaibal

    2013-01-01

    Electroencephalogram (EEG) reflects the brain activity and is widely used in biomedical research. However, analysis of this signal is still a challenging issue. This paper presents a hybrid approach for assessing stress using the EEG signal. It applies Multivariate Multi-scale Entropy Analysis (MMSE) for the data level fusion. Case-based reasoning is used for the classification tasks. Our preliminary result indicates that EEG sensor based classification could be an efficient technique for evaluation of the psychological state of individuals. Thus, the system can be used for personal health monitoring in order to improve users health.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-06-15

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

  18. EEG topography and tomography (LORETA) in the classification and evaluation of the pharmacodynamics of psychotropic drugs.

    Science.gov (United States)

    Saletu, Bernd; Anderer, Peter; Saletu-Zyhlarz, Gerda M

    2006-04-01

    By multi-lead computer-assisted quantitative analyses of human scalp-recorded electroencephalogram (QEEG) in combination with certain statistical procedures (quantitative pharmaco-EEG) and mapping techniques (pharmaco-EEG mapping or topography), it is possible to classify psychotropic substances and objectively evaluate their bioavailability at the target organ, the human brain. Specifically, one may determine at an early stage of drug development whether a drug is effective on the central nervous system (CNS) compared with placebo, what its clinical efficacy will be like, at which dosage it acts, when it acts and the equipotent dosages of different galenic formulations. Pharmaco-EEG maps of neuroleptics, antidepressants, tranquilizers, hypnotics, psychostimulants and nootropics/cognition-enhancing drugs will be described. Methodological problems, as well as the relationships between acute and chronic drug effects, alterations in normal subjects and patients, CNS effects and therapeutic efficacy will be discussed. Imaging of drug effects on the regional brain electrical activity of healthy subjects by means of EEG tomography such as low-resolution electromagnetic tomography (LORETA) has been used for identifying brain areas predominantly involved in psychopharmacological action. This will be shown for the representative drugs of the four main psychopharmacological classes, such as 3 mg haloperidol for neuroleptics, 20 mg citalopram for antidepressants, 2 mg lorazepam for tranquilizers and 20 mg methylphenidate for psychostimulants. LORETA demonstrates that these psychopharmacological classes affect brain structures differently. By considering these differences between psychotropic drugs and placebo in normal subjects, as well as between mental disorder patients and normal controls, it may be possible to choose the optimum drug for a specific patient according to a key-lock principle, since the drug should normalize the deviant brain function. Thus, pharmaco-EEG

  19. Quantitative EEG parameters correlate with the progression of human prion diseases

    Science.gov (United States)

    Wehner, Tim; Lowe, Jessica; Porter, Marie-Claire; Kenny, Joanna; Thompson, Andrew; Rudge, Peter; Collinge, John; Mead, Simon

    2016-01-01

    Background Prion diseases are universally fatal and often rapidly progressive neurodegenerative diseases. EEG has long been used in the diagnosis of sporadic Creutzfeldt-Jakob disease; however, the characteristic waveforms do not occur in all types of prion diseases. Here, we re-evaluate the utility of EEG by focusing on the development of biomarkers. We test whether abnormal quantitative EEG parameters can be used to measure disease progression in prion diseases or predict disease onset in healthy individuals at risk of disease. Methods In the National Prion Monitoring Cohort study, we did quantitative encephalography on 301 occasions in 29 healthy controls and 67 patients with prion disease. The patients had either inherited prion disease or sporadic Creutzfeldt-Jakob disease. We computed the main background frequency, the α and θ power and the α/θ power ratio, then averaged these within 5 electrode groups. These measurements were then compared among participant groups and correlated with functional and cognitive scores cross-sectionally and longitudinally. Results We found lower main background frequency, α power and α/θ power ratio and higher θ power in patients compared to control participants. The main background frequency, the power in the α band and the α/θ power ratio also differed in a consistent way among the patient groups. Moreover, the main background frequency and the α/θ power ratio correlated significantly with functional and cognitive scores. Longitudinally, change in these parameters also showed significant correlation with the change in clinical and cognitive scores. Conclusions Our findings support the use of quantitative EEG to follow the progression of prion disease, with potential to help evaluate the treatment effects in future clinical-trials. PMID:27413165

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

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    Susanti Halim

    2015-11-01

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

  1. An effective correlation dimension and burst suppression ratio of the EEG in rat. Correlation with sevoflurane induced anaesthetic depth

    NARCIS (Netherlands)

    Broek, P.L.C. van den; Rijn, C.M. van; Egmond, J. van; Coenen, A.M.L.; Booij, L.H.D.J.

    2006-01-01

    Although brain signals measured under the skull (electrocorticogram, ECoG) and signals measured on top of the scalp (electroencephalogram, EEG) stem from the same brain activity, they are different. We investigated how we can produce EEG when we know ECoG ("forward problem") and how we can produce

  2. Modulation of the COMT Val158Met polymorphism on resting-state EEG power in postmenopausal healthy women

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    Silvia eSolis-Ortiz

    2015-04-01

    Full Text Available The catechol-O-methyltransferase (COMT Val158Met polymorphism impacts cortical dopamine levels and may influence cortical electrical activity in the human brain. This study investigated whether COMT genotype influences resting-state electroencephalogram (EEG power in the frontal, parietal and midline regions in healthy volunteers. EEG recordings were conducted in the resting-state in 13 postmenopausal healthy woman carriers of the Val/Val genotype and 11 with the Met/Met genotype. The resting EEG spectral absolute power in the frontal (F3, F4, F7, F8, FC3 and FC4, parietal (CP3, CP4, P3 and P4 and midline (Fz, FCz, Cz, CPz, Pz and Oz was analyzed during the eyes-open and eyes-closed conditions. The frequency bands considered were the delta, theta, alpha1, alpha2, beta1 and beta2. EEG data of the Val/Val and Met/Met genotypes, brain regions and conditions were analyzed using a general linear model analysis. In the individuals with the Met/Met genotype, delta activity was increased in the eyes-closed condition, theta activity was increased in the eyes-closed and in the eyes-open conditions, and alpha1 band, alpha2 band and beta1band activity was increased in the eyes-closed condition.A significant interaction between COMT genotypes and spectral bands was observed. Met homozygote individuals exhibited more delta, theta and beta1 activity than individuals with the Val/Val genotype. No significant interaction between COMT genotypes and the resting-state EEG regional power and conditions were observed for the three brain regions studied. Our findings indicate that the COMT Val158Met polymorphism does not directly impact resting-state EEG regional power, but instead suggest that COMT genotype can modulate resting-state EEG spectral power in postmenopausal healthy women.

  3. Cognitive, Affective, and Motivational Changes during Ostracism: An ERP, EMG, and EEG Study Using a Computerized Cyberball Task.

    Science.gov (United States)

    Kawamoto, Taishi; Nittono, Hiroshi; Ura, Mitsuhiro

    2013-01-01

    Individuals are known to be highly sensitive to signs of ostracism, such as being ignored or excluded; however, the cognitive, affective, and motivational processes underlying ostracism have remained unclear. We investigated temporal changes in these psychological states resulting from being ostracized by a computer. Using event-related brain potentials (ERPs), the facial electromyogram (EMG), and electroencephalogram (EEG), we focused on the P3b amplitude, corrugator supercilii activity, and frontal EEG asymmetry, which reflect attention directed at stimuli, negative affect, and approach/withdrawal motivation, respectively. Results of the P3b and corrugator supercilii activity replicated findings of previous studies on being ostracized by humans. The mean amplitude of the P3b wave decreased, and facial EMG activity increased over time. In addition, frontal EEG asymmetry changed from relative left frontal activation, suggestive of approach motivation, to relative right frontal activation, indicative of withdrawal motivation. These findings suggest that ostracism by a computer-generated opponent is an aversive experience that in time changes the psychological status of ostracized people, similar to ostracism by human. Our findings also imply that frontal EEG asymmetry is a useful index for investigating ostracism. Results of this study suggest that ostracism has well developed neurobiological foundations.

  4. Cognitive, Affective, and Motivational Changes during Ostracism: An ERP, EMG, and EEG Study Using a Computerized Cyberball Task

    Directory of Open Access Journals (Sweden)

    Taishi Kawamoto

    2013-01-01

    Full Text Available Individuals are known to be highly sensitive to signs of ostracism, such as being ignored or excluded; however, the cognitive, affective, and motivational processes underlying ostracism have remained unclear. We investigated temporal changes in these psychological states resulting from being ostracized by a computer. Using event-related brain potentials (ERPs, the facial electromyogram (EMG, and electroencephalogram (EEG, we focused on the P3b amplitude, corrugator supercilii activity, and frontal EEG asymmetry, which reflect attention directed at stimuli, negative affect, and approach/withdrawal motivation, respectively. Results of the P3b and corrugator supercilii activity replicated findings of previous studies on being ostracized by humans. The mean amplitude of the P3b wave decreased, and facial EMG activity increased over time. In addition, frontal EEG asymmetry changed from relative left frontal activation, suggestive of approach motivation, to relative right frontal activation, indicative of withdrawal motivation. These findings suggest that ostracism by a computer-generated opponent is an aversive experience that in time changes the psychological status of ostracized people, similar to ostracism by human. Our findings also imply that frontal EEG asymmetry is a useful index for investigating ostracism. Results of this study suggest that ostracism has well developed neurobiological foundations.

  5. Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals

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    Seyyed Abed Hosseini

    2015-10-01

    Full Text Available Background: This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research.Methods: We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz and peripheral signals such as blood volume pulse, skin conductance (SC and respiration, under images induction (calm-neutral and negatively excited for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method.Results: The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM.Conclusion: This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

  6. Emotional stress recognition using a new fusion link between electroencephalogram and peripheral signals.

    Science.gov (United States)

    Hosseini, Seyyed Abed; Khalilzadeh, Mohammad Ali; Naghibi-Sistani, Mohammad Bagher; Homam, Seyyed Mehran

    2015-07-06

    This paper proposes a new emotional stress assessment system using multi-modal bio-signals. Electroencephalogram (EEG) is the reflection of brain activity and is widely used in clinical diagnosis and biomedical research. We design an efficient acquisition protocol to acquire the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) and peripheral signals such as blood volume pulse, skin conductance (SC) and respiration, under images induction (calm-neutral and negatively excited) for the participants. The visual stimuli images are selected from the subset International Affective Picture System database. The qualitative and quantitative evaluation of peripheral signals are used to select suitable segments of EEG signals for improving the accuracy of signal labeling according to emotional stress states. After pre-processing, wavelet coefficients, fractal dimension, and Lempel-Ziv complexity are used to extract the features of the EEG signals. The vast number of features leads to the problem of dimensionality, which is solved using the genetic algorithm as a feature selection method. The results show that the average classification accuracy is 89.6% for two categories of emotional stress states using the support vector machine (SVM). This is a great improvement in results compared to other similar researches. We achieve a noticeable improvement of 11.3% in accuracy using SVM classifier, in compared to previous studies. Therefore, a new fusion between EEG and peripheral signals are more robust in comparison to the separate signals.

  7. Implanted electrodes for multi-month EEG.

    Science.gov (United States)

    Jochum, Thomas; Engdahl, Susannah; Kolls, Brad J; Wolf, Patrick

    2014-01-01

    An implanted electroencephalogram (EEG) recorder would help diagnose infrequent seizure-like events. A proof-of-concept study quantified the electrical characteristics of the electrodes planned for the proposed recorder. The electrodes were implanted in an ovine model for eight weeks. Electrode impedance was less than 800 Ohms throughout the study. A frequency-domain determination of sedation performed similarly for surface versus implanted electrodes throughout the study. The time-domain correlation between an implanted electrode and a surface electrode was almost as high as between two surface electrodes (0.86 versus 0.92). EEG-certified clinicians judged that the implanted electrode quality was adequate to excellent and that the implanted electrodes provided the same clinical information as surface electrodes except for a noticeable amplitude difference. No significant issues were found that would stop development of the EEG recorder.

  8. The Effect of Temporal EEG Signals While Listening to Quran Recitation

    Directory of Open Access Journals (Sweden)

    Azian Azamimi Abdullah

    2011-01-01

    Full Text Available Human brain which is one of the most complex organic systems, involves billons of interacting physiological and chemical process that will give rise to experimentally observed neuroelectrical activity, which is called an electroencephalogram (EEG. Many researchers have investigated the effect of various events to the EEG signals such as meditation and classical music [1]-[3]. From their analysis result, they claimed that meditation and classical music can help  a  person  to be  in relaxing conditions. This study is performed in order to extend the research findings of the effect of religious activities to the human brain. EEG signals from subject at rests, as well as in different cognitive states; listening to Quran recitation and listening to hard music are measured and analysed. Statistical analysis using SPSS software is performed in order to test the validity of obtained data. The analysis results from this study show that listening to Quran recitation can  generate alpha wave and can  help a person always in relax condition compared with listening to hard rock music.

  9. A novel EOG/EEG hybrid human-machine interface adopting eye movements and ERPs: application to robot control.

    Science.gov (United States)

    Ma, Jiaxin; Zhang, Yu; Cichocki, Andrzej; Matsuno, Fumitoshi

    2015-03-01

    This study presents a novel human-machine interface (HMI) based on both electrooculography (EOG) and electroencephalography (EEG). This hybrid interface works in two modes: an EOG mode recognizes eye movements such as blinks, and an EEG mode detects event related potentials (ERPs) like P300. While both eye movements and ERPs have been separately used for implementing assistive interfaces, which help patients with motor disabilities in performing daily tasks, the proposed hybrid interface integrates them together. In this way, both the eye movements and ERPs complement each other. Therefore, it can provide a better efficiency and a wider scope of application. In this study, we design a threshold algorithm that can recognize four kinds of eye movements including blink, wink, gaze, and frown. In addition, an oddball paradigm with stimuli of inverted faces is used to evoke multiple ERP components including P300, N170, and VPP. To verify the effectiveness of the proposed system, two different online experiments are carried out. One is to control a multifunctional humanoid robot, and the other is to control four mobile robots. In both experiments, the subjects can complete tasks effectively by using the proposed interface, whereas the best completion time is relatively short and very close to the one operated by hand.

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

    Directory of Open Access Journals (Sweden)

    Ernesto Pablo Lana

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

  11. Cognitive neuroscience of creativity: EEG based approaches.

    Science.gov (United States)

    Srinivasan, Narayanan

    2007-05-01

    Cognitive neuroscience of creativity has been extensively studied using non-invasive electrical recordings from the scalp called electroencephalograms (EEGs) and event related potentials (ERPs). The paper discusses major aspects of performing research using EEG/ERP based experiments including the recording of the signals, removing noise, estimating ERP signals, and signal analysis for better understanding of the neural correlates of processes involved in creativity. Important factors to be kept in mind to record clean EEG signal in creativity research are discussed. The recorded EEG signal can be corrupted by various sources of noise and methodologies to handle the presence of unwanted artifacts and filtering noise are presented followed by methods to estimate ERPs from the EEG signals from multiple trials. The EEG and ERP signals are further analyzed using various techniques including spectral analysis, coherence analysis, and non-linear signal analysis. These analysis techniques provide a way to understand the spatial activations and temporal development of large scale electrical activity in the brain during creative tasks. The use of this methodology will further enhance our understanding the processes neural and cognitive processes involved in creativity.

  12. Distribution entropy analysis of epileptic EEG signals.

    Science.gov (United States)

    Li, Peng; Yan, Chang; Karmakar, Chandan; Liu, Changchun

    2015-01-01

    It is an open-ended challenge to accurately detect the epileptic seizures through electroencephalogram (EEG) signals. Recently published studies have made elaborate attempts to distinguish between the normal and epileptic EEG signals by advanced nonlinear entropy methods, such as the approximate entropy, sample entropy, fuzzy entropy, and permutation entropy, etc. Most recently, a novel distribution entropy (DistEn) has been reported to have superior performance compared with the conventional entropy methods for especially short length data. We thus aimed, in the present study, to show the potential of DistEn in the analysis of epileptic EEG signals. The publicly-accessible Bonn database which consisted of normal, interictal, and ictal EEG signals was used in this study. Three different measurement protocols were set for better understanding the performance of DistEn, which are: i) calculate the DistEn of a specific EEG signal using the full recording; ii) calculate the DistEn by averaging the results for all its possible non-overlapped 5 second segments; and iii) calculate it by averaging the DistEn values for all the possible non-overlapped segments of 1 second length, respectively. Results for all three protocols indicated a statistically significantly increased DistEn for the ictal class compared with both the normal and interictal classes. Besides, the results obtained under the third protocol, which only used very short segments (1 s) of EEG recordings showed a significantly (p entropy algorithm. The capability of discriminating between the normal and interictal EEG signals is of great clinical relevance since it may provide helpful tools for the detection of a seizure onset. Therefore, our study suggests that the DistEn analysis of EEG signals is very promising for clinical and even portable EEG monitoring.

  13. Voltage synchronizations between multichannel electroencephalograms during epileptic seizures

    CERN Document Server

    Tuncay, Caglar

    2010-01-01

    The underlying dynamics for the electroencephalographic (EEG) recordings from humans but especially epilepsy patients are usually not completely known. However, the ictal activity is claimed to be characterized by synchronous oscillations in the brain voltages in the literature. These time dependent interdependencies (synchronization, coupling) between the EEG voltages from epileptogenic and non epileptogenic brain sites of nineteen focal epileptic patients are investigated in this work. It is found that strong synchronizationdesynchronization events occur in alternation during most of the investigated seizures. Thus, these seizures are detected with considerable sensitivity (71 of the 79 seizures).

  14. Electroencephalogram and brainstem auditory evoked potential in 539 patients with central coordination disorder

    Institute of Scientific and Technical Information of China (English)

    Huijia Zhang; Hua Yan; Paoqiu Wang; Jihong Hu; Hongtao Zhou; Rong Qin

    2008-01-01

    BACKGROUND: Electroencephalogram (EEG) and brainstem auditory evoked potential (BAEP) are objective non-invasive means of measuring brain electrophysiology.OBJECTIVE: To analyze the value of EEG and BAEP in early diagnosis, treatment and prognostic evaluation of central coordination disorder.DESIGN, TIME AND SETTING: This case analysis study was performed at the Rehabilitation Center of Hunan Children's Hospital from January 2002 to January 2006.PARTICIPANTS: A total of 593 patients with severe central coordination disorder, comprising 455 boys and 138 girls, aged 1--6 months were enrolled for this study.METHODS: EEG was monitored using electroencephalography. BAEP was recorded using a Keypoint electromyogram device. Intelligence was tested by professionals using the Gesell scale.MAIN OUTCOME MEASURES: (1) The rate of abnormal EEG and BAEP, (2) correlation of abnormalities of EEG and BAEP with associated injuries, (3) correlation of abnormalities of EEG and BAEP with high risk factors.RESULTS: The rate of abnormal EEG was 68.6% (407/593 patients), and was increased in patients who also had mental retardation (P < 0.05). The rate of abnormal BAEP was 21.4% (127/593 patients). These 127 patients included 67 patients (52.8%) with peripheral auditory damage and 60 patients (47.2%) with central and mixed auditory damage. The rate of abnormal BAEP was significantly increased in patients who also had mental retardation (P < 0.01). Logistic regression analysis showed that asphyxia (P < 0.05), jaundice,preterm delivery, low birth weight and the umbilical cord around the neck were closely correlated with abnormal EEG in patients with central coordination disorder. Intracranial hemorrhage, jaundice (P < 0.05),low birth weight and intrauterine infection (P < 0.05) were closely correlated with abnormal BAEP in patients with central coordination disorder.CONCLUSION: Central coordination disorder is often associated with abnormal EEG and BAEP. The rate of EEG or BAEP abnormality

  15. Simultaneous scalp electroencephalography (EEG), electromyography (EMG), and whole-body segmental inertial recording for multi-modal neural decoding.

    Science.gov (United States)

    Bulea, Thomas C; Kilicarslan, Atilla; Ozdemir, Recep; Paloski, William H; Contreras-Vidal, Jose L

    2013-07-26

    Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

  16. ``Seeing'' electroencephalogram through the skull: imaging prefrontal cortex with fast optical signal

    Science.gov (United States)

    Medvedev, Andrei V.; Kainerstorfer, Jana M.; Borisov, Sergey V.; Gandjbakhche, Amir H.; Vanmeter, John

    2010-11-01

    Near-infrared spectroscopy is a novel imaging technique potentially sensitive to both brain hemodynamics (slow signal) and neuronal activity (fast optical signal, FOS). The big challenge of measuring FOS noninvasively lies in the presumably low signal-to-noise ratio. Thus, detectability of the FOS has been controversially discussed. We present reliable detection of FOS from 11 individuals concurrently with electroencephalogram (EEG) during a Go-NoGo task. Probes were placed bilaterally over prefrontal cortex. Independent component analysis (ICA) was used for artifact removal. Correlation coefficient in the best correlated FOS-EEG ICA pairs was highly significant (p prefrontal cortex in rapid object recognition. EROS is highly localized and can provide cost-effective imaging tools for cortical mapping of cognitive processes.

  17. Optimizing Detection Rate and Characterization of Subtle Paroxysmal Neonatal Abnormal Facial Movements with Multi-Camera Video-Electroencephalogram Recordings.

    Science.gov (United States)

    Pisani, Francesco; Pavlidis, Elena; Cattani, Luca; Ferrari, Gianluigi; Raheli, Riccardo; Spagnoli, Carlotta

    2016-06-01

    Objectives We retrospectively analyze the diagnostic accuracy for paroxysmal abnormal facial movements, comparing one camera versus multi-camera approach. Background Polygraphic video-electroencephalogram (vEEG) recording is the current gold standard for brain monitoring in high-risk newborns, especially when neonatal seizures are suspected. One camera synchronized with the EEG is commonly used. Methods Since mid-June 2012, we have started using multiple cameras, one of which point toward newborns' faces. We evaluated vEEGs recorded in newborns in the study period between mid-June 2012 and the end of September 2014 and compared, for each recording, the diagnostic accuracies obtained with one-camera and multi-camera approaches. Results We recorded 147 vEEGs from 87 newborns and found 73 episodes of paroxysmal facial abnormal movements in 18 vEEGs of 11 newborns with the multi-camera approach. By using the single-camera approach, only 28.8% of these events were identified (21/73). Ten positive vEEGs with multicamera with 52 paroxysmal facial abnormal movements (52/73, 71.2%) would have been considered as negative with the single-camera approach. Conclusions The use of one additional facial camera can significantly increase the diagnostic accuracy of vEEGs in the detection of paroxysmal abnormal facial movements in the newborns.

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

    Directory of Open Access Journals (Sweden)

    Saptono Debyo

    2016-01-01

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

  19. Modeling EEG fractal dimension changes in wake and drowsy states in humans--a preliminary study.

    Science.gov (United States)

    Bojić, Tijana; Vuckovic, Aleksandra; Kalauzi, Aleksandar

    2010-01-21

    Aim of this preliminary study was to examine and compare topographic distribution of Higuchi's fractal dimension (FD, measure of signal complexity) of EEG signals between states of relaxed wakefulness and drowsiness, as well as their FD differences. The experiments were performed on 10 healthy individuals using a fourteen-channel montage. An explanation is offered on the causes of the detected FD changes. FD values of 60s records belonging to wake (Hori's stage 1) and drowsy (Hori's stages 2-4) states were calculated for each channel and each subject. In 136 out of 140 epochs an increase in FD was obtained. Relationship between signal FD and its relative alpha amplitude was mathematically modeled and we quantitatively demonstrated that the increase in FD was predominantly due to a reduction in alpha activity. The model was generalized to include other EEG oscillations. By averaging FD values for each channel across 10 subjects, four clusters (O2O1; T6P4T5P3; C3F3F4C4F8F7; T4T3) for the wake and two clusters (O2O1P3T6P4T5; C3C4F4F3F8T4T3F7) for the drowsy state were statistically verified. Topographic distribution of FD values in wakefulness showed a lateral symmetry and a partial fronto-occipital gradient. In drowsiness, a reduction in the number of clusters was detected, due to regrouping of channels T3, T4, O1 and O2. Topographic distribution of absolute FD differences revealed largest values at F7, O1 and F3. Reorganization of channel clusters showed that regionalized brain activity, specific for wakefulness, became more global by entering into drowsiness. Since the global increase in FD during wake-to-drowsy transition correlated with the decrease of alpha power, we inferred that increase of EEG complexity may not necessarily be an index of brain activation.

  20. Electroencephalogram and evoked potential parameters examined in Chinese mild head injury patients for forensic medicine

    Institute of Scientific and Technical Information of China (English)

    Xi-Ping CHEN; Lu-Yang TAO; Andrew CN CHEN

    2006-01-01

    Objective To evaluate the usefulness of quantitative electroencephalogram (QEEG), flash visual evoked potential (F-VEP) and auditory brainstem responses (ABR) as indicators of general neurological status. Methods Comparison was conducted on healthy controls (N=30) and patients with brain concussion (N=60) within 24 h after traumatic brain injury. Follow-up study of patient group was completed with the same standard paradigm 3 months later. All participants were recorded in multi-modality related potential testing in both early and late concussion at the same clinical setting. Glasgow coma scale, CT scanning, and physical examinations of neuro-psychological function, optic and auditory nervous system were performed before electroencephalogram (EEG) and evoked potential (EEG-EP) testing. Any participants showed abnormal changes of clinical examinations were excluded from the study. Average power of frequency spectrum and power ratios were selected for QEEG testing, and latency and amplitude of F-VEP and ABR were recorded.Results Between patients and normal controls, the results indicated: (1) Highly significance (P < 0.01) in average power of α1 and power ratios of θ/α1, θ/α2, α1/α2 of EEG recording; (2) N70-P100 amplitude of F-VEP in significant difference at early brain concussion; and (3) apparent prolongation of Ⅰ~Ⅲ inter-peak latency of ABR appeared in some individuals at early stage after concussion. The follow-up study showed that some patients with concussion were also afflicted with characteristic changes of EEG components for both increments of α1 average power and θ/α2 power ratio after 3 months recording. Conclusion EEG testing has been shown to be more effective and sensitive than evoked potential tests alone on detecting functional state of patients with mild traumatic brain injury (MTBI). Increments of α1 average power and θ/α2 power ratio are the sensitive EEG parameters to determining early concussion and evaluating outcome of

  1. Human decision making based on variations in internal noise: an EEG study.

    Directory of Open Access Journals (Sweden)

    Sygal Amitay

    Full Text Available Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or 'internal' noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments.

  2. Automatic classification of infant sleep based on instantaneous frequencies in a single-channel EEG signal.

    Science.gov (United States)

    Čić, Maja; Šoda, Joško; Bonković, Mirjana

    2013-12-01

    This study presents a novel approach for the electroencephalogram (EEG) signal quantification in which the empirical mode decomposition method, a time-frequency method designated for nonlinear and non-stationary signals, decomposes the EEG signal into intrinsic mode functions (IMF) with corresponding frequency ranges that characterize the appropriate oscillatory modes embedded in the brain neural activity acquired using EEG. To calculate the instantaneous frequency of IMFs, an algorithm was developed using the Generalized Zero Crossing method. From the resulting frequencies, two different novel features were generated: the median instantaneous frequencies and the number of instantaneous frequency changes during a 30s segment for seven IMFs. The sleep stage classification for the daytime sleep of 20 healthy babies was determined using the Support Vector Machine classification algorithm. The results were evaluated using the cross-validation method to achieve an approximately 90% accuracy and with new examinee data to achieve 80% average accuracy of classification. The obtained results were higher than the human experts' agreement and were statistically significant, which positioned the method, based on the proposed features, as an efficient procedure for automatic sleep stage classification. The uniqueness of this study arises from newly proposed features of the time-frequency domain, which bind characteristics of the sleep signals to the oscillation modes of brain activity, reflecting the physical characteristics of sleep, and thus have the potential to highlight the congruency of twin pairs with potential implications for the genetic determination of sleep.

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

    Directory of Open Access Journals (Sweden)

    Chung-Yeon Lee

    2012-10-01

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

  4. Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study.

    Science.gov (United States)

    Lima, Clodoaldo A M; Coelho, André L V; Eisencraft, Marcio

    2010-08-01

    The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset. Copyright 2010 Elsevier Ltd. All rights reserved.

  5. Optimal channel selection for analysis of EEG-sleep patterns of neonates.

    Science.gov (United States)

    Piryatinska, Alexandra; Woyczynski, Wojbor A; Scher, Mark S; Loparo, Kenneth A

    2012-04-01

    This paper extends our previous work on automated detection and classification of neonate EEG sleep stages. In [19] we adapted and integrated a range of computational, mathematical and statistical tools for the analysis of neonatal electroencephalogram (EEG) sleep recordings with the aim of facilitating the assessment of neonatal brain maturation and dismaturity by studying the structure and temporal patterns of their sleep. That work relied on algorithms using a single channel of EEG. The present paper builds on our previous work by incorporating a larger selection of EEG channels that capture both the spatial distribution and temporal patterns of EEG during sleep. Using a multivariate analysis approach, we obtain the "optimal" selection of the EEG channels and characteristics that are most suitable for EEG sleep state separation. Copyright © 2011. Published by Elsevier Ireland Ltd.

  6. 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.

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

    DEFF Research Database (Denmark)

    Hansen, GL; Foli-Andersen, P; Fredheim, S

    2016-01-01

    BACKGROUND: The purpose of this study was to explore the possible difference in the electroencephalogram (EEG) pattern between euglycemia and hypoglycemia in children with type 1 diabetes (T1D) during daytime and during sleep. The aim is to develop a hypoglycemia alarm based on continuous EEG...... measurement and real-time signal processing. METHOD: Eight T1D patients aged 6-12 years were included. A hyperinsulinemic hypoglycemic clamp was performed to induce hypoglycemia both during daytime and during sleep. Continuous EEG monitoring was performed. For each patient, quantitative EEG (qEEG) measures...... 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...

  8. Driver drowsiness detection using the in-ear EEG.

    Science.gov (United States)

    Taeho Hwang; Miyoung Kim; Seunghyeok Hong; Kwang Suk Park

    2016-08-01

    Driver drowsiness monitoring is one of the most demanded technologies for active prevention of severe road accidents. Electroencephalogram (EEG) and several peripheral signals have been suggested for the drowsiness monitoring. However, each type of signal has partial limitations in terms of either convenience or accuracy. Recent emerged concept of in-ear EEG raises expectations due to reduced obtrusiveness. It is yet unclear whether the in-ear EEG is effective enough for drowsiness detection in comparison with on-scalp EEG or peripheral signals. In this work, we evaluated performance of the in-ear EEG in drivers' alertness-drowsiness classification for the first time. Simultaneously, we also tested three peripheral signals including electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) which have advantage in convenience of measurement. The classification analysis using the in-ear EEG resulted in high classification accuracy comparable to that of the individual on-scalp EEG channels. The ECG, PPG and GSR showed competitive performance but only when used together in pairwise combinations. Our results suggest that the in-ear EEG would be viable alternative to the single channel EEG or the individual peripheral signals for the drowsiness monitoring.

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

    Science.gov (United States)

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

    2012-12-01

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

  10. Quantitative electroencephalogram utility in predicting conversion of mild cognitive impairment to dementia with Lewy bodies☆

    Science.gov (United States)

    Bonanni, Laura; Perfetti, Bernardo; Bifolchetti, Stefania; Taylor, John-Paul; Franciotti, Raffaella; Parnetti, Lucilla; Thomas, Astrid; Onofrj, Marco

    2015-01-01

    Mild cognitive impairment (MCI) as a precursor of dementia with Lewy bodies (DLB) is the focus of recent research, trying to explore the early mechanisms and possible biomarkers of DLB. Quantitative electroencephalogram (QEEG) methods are able to differentiate early DLB from Alzheimer's disease (AD). The aim of the present study was to assess whether QEEG abnormalities, characterized by dominant frequency 1.5 Hz, typical of early DLB, are already present at the stage of MCI and to evaluate whether EEG abnormalities can predict the development of DLB. Forty-seven MCI subjects were followed for 3 years. EEG recordings were obtained at admission and at the end of the study. At the end of follow-up, 20 subjects had developed probable DLB (MCI-DLB), 14 had probable AD (MCI-AD), 8 did not convert to dementia, 5 developed a non-AD/DLB dementia. One hundred percent of MCI-DLB showed EEG abnormalities at admission. Ninety three percent of MCI-AD maintained a normal EEG throughout the study. QEEG may represent a powerful tool to predict the progression from MCI to DLB with a sensitivity and specificity close to 100%. PMID:25129239

  11. Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data

    Institute of Scientific and Technical Information of China (English)

    Suparerk JANJARASJITT

    2014-01-01

    Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram (EEG) signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study, a computational method for estimating the spectral exponent based on wavelet transform was examined. A series of Daubechies wavelet bases with various numbers of vanishing moments were applied to analyze the self-similar characteristics of intracranial EEG data corresponding to different pathological states of the brain, i.e., ictal and interictal states, in patients with epilepsy. The computational results show that the spectral exponents of intracranial EEG signals obtained during epileptic seizure activity tend to be higher than those obtained during non-seizure periods. This suggests that the intracranial EEG signals obtained during epileptic seizure activity tend to be more self-similar than those obtained during non-seizure periods. The computational results obtained using the wavelet-based approach were validated by comparison with results obtained using the power spectrum method.

  12. Sleep respiratory disturbances and arousals at moderate altitude have overlapping electroencephalogram spectral signatures.

    Science.gov (United States)

    Stadelmann, Katrin; Latshang, Tsogyal D; Tarokh, Leila; Lo Cascio, Christian M; Tesler, Noemi; Stoewhas, Anne-Christin; Kohler, Malcolm; Bloch, Konrad E; Huber, Reto; Achermann, Peter

    2014-08-01

    An ascent to altitude has been shown to result in more central apneas and a shift towards lighter sleep in healthy individuals. This study employs spectral analysis to investigate the impact of respiratory disturbances (central/obstructive apnea and hypopnea or periodic breathing) at moderate altitude on the sleep electroencephalogram (EEG) and to compare EEG changes resulting from respiratory disturbances and arousals. Data were collected from 51 healthy male subjects who spent 1 night at moderate altitude (2590 m). Power density spectra of Stage 2 sleep were calculated in a subset (20) of these participants with sufficient artefact-free data for (a) epochs with respiratory events without an accompanying arousal, (b) epochs containing an arousal and (c) epochs of undisturbed Stage 2 sleep containing neither arousal nor respiratory events. Both arousals and respiratory disturbances resulted in reduced power in the delta, theta and spindle frequency range and increased beta power compared to undisturbed sleep. The similarity of the EEG changes resulting from altitude-induced respiratory disturbances and arousals indicates that central apneas are associated with micro-arousals, not apparent by visual inspection of the EEG. Our findings may have implications for sleep in patients and mountain tourists with central apneas and suggest that respiratory disturbances not accompanied by an arousal may, none the less, impact sleep quality and impair recuperative processes associated with sleep more than previously believed.

  13. The quantitative electroencephalogram and the low-resolution electrical tomographic analysis in posttraumatic stress disorder.

    Science.gov (United States)

    Todder, Doran; Levine, Joseph; Abujumah, Ahmad; Mater, Michael; Cohen, Hagit; Kaplan, Zeev

    2012-01-01

    The electroencephalogram (EEG) is the recording of the brain electrical activity as measured on the scalp. Using mathematical algorithms, the 3-dimensional (3D) distribution of the electrical potential inside the brain can be calculated. One of the methods to calculate it is the low-resolution electrical tomographic analysis (LORETA). In this research, we seek to find the brain structures that differentiate patients with posttraumatic stress disorder (PTSD) from controls. Ten right-handed consenting adult male patients were recruited from a PTSD clinic. All patients fulfilled Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision [DSM-IV-TR]) criteria for chronic PTSD (duration >2 years.) and were on drug treatment regimens that had been stable for at least 2 months (involving only serotonin reuptake inhibitors [SSRIs] and benzodiazepines).The control group consisted of 10 healthy hospital staff members. All study participants underwent 19 channel EEG measurements according to current standards of practice. All artifact-free EEG strips were examined for spectral as well as LORETA analysis focusing on the theta (4-7 Hz) band which is suggested to reflect the activity of the limbic system. The theta band showed a statistically significant difference (P QEEG) and the LORETA method, among other methods, may improve the neuroanatomical resolution of EEG data analysis.

  14. Nonlinear Evaluation of Electroencephalogram Signals in Different Sleep Stages in Apnea Episodes

    Directory of Open Access Journals (Sweden)

    Atefeh Goshvarpour

    2013-09-01

    Full Text Available Distinct sleep phases are related to different dynamical patterns in electroencephalogram (EEG signals. In this article, the relationship between the sleep stages and nonlinear behavior of sleep EEG is explored. In particular, analysis of approximate entropy (ApEn and the largest Lyapunov exponent is evaluated in patients with sleep apnea, which is defined as respiratory flow that is suspended or decreased for more than 10 s. The pathological sleep EEG signals for analysis were obtained from the MIT-BIH polysomnography database available online at the PhysioBank. The results show that for the both normal and apneic sleep epochs, ApEn decreased significantly as the sleep goes into deeper stages. Therefore, it indicated that as sleep becomes deeper, the brain function becomes less activated. Compared with normal sleep, the mean value of largest lyapunov exponents was also significantly lower than that of normal epochs during deep sleep stages. The results also show that the average largest lyapunov exponents of EEG signals increased in the REM state. Because during this stage of sleep, the cortex becomes more active and more neurons incorporate in the information processing. In conclusion, the nonlinear dynamical measures obtained from the nonlinear dynamical analysis such as the approximate entropy and largest lyapunov exponents can be useful for characterizing the physiological or pathological states of the brain.

  15. Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients.

    Science.gov (United States)

    Yu, Yi; Zhao, Yun; Si, Yajing; Ren, Qiongqiong; Ren, Wu; Jing, Changqin; Zhang, Hongxing

    2016-11-25

    In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia. The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks. The results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task. These results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.

  16. An Energy Efficient Compressed Sensing Framework for the Compression of Electroencephalogram Signals

    Directory of Open Access Journals (Sweden)

    Simon Fauvel

    2014-01-01

    Full Text Available The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG signals. We propose the use of a compressed sensing (CS framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.

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

    KAUST Repository

    Ngo, Duy

    2015-08-19

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

  18. Dipole source localization of mouse electroencephalogram using the Fieldtrip toolbox.

    Science.gov (United States)

    Lee, Chungki; Oostenveld, Robert; Lee, Soo Hyun; Kim, Lae Hyun; Sung, Hokun; Choi, Jee Hyun

    2013-01-01

    The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG) with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE), scanning with multiple signal classification (MUSIC), and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC) analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.

  19. Dipole source localization of mouse electroencephalogram using the Fieldtrip toolbox.

    Directory of Open Access Journals (Sweden)

    Chungki Lee

    Full Text Available The mouse model is an important research tool in neurosciences to examine brain function and diseases with genetic perturbation in different brain regions. However, the limited techniques to map activated brain regions under specific experimental manipulations has been a drawback of the mouse model compared to human functional brain mapping. Here, we present a functional brain mapping method for fast and robust in vivo brain mapping of the mouse brain. The method is based on the acquisition of high density electroencephalography (EEG with a microarray and EEG source estimation to localize the electrophysiological origins. We adapted the Fieldtrip toolbox for the source estimation, taking advantage of its software openness and flexibility in modeling the EEG volume conduction. Three source estimation techniques were compared: Distribution source modeling with minimum-norm estimation (MNE, scanning with multiple signal classification (MUSIC, and single-dipole fitting. Known sources to evaluate the performance of the localization methods were provided using optogenetic tools. The accuracy was quantified based on the receiver operating characteristic (ROC analysis. The mean detection accuracy was high, with a false positive rate less than 1.3% and 7% at the sensitivity of 90% plotted with the MNE and MUSIC algorithms, respectively. The mean center-to-center distance was less than 1.2 mm in single dipole fitting algorithm. Mouse microarray EEG source localization using microarray allows a reliable method for functional brain mapping in awake mouse opening an access to cross-species study with human brain.

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

    Science.gov (United States)

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

    2003-03-01

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

  1. A Novel Approach Based on Data Redundancy for Feature Extraction of EEG Signals.

    Science.gov (United States)

    Amin, Hafeez Ullah; Malik, Aamir Saeed; Kamel, Nidal; Hussain, Muhammad

    2016-03-01

    Feature extraction and classification for electroencephalogram (EEG) in medical applications is a challenging task. The EEG signals produce a huge amount of redundant data or repeating information. This redundancy causes potential hurdles in EEG analysis. Hence, we propose to use this redundant information of EEG as a feature to discriminate and classify different EEG datasets. In this study, we have proposed a JPEG2000 based approach for computing data redundancy from multi-channels EEG signals and have used the redundancy as a feature for classification of EEG signals by applying support vector machine, multi-layer perceptron and k-nearest neighbors classifiers. The approach is validated on three EEG datasets and achieved high accuracy rate (95-99 %) in the classification. Dataset-1 includes the EEG signals recorded during fluid intelligence test, dataset-2 consists of EEG signals recorded during memory recall test, and dataset-3 has epileptic seizure and non-seizure EEG. The findings demonstrate that the approach has the ability to extract robust feature and classify the EEG signals in various applications including clinical as well as normal EEG patterns.

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

    DEFF Research Database (Denmark)

    Sejling, A. S.; Kjaer, T. W.; Pedersen-Bjergaard, U.;

    2015-01-01

    Hypoglycemia is associated with increased activity in the low-frequency bands in the electroencephalogram (EEG). We investigated whether hypoglycemia awareness and unawareness are associated with different hypoglycemia-associated EEG changes in patients with type 1 diabetes.Twenty-four patients...... participated in the study; 10 with normal hypoglycemia awareness and 14 with hypoglycemia unawareness. The patients were studied at normoglycemia (5-6 mmol/l), hypoglycemia (2.0-2.5 mmol/l) and during recovery (5-6 mmol/l) by hyperinsulinemic glucose clamp. During each one-hour period, EEG, cognitive function...... amplitude remained increased. Cognitive function declined equally during hypoglycemia in both groups and during recovery reaction time was still prolonged in a subset of tests. The aware group reported higher hypoglycemia symptom scores and had higher epinephrine and cortisol responses compared...

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

    Science.gov (United States)

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

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

    Science.gov (United States)

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

  5. Automated detection of hypoglycemia-induced EEG changes recorded by subcutaneous electrodes in subjects with type 1 diabetes--the brain as a biosensor

    DEFF Research Database (Denmark)

    Juhl, Claus B.; Højlund, Kurt; Elsborg, Rasmus

    2010-01-01

    Hypoglycemia unawareness is a common condition associated with increased risk of severe hypoglycemia. We test the hypothesis that specific changes in the electroencephalogram (EEG) during hypoglycemia can be recorded by subcutaneous electrodes and processed by a general mathematical algorithm, an......, and that hypoglycemia associated EEG changes appear before the development of severe hypoglycemia....

  6. Pilot Study: The Use of Electroencephalogram to Measure Attentiveness towards Short Training Videos

    Directory of Open Access Journals (Sweden)

    Paul Alton Nussbaum

    2013-04-01

    Full Text Available Universities, schools, and training centers are seeking to improve their computer-based [3] and distance learning classes through the addition of short training videos, often referred to as podcasts [4]. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. The proposed research presents a novel approach to this issue. Signal processing of electroencephalogram (EEG has proven useful in measuring attentiveness in a variety of applications such as vehicle operation and listening to sonar [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]. Additionally, studies have shown that EEG data can be correlated to the ability of participants to remember television commercials days after they have seen them [16]. Electrical engineering presents a possible solution with recent advances in the use of biometric signal analysis for the detection of affective (emotional response [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]. Despite the wealth of literature on the use of EEG to determine attentiveness in a variety of applications, the use of EEG for the detection of attentiveness towards short training videos has not been studied, nor is there a great deal of consistency with regard to specific methods that would imply a single method for this new application. Indeed, there is great variety in EEG signal processing and machine learning methods described in the literature cited above and in other literature [28] [29] [30] [31] [32] [33] [34]. This paper presents a novel method which uses EEG as an input to an automated system that measures a participant’s attentiveness while watching a short training video. This paper provides the results of a pilot study, including a structured comparison of signal processing and machine learning methods to find optimal solutions which can be extended to other applications.

  7. EEG frequency tagging dissociates between neural processing of motion synchrony and human quality of multiple point-light dancers

    Science.gov (United States)

    Alp, Nihan; Nikolaev, Andrey R.; Wagemans, Johan; Kogo, Naoki

    2017-01-01

    Do we perceive a group of dancers moving in synchrony differently from a group of drones flying in-sync? The brain has dedicated networks for perception of coherent motion and interacting human bodies. However, it is unclear to what extent the underlying neural mechanisms overlap. Here we delineate these mechanisms by independently manipulating the degree of motion synchrony and the humanoid quality of multiple point-light displays (PLDs). Four PLDs moving within a group were changing contrast in cycles of fixed frequencies, which permits the identification of the neural processes that are tagged by these frequencies. In the frequency spectrum of the steady-state EEG we found two emergent frequency components, which signified distinct levels of interactions between PLDs. The first component was associated with motion synchrony, the second with the human quality of the moving items. These findings indicate that visual processing of synchronously moving dancers involves two distinct neural mechanisms: one for the perception of a group of items moving in synchrony and one for the perception of a group of moving items with human quality. We propose that these mechanisms underlie high-level perception of social interactions. PMID:28272421

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

    Science.gov (United States)

    2016-01-01

    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 personality dimensions and

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

    Directory of Open Access Journals (Sweden)

    Molina Gary N Garcia

    2003-01-01

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

  10. Detrended cross-correlation analysis of electroencephalogram

    Institute of Scientific and Technical Information of China (English)

    Wang Jun; Zhao Da-Qing

    2012-01-01

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

  11. Data selection in EEG signals classification.

    Science.gov (United States)

    Wang, Shuaifang; Li, Yan; Wen, Peng; Lai, David

    2016-03-01

    The alcoholism can be detected by analyzing electroencephalogram (EEG) signals. However, analyzing multi-channel EEG signals is a challenging task, which often requires complicated calculations and long execution time. This paper proposes three data selection methods to extract representative data from the EEG signals of alcoholics. The methods are the principal component analysis based on graph entropy (PCA-GE), the channel selection based on graph entropy (GE) difference, and the mathematic combinations channel selection, respectively. For comparison purposes, the selected data from the three methods are then classified by three classifiers: the J48 decision tree, the K-nearest neighbor and the Kstar, separately. The experimental results show that the proposed methods are successful in selecting data without compromising the classification accuracy in discriminating the EEG signals from alcoholics and non-alcoholics. Among them, the proposed PCA-GE method uses only 29.69% of the whole data and 29.5% of the computation time but achieves a 94.5% classification accuracy. The channel selection method based on the GE difference also gains a 91.67% classification accuracy by using only 29.69% of the full size of the original data. Using as little data as possible without sacrificing the final classification accuracy is useful for online EEG analysis and classification application design.

  12. A dry electrode for EEG recording.

    Science.gov (United States)

    Taheri, B A; Knight, R T; Smith, R L

    1994-05-01

    This paper describes the design, fabrication and testing of a prototype dry surface electrode for EEG signal recording. The new dry electrode has the advantages of no need for skin preparation or conductive paste, potential for reduced sensitivity to motion artifacts and an enhanced signal-to-noise ratio. The electrode's sensing element is a 3 mm stainless steel disk which has a 2000 A (200 nm) thick nitride coating deposited onto one side. The back side of the disk is attached to an impedance converting amplifier. The prototype electrode was mounted on a copper plate attached to the scalp by a Velcro strap. The performance of this prototype dry electrode was compared to commercially available wet electrodes in 3 areas of electroencephalogram (EEG) recording: (1) spontaneous EEG, (2) sensory evoked potentials, and (3) cognitive evoked potentials. In addition to the raw EEG, the power spectra of the signals from both types of electrodes were also recorded. The results suggest that the dry electrode performs comparably to conventional electrodes for all types of EEG signal analysis. This new electrode may be useful for the production of high resolution surface maps of brain activity where a large number of electrodes or prolonged recording times are required.

  13. An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures

    Directory of Open Access Journals (Sweden)

    Rajeev Sharma

    2015-07-01

    Full Text Available The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI, developed using discrete wavelet transform (DWT and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s t-test, the Wilcoxon test, the receiver operating characteristic (ROC and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN, probabilistic neural network (PNN, fuzzy classifier and least squares support vector machine (LS-SVM, for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.

  14. The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like.

    Science.gov (United States)

    Tarokh, Leila; Rusterholz, Thomas; Achermann, Peter; Van Dongen, Hans P A

    2015-08-01

    The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non-rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.

  15. Role of electroencephalogram and neuroimaging in first onset afebrile and complex febrile seizures in children from Kashmir

    Directory of Open Access Journals (Sweden)

    Akhter Rasool

    2012-01-01

    Full Text Available Objectives: (1 To determine the frequency of abnormal neuroimaging in children with new-onset afebrile and complex febrile seizures; (2 to draw a correlation between Electroencephalogram (EEG and neuroimaging. Study Design: A hospital-based prospective study. Materials and Methods: A total of 276 children (6 months to 14 years of age, who presented with new-onset afebrile or complex febrile seizures, underwent EEG and neuroimaging [Computed Tomography (CT and/or Magnetic Resonance Imaging (MRI]. Results: Generalized seizures constituted the major seizure group in our study - 116/276 (42% - followed by partial seizures 86/276 (31.2% and complex febrile seizure in 64/276 (23.2%. Generalized as well as partial seizures were more common in children aged 6-14 years, while complex febrile seizures were predominantly seen in children less than 6 years old. Most of the patients with generalized and partial seizures had EEG abnormalities, while EEG abnormalities were uncommon in patients with complex febrile seizures. A total of 27/276 (9.8% patients with seizure disorder had abnormal CT scans and this abnormality was more common in patients with partial seizures. CT abnormality was seen more commonly in those patients who had an abnormal EEG. EEG and CT correlation showed that patients with abnormal EEG had higher rates of CT abnormality, ie, 16.1% (25/155. Abnormal MRI was seen in 32/157 (20.4% of patients; accuracy of picking abnormality by MRI, when EEG was abnormal, was 24.8% (P<0.05. Conclusion: Our findings indicate that clinical examination and EEG results are good indicators for neuroimaging, and these can be used as one of the criteria for ordering neuroimaging in new-onset seizures.

  16. Evaluation of human mental stress states based on wavelet package transformation and nonlinear analysis of EEG signals%利用EEG信号的小波包变换与非线性分析实现精神疲劳状态的判定

    Institute of Scientific and Technical Information of China (English)

    韩清鹏

    2013-01-01

    EEG(脑电)信号的4个节律(δ波、θ波、α波、β波)与人的精神疲劳状态有对应关系,不同节律的能量值及其非线性特征参数可以用于疲劳状态的判定.本文首先利用小波包分解与重构技术,构造了以“db20”为基小波函数的6层分解,得到EEG信号的4个节律.然后,对4个节律信号分别计算相应的节律的频带能量比例值,这些频带能量比例值作为对人体精神状态进行评价的量化指标.通过计算EEG信号α波的非线性特征参数,包括最大Lyapunov指数、近似熵、复杂度,并将这些非线性特征参数组成疲劳状态的综合评估判据,可以实现疲劳状态的判定.10组EEG信号的分析结果表明了该本文方法的有效性,其中对疲劳和非疲劳状态的判定准确率较高,而对轻微疲劳、中等疲劳和严重疲劳三种状态的准确区分稍差一些.%The positive correlation is known between the four rhythms of human Electroencephalogram ( EEG) signals including 8 wave, 8 wave, a wave and (3 wave and human mental stress states. So the energy values of the four rhythms of EEG together with their nonlinear parameters can be used to evaluate mental stress states. Here, the four rhythms of EEG was firstly reconstructed by using the technique of wavelet package transformation, where a 6-level-frame was achieved to decompose the original EEG signal with help of the basis wavelet function of " db20". Then, the corresponding frequency-band energy ratio ( FBER) of each rhythm was calculated and used to estimate states of mental stress quantitatively. Some nonlinear parameters of a wave including maximum Lyapunov exponent, approximated entropy and complexity level were also calculated and a synthesized evaluating criterion was made to determine human mental stress states. The proposed method was verified to be effective with 10 sets of EEG data. It was shown that its accuracy is higher when evaluating fatigue or non-fatigue states

  17. Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal

    Directory of Open Access Journals (Sweden)

    Paulchamy Balaiah

    2012-01-01

    Full Text Available Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time, time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA, Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI. Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors

  18. Detection of neonatal EEG seizure using multichannel matching pursuit.

    Science.gov (United States)

    Khlif, M S; Mesbah, M; Boashash, B; Colditz, P

    2008-01-01

    It is unusual for a newborn to have the classic "tonic-clonic" seizure experienced by adults and older children. Signs of seizure in newborns are either subtle or may become clinically silent. Therefore, the electroencephalogram (EEG) is becoming the most reliable tool for detecting neonatal seizure. Being non-stationary and multicomponent, EEG signals are suitably analyzed using time-frequency (TF) based methods. In this paper, we present a seizure detection method using a new measure based on the matching pursuit (MP) decomposition of EEG data. Signals are represented in the TF domain where seizure structural characteristics are extracted to form a new coherent TF dictionary to be used in the MP decomposition. A new approach to set data-dependent thresholds, used in the seizure detection process, is proposed. To enhance the performance of the detector, the concept of areas of incidence is utilized to determine the geometrical correlation between EEG recording channels.

  19. SVM detection of epileptiform activity in routine EEG.

    LENUS (Irish Health Repository)

    Kelleher, Daniel

    2010-01-01

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

  20. Prognostic value of dynamic electroencephalogram in comatose patients with different diseased regions

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    BACKGROUND: It has proved that dynamic electroencephalogram (EEG) is definite in judging the outcome of ischemic hypoxic comatose patients, EEG is more sensitive to the cortical affection, but not sensitive to the subcortical and brainstem affections, thus it is necessary to clarify the indications of this technique in the clinical application.OBJECTIVE: To observe and compare the prognostic value of dynamic EEG and Glasgow coma score in comatose patients with different diseased region.DESIGN: A clinical case-controlled observation.SETTING: Union Hospital of Fujian Medical University.PARTICIPANTS: Sixty-eight comatose patients were selected from the Union Hospital affiliated to Fujian Medical University from June 1998 to January 2005. The diseased regions were identified using cranial CT (n =43) or MR (n =25). According to different primarily diseased regions, the comatose patients were divided into two groups: ① brainstem affection group (n =23): 13 males and 10 females, 14 - 62 years of age; ②diffuse cortical affection group (n =45): 28 males and 17 females, 23 - 75 years of age.METHODS: The dynamic EEG and Glasgow coma score were examined in the 45 comatose patients with primarily cortical affection and 22 comatose patients with primarily brainstem affection at acute phase. The patients were followed-up for 3 months to observe the outcome, The termination of outcome judgment was 3 months after attack or the death. The clinical outcome was classified as complete rehabilitation, survived with disability, death or vegetative state. Correlations of dynamic EEG and Glasgow coma score with the outcome of patients were analyzed. The correlations of dynamic EEG grades and Glasgow coma scores with the outcome were analyzed, and the prognostic value of dynamic EEG grades was compared between the two groups.MAIN OUTCOME MEASURES: ① Correlations of dynamic EEG and Glasgow coma score with the outcome of patients; ② Comparison of the prognostic value of dynamic EEG

  1. An empirical EEG analysis in brain death diagnosis for adults.

    Science.gov (United States)

    Chen, Zhe; Cao, Jianting; Cao, Yang; Zhang, Yue; Gu, Fanji; Zhu, Guoxian; Hong, Zhen; Wang, Bin; Cichocki, Andrzej

    2008-09-01

    Electroencephalogram (EEG) is often used in the confirmatory test for brain death diagnosis in clinical practice. Because EEG recording and monitoring is relatively safe for the patients in deep coma, it is believed to be valuable for either reducing the risk of brain death diagnosis (while comparing other tests such as the apnea) or preventing mistaken diagnosis. The objective of this paper is to study several statistical methods for quantitative EEG analysis in order to help bedside or ambulatory monitoring or diagnosis. We apply signal processing and quantitative statistical analysis for the EEG recordings of 32 adult patients. For EEG signal processing, independent component analysis (ICA) was applied to separate the independent source components, followed by Fourier and time-frequency analysis. For quantitative EEG analysis, we apply several statistical complexity measures to the EEG signals and evaluate the differences between two groups of patients: the subjects in deep coma, and the subjects who were categorized as brain death. We report statistically significant differences of quantitative statistics with real-life EEG recordings in such a clinical study, and we also present interpretation and discussions on the preliminary experimental results.

  2. EEG power spectral measurements comparing normal and "thatcherized" faces.

    Science.gov (United States)

    Gersenowies, Jorge; Marosi, Erzsebet; Cansino, Selene; Rodriguez, Mario

    2010-08-01

    In this paper we have made a broadband analysis to detect the electroencephalogram (EEG) frequencies that change selectively during the presentation of normal and "thatcherized" faces. Referential recordings to linked ears were obtained in 21 leads in 48 right-handed healthy male volunteers. Increase of delta power (1.75-3.91 Hz) was observed, related to the detection of distortion in faces at bifrontal and left temporoparietal cortex. The other bands had no contribution, when normal and modified faces were compared. These results support our hypothesis that a change in EEG power spectral may be related to discrimination between normal and thatcherized faces.

  3. Development of a Mobile EEG-based Biometric Authentication System

    DEFF Research Database (Denmark)

    Klonovs, Juris; Petersen, Christoffer Kjeldgaard; Olesen, Henning

    In recent years the need for greater security for storing personal and business data or accessing corporate networks on mobile devices is growing rapidly, and one of the potential solutions is to employ the innovative biometric authentication techniques. This paper presents the development...... of a mobile biometric authentication system based on electroencephalogram (EEG) recordings in combination with already proven technologies such as facial detection and near-field communication (NFC). The overall goal of this work is to fill the gap between mobile web technologies and wireless EEG devices...

  4. SCoT: a Python toolbox for EEG source connectivity

    OpenAIRE

    Billinger, Martin; Brunner, Clemens; Müller-Putz, Gernot R.

    2014-01-01

    Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This toolbox implements routines for blind source...

  5. Instantaneous frequency based newborn EEG seizure characterisation

    Science.gov (United States)

    Mesbah, Mostefa; O'Toole, John M.; Colditz, Paul B.; Boashash, Boualem

    2012-12-01

    The electroencephalogram (EEG), used to noninvasively monitor brain activity, remains the most reliable tool in the diagnosis of neonatal seizures. Due to their nonstationary and multi-component nature, newborn EEG seizures are better represented in the joint time-frequency domain than in either the time domain or the frequency domain. Characterising newborn EEG seizure nonstationarities helps to better understand their time-varying nature and, therefore, allow developing efficient signal processing methods for both modelling and seizure detection and classification. In this article, we used the instantaneous frequency (IF) extracted from a time-frequency distribution to characterise newborn EEG seizures. We fitted four frequency modulated (FM) models to the extracted IFs, namely a linear FM, a piecewise-linear FM, a sinusoidal FM, and a hyperbolic FM. Using a database of 30-s EEG seizure epochs acquired from 35 newborns, we were able to show that, depending on EEG channel, the sinusoidal and piecewise-linear FM models best fitted 80-98% of seizure epochs. To further characterise the EEG seizures, we calculated the mean frequency and frequency span of the extracted IFs. We showed that in the majority of the cases (>95%), the mean frequency resides in the 0.6-3 Hz band with a frequency span of 0.2-1 Hz. In terms of the frequency of occurrence of the four seizure models, the statistical analysis showed that there is no significant difference( p = 0.332) between the two hemispheres. The results also indicate that there is no significant differences between the two hemispheres in terms of the mean frequency ( p = 0.186) and the frequency span ( p = 0.302).

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

    Science.gov (United States)

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

    2013-01-01

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

  7. Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity

    OpenAIRE

    Onton, Julie A.; Dae Y. Kang; Coleman, Todd P.

    2016-01-01

    Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and ...

  8. Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages With Differential Electrodermal Activity

    OpenAIRE

    Onton, Julie A.; Dae Y. Kang; Coleman, Todd P.

    2016-01-01

    Brain activity during sleep is powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies, and o...

  9. Ischemic injury suppresses hypoxia-induced electrographic seizures and the background EEG in a rat model of perinatal hypoxic-ischemic encephalopathy

    OpenAIRE

    2015-01-01

    The relationship among neonatal seizures, abnormalities of the electroencephalogram (EEG), brain injury, and long-term neurological outcome (e.g., epilepsy) remains controversial. The effects of hypoxia alone (Ha) and hypoxia-ischemia (HI) were studied in neonatal rats at postnatal day 7; both models generate EEG seizures during the 2-h hypoxia treatment, but only HI causes an infarct with severe neuronal degeneration. Single-channel, differential recordings of acute EEG seizures and backgrou...

  10. The Spatial Equivalence Between Wavelet Decomposition and Phase Space Embedding of EEG

    Institute of Scientific and Technical Information of China (English)

    YOU Rong-yi; HUANG Xiao-jing

    2008-01-01

    Using both the wavelet decomposition and the phase space embedding, the phase trajectories of electroencephalogram (EEG) is described. It is illustrated based on the present work,that is,the wavelet decomposition of EEG is essentially a projection of EEG chaotic attractor onto the wavelet space opened by wavelet filter vectors, which is in correspondence with the phase space embedding of the same EEG. In other words, wavelet decomposition and phase space embedding are equivalent in methodology. Our experimental results show that in both the wavelet space and the embedded space the structure of phase trajectory of EEG is similar to each other. These results demonstrate that wavelet decomposition is effective on characterizing EEG time series.

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

    Directory of Open Access Journals (Sweden)

    Arjon Turnip

    2014-12-01

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

  12. Transcranial electrical stimulation accelerates human sleep homeostasis.

    Directory of Open Access Journals (Sweden)

    Davide Reato

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

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

    Science.gov (United States)

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

    2017-07-21

    Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results. EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance. Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics. . © 2017 IOP Publishing Ltd.

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

    NARCIS (Netherlands)

    Olesen, S.S.; Hansen, T.M.; Graversen, C.; Steimle, K.; Wilder-Smith, O.H.G.; Drewes, A.M.

    2011-01-01

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

  15. Ambient temperature during torpor affects NREM sleep EEG during arousal episodes in hibernating European ground squirrels

    NARCIS (Netherlands)

    Strijkstra, AM; Daan, S

    1997-01-01

    Ambient temperature (T-a) systematically affects the frequency of arousal episodes in mammalian hibernation. This variation might hypothetically be attributed to temperature effects on the rate of sleep debt increase in torpor. We studied this rate by recording sleep electroencephalogram (EEG) in

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

    NARCIS (Netherlands)

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

    1989-01-01

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

  17. A social conflict increases EEG slow-wave activity during subsequent sleep

    NARCIS (Netherlands)

    Meerlo, P; de Bruin, EA; Strijkstra, AM; Daan, S

    2001-01-01

    Electroencephalogram (EEG) slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep is widely viewed as an indicator of sleep debt and sleep intensity. In a previous study, we reported a strong increase in SWA during NREM sleep after a social conflict in rats. To test whether this

  18. Speech Presentation Cues Moderate Frontal EEG Asymmetry in Socially Withdrawn Young Adults

    Science.gov (United States)

    Cole, Claire; Zapp, Daniel J.; Nelson, S. Katherine; Perez-Edgar, Koraly

    2012-01-01

    Socially withdrawn individuals display solitary behavior across wide contexts with both unfamiliar and familiar peers. This tendency to withdraw may be driven by either past or anticipated negative social encounters. In addition, socially withdrawn individuals often exhibit right frontal electroencephalogram (EEG) asymmetry at baseline and when…

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

    DEFF Research Database (Denmark)

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

    2011-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  1. Crepuscular rhythms of EEG sleep-wake in a hystricomorph rodent, Octodon degus

    NARCIS (Netherlands)

    Kas, M J; Edgar, D M

    1998-01-01

    Sleep-wake circadian rhythms are well documented for nocturnal rodents, but little is known about sleep regulation in diurnal or crepuscular rodent species. This study examined the circadian sleep-wake rhythms in Octodon degus by means of electroencephalogram (EEG) analysis. Recordings were made fro

  2. An Investigation of Cerebral Lateral Functioning and the EEG. Final Report.

    Science.gov (United States)

    Metcalf, David R.

    Forty-two volunteer subjects, mostly young adults, participated in developing a methodology for studying cognitive processes and cerebral lateral functions in relation to individual cognitive styles and age. Four test batteries were developed and refined in this study: the adult cognitive, the adult electroencephalogram (EEG), the children's…

  3. EEG PHASE RESET OF THE DEFAULT MODE NETWORK

    Directory of Open Access Journals (Sweden)

    Robert W. Thatcher

    2014-07-01

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

  4. Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction: a review

    Science.gov (United States)

    Quitadamo, L. R.; Cavrini, F.; Sbernini, L.; Riillo, F.; Bianchi, L.; Seri, S.; Saggio, G.

    2017-02-01

    Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2012-02-01

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

  7. Epileptiform activity in the electroencephalogram of 6-year-old children of women with epilepsy

    Directory of Open Access Journals (Sweden)

    Unnikrishnan Krishnan Syam

    2016-01-01

    Full Text Available Purpose: To study the epileptiform discharges (EDs in the electroencephalogram (EEG of 6-8-year-old children of women with epilepsy (WWE. Materials and Methods: All children born to women with epilepsy and prospectively followed up through the Kerala Registry of Epilepsy and Pregnancy (KREP, aged 6-8 years, were invited (n = 532. Out of the 254 children who responded, clinical evaluations and a 30-min digital 18 channel EEG were completed in 185 children. Results: Of the 185 children examined, 37 (20% children (19 males, 18 females had ED in their EEG. The EDs were generalized in 7 children, and focal in 30 children. The EDs were present in the sleep record only of 16 (43% children and in the awake record only of 6 (16% children. Out of the 94 children for whom seizure history was available, 7 children (7.4% had seizures (neonatal seizures: 4, febrile seizure: 1, and single nonfebrile seizure: 2 and none had history of epilepsy or recurrent nonfebrile seizures. The odds ratio (OR for occurrence of ED in the EEG was significantly higher for children of WWE [OR = 3.5, 95% confidence interval (CI 2.3-6.0] when compared to the published data for age-matched children of mothers without epilepsy. There was no association between the occurrence of ED and the children′s maternal characteristics [epilepsy syndrome, seizures during pregnancy, maternal intelligence quotient (IQ] or the children′s characteristics [antenatal exposure to specific antiepileptic drugs (AEDs, birth weight, malformations, IQ]. Conclusion: Children of WWE have a higher risk of epileptiform activity in their EEG when compared to healthy children in the community though none had recurrent seizures.

  8. Electroencephalogram-based scaling of multifocal visual evoked potentials: effect on intersubject amplitude variability.

    Science.gov (United States)

    Klistorner, A I; Graham, S L

    2001-08-01

    The interindividual variability of the visual evoked potential (VEP) has been recognized as a problem for interpretation of clinical results. This study examines whether VEP variability can be reduced by scaling responses according to underlying electroencephalogram (EEG) activity. A multifocal objective perimeter provided different random check patterns to each of 58 points extending out to 32 degrees nasally. A multichannel VEP was recorded (bipolar occipital cross electrodes, 7 min/eye). One hundred normal subjects (age 58.9 +/- 10.7 years) were tested. The amplitude and inter-eye asymmetry coefficient for each point of the field was calculated. VEP signals were then normalized according to underlying EEG activity recorded using Fourier transform to quantify EEG levels. High alpha-rhythm and electrocardiogram contamination were removed before scaling. High intersubject variability was present in the multifocal VEP, with amplitude in women on average 33% larger than in men. The variability for all left eyes was 42.2% +/- 3.9%, for right eyes 41.7% +/- 4.4% (coefficient of variability [CV]). There was a strong correlation between EEG activity and the amplitude of the VEP (left eye, r = 0.83; P < 0.001; right eye, r = 0.82; P < 0.001). When this was used to normalize VEP results, the CVs dropped to 24.6% +/- 3.1% (P < 0.0001) and 24.0% +/- 3.2% (P < 0.0001), respectively. The gender difference was effectively removed. Using underlying EEG amplitudes to normalize an individual's VEP substantially reduces intersubject variability, including differences between men and women. This renders the use of a normal database more reliable when applying the multifocal VEP in the clinical detection of visual field changes.

  9. Multichannel EEG Visualization

    NARCIS (Netherlands)

    Caat, Michael ten

    2008-01-01

    Electroencephalography (EEG) measures electrical brain activity by electrodes attached to the scalp. Multichannel EEG refers to a measurement with a large number of electrodes. EEG has clinical as well as scientific applications, including neurology, psychology, pharmacy, linguistics, and biology.

  10. Detection of near-threshold sounds is independent of EEG phase in common frequency bands

    Directory of Open Access Journals (Sweden)

    Benedikt eZoefel

    2013-05-01

    Full Text Available Low-frequency oscillations in the electroencephalogram (EEG are thought to reflect periodic excitability changes of large neural networks. Consistent with this notion, detection probability of near-threshold somatosensory, visual, and auditory targets has been reported to co-vary with the phase of oscillations in the EEG. In audition, entrainment of δ-oscillations to the periodic occurrence of sounds has been suggested to function as a mechanism of attentional selection. Here, we examine in humans whether the detection of brief near-threshold sounds in quiet depends on the phase of EEG oscillations. When stimuli were presented at irregular intervals, we did not find a systematic relationship between detection probability and phase. When stimuli were presented at regular intervals (2-s, reaction times were significantly shorter and we observed phase entrainment of EEG oscillations corresponding to the frequency of stimulus presentation (0.5 Hz, revealing an adjustment of the system to the regular stimulation. The amplitude of the entrained oscillation was higher for hits than for misses, suggesting a link between entrainment and stimulus detection. However, detection was independent of phase at frequencies ≥ 1 Hz. Furthermore, we show that when the data are analyzed using acausal, though common, algorithms, an apparent ‘entrainment’ of the δ-phase to presented stimuli emerges and detection probability appears to depend on δ-phase, similar to reports in the literature. We show that these effects are artifacts from phase distortion at stimulus onset by contamination with the event-related potential, which differs markedly for hits and misses. This highlights the need to carefully deal with this common problem, since otherwise it might bias and mislead this exciting field of research.

  11. Synchronization Phenomena and Epoch Filter of Electroencephalogram

    Science.gov (United States)

    Matani, Ayumu

    Nonlinear electrophysiological synchronization phenomena in the brain, such as event-related (de)synchronization, long distance synchronization, and phase-reset, have received much attention in neuroscience over the last decade. These phenomena contain more electrical than physiological keywords and actually require electrical techniques to capture with electroencephalography (EEG). For instance, epoch filters, which have just recently been proposed, allow us to investigate such phenomena. Moreover, epoch filters are still developing and would hopefully generate a new paradigm in neuroscience from an electrical engineering viewpoint. Consequently, electrical engineers could be interested in EEG once again or from now on.

  12. The study of evolution and depression of the alpha-rhythm in the human brain EEG by means of wavelet-based methods

    Science.gov (United States)

    Runnova, A. E.; Zhuravlev, M. O.; Khramova, M. V.; Pysarchik, A. N.

    2017-04-01

    We study the appearance, development and depression of the alpha-rhythm in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. The new method based on continuous wavelet transform allows to estimate the energy contribution of various components, including the alpha rhythm, in the general dynamics of the electrical activity of the projections of various areas of the brain. The decision-making process by observe ambiguous images is characterized by specific oscillatory alfa-rhytm patterns in the multi-channel EEG data. We have shown the repeatability of detected principles of the alpha-rhythm evolution in a data of group of 12 healthy male volunteers.

  13. Influence of vertical dimension of occlusion changes on the electroencephalograms of complete denture wearers.

    Science.gov (United States)

    Matsuda, Risa; Yoneyama, Yoshikazu; Morokuma, Masakazu; Ohkubo, Chikahiro

    2014-04-01

    The present study was conducted to identify how changes in the vertical dimension of occlusion (VDO) affect the sensory perception and activity of the brain in complete denture wearers using an electroencephalogram (EEG). Subjects were 21 individuals wearing complete dentures who regularly visited the Division of Prosthodontics at Tsurumi University Dental Hospital for checkups (12 males and 9 females, average age: 76.6). Based on their original dentures, two duplicate dentures with different VDO (-3mm and +5mm) were fabricated. EEG activity and occlusal force were measured before and after gum chewing with each denture in all subjects. Negative indicator scores for psychological conditions and stable neuronal activity (Dα) were calculated using EEG data. Statistical analysis was performed using the Wilcoxon test to compare changes in the sensory perception, activity of the brain, and occlusal force (α=0.05). After gum chewing with the +5-mm denture, a significant increase was observed in the negative indicator score (pdentures (p>0.05). A significant decrease was observed in the occlusal force between the original denture and the -3-mm denture (pcomplete denture. Copyright © 2014 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

  14. Modification of sleep-waking and electroencephalogram induced by vetiver essential oil inhalation.

    Science.gov (United States)

    Cheaha, Dania; Issuriya, Acharaporn; Manor, Rodiya; Kwangjai, Jackapun; Rujiralai, Thitima; Kumarnsit, Ekkasit

    2016-01-01

    Essential oils (EOs) have been claimed to modulate mental functions though the most of data were obtained from subjective methods of assessment. Direct effects of EO on brain function remained largely to be confirmed with scientific proof. This study aimed to demonstrate quantifiable and reproducible effects of commercial vetiver (Vetiveria zizanioides) EO inhalation on sleep-waking and electroencephalogram (EEG) patterns in adult male Wistar rats. The experiments were conducted during November 2013 - February 2014. The following electrode implantation on the skull, control, and treated animals were subjected for EEG recording while inhaling water and vetiver EO (20 and 200 µl), respectively. Fast Fourier transform was used for analysis of EEG power spectrum. One-way ANOVA analysis confirmed that vetiver EO inhalation significantly increased total waking and reduced slow-wave sleep time. Moreover, EO inhalation decreased alpha and beta1 activity in both frontal and parietal cortices and increased gamma activity in the frontal cortex. Changes in these frequencies began almost from the start of the inhalation. These data suggest refreshing properties of vetiver EO on electrical brain activity and alertness.

  15. Effects of incense on brain function: evaluation using electroencephalograms and event-related potentials.

    Science.gov (United States)

    Iijima, Mutsumi; Osawa, Mikio; Nishitani, Nobuyuki; Iwata, Makoto

    2009-01-01

    To evaluate the effect of the odor of incense on brain activity, electroencephalograms (EEGs) and event-related potentials (ERPs) in a push/wait paradigm were recorded in 10 healthy adults (aged 23-39 years) with normal olfactory function. EEG was recorded from 21 electrodes on the scalp, according to the International 10-20 system, and EEG power spectra were calculated by fast Fourier transform for 3 min before and during odor presentation. ERPs were recorded from 15 electrodes on the scalp before, during and after exposure to incense with intervals of 10 min. In a push/wait paradigm, two Japanese words, 'push' as the go stimulus and 'wait' as the no-go stimulus, appeared randomly on a CRT screen with equal probability. The subjects were instructed to push a button whenever the 'push' signal appeared. Fast alpha activity (10-13 Hz) increased significantly in bilateral posterior regions during incense exposure compared to that during rose oil exposure. The peak amplitudes of no-go P3 at Fz and Cz were significantly greater during incense inhalation. The latencies of go P3 and no-go P3, and the amplitude and latencies of no-go N2 did not change by exposure to the odors of both incense, rose and odorless air. These results suggest that the odor of incense may enhance cortical activities and the function of inhibitory processing of motor response.

  16. Prolonged menstruation and increased menstrual blood with generalized δ electroencephalogram power: A case report.

    Science.gov (United States)

    Peng, Fenghua; Zhang, Lianping

    2014-03-01

    Estradiol changes associated with the menstrual cycle have a great impact on brain activation. δ frequency mainly appears during normal sleep status or brain injury diseases, including encephalitis and mental confusion. The current case report presents a 51-year-old female with prolonged menstruation and increased menstrual blood volume whose electroencephalogram (EEG) recording demonstrated a rare generalized 3 Hz δ frequency band in the waking status. The patient had been suffering from heart palpitations and dizziness for 6 months and was receiving treatment in the Department of Neurology (Second Xiangya Hospital). The individual had been experiencing prolonged menstruation and increased menstrual blood volume for 6 years. Gynecologial examination revealed secondary anemia and hysteromyoma. Hemoglobin levels were decreased to 69 g/l. Physical and neurological examinations, and computed tomography results appeared normal. The EEG recording indicated a generalized 3 Hz δ frequency band with 30-80 μV power and a long-range δ frequency band when the patient was hyperventilating. The prolonged menstruation and increased menstrual blood volume may have induced the generalized δ frequency without brain injury. To the best of our knowledge, this is the first formal case report of prolonged menstruation and increased menstrual blood volume with the abnormality of δ EEG power.

  17. Wavelet analysis of learning and forgetting of photostimulation rhythms for a nonstationary electroencephalogram

    Science.gov (United States)

    Bozhokin, S. V.

    2010-09-01

    Quantitative parameters characterizing transient processes of mastering and forgetting of photostimulation (PST) rhythms for a nonstationary electroencephalogram (EEG) are developed on the basis of a continuous wavelet transformation. Nonstationarity factor K nst(μ), as well as rhythm mastering K M (μ) and confinement K C (μ) factors are calculated for various spectral ranges μ. Photoflash mastering time τ M = τ S + τ I , which is the sum of latent silence period τ S after PST actuation and the rhythm increasing period τ I is calculated. In the case of PST, the EEG rhythm retardation time τ R relative to the beginning of PST is calculated. Rhythm forgetting time τ F = τ P + τ D after PST actuation is the sum of the preservation time τ P of the corresponding rhythm over a certain time interval and its decay period τ D . The lag time τ L of the EEG signal relative to the PST signal after its removal is determined. The proposed method is used in quantitative analysis and classification of transient processes characterizing the properties of the central nervous system. Possible applications of the method in analysis of various nonstationary signals in physics are discussed.

  18. Cerebral monitoring during carotid endarterectomy using near-infrared diffuse optical spectroscopies and electroencephalogram

    Energy Technology Data Exchange (ETDEWEB)

    Shang Yu; Cheng Ran; Dong Lixin; Yu Guoqiang [Center for Biomedical Engineering, University of Kentucky, KY (United States); Ryan, Stephen J [Department of Neurology, University of Kentucky, KY (United States); Saha, Sibu P, E-mail: guoqiang.yu@uky.edu [Division of Cardiothoracic Surgery, University of Kentucky, KY (United States)

    2011-05-21

    Intraoperative monitoring of cerebral hemodynamics during carotid endarterectomy (CEA) provides essential information for detecting cerebral hypoperfusion induced by temporary internal carotid artery (ICA) clamping and post-CEA hyperperfusion syndrome. This study tests the feasibility and sensitivity of a novel dual-wavelength near-infrared diffuse correlation spectroscopy technique in detecting cerebral blood flow (CBF) and cerebral oxygenation in patients undergoing CEA. Two fiber-optic probes were taped on both sides of the forehead for cerebral hemodynamic measurements, and the instantaneous decreases in CBF and electroencephalogram (EEG) alpha-band power during ICA clamping were compared to test the measurement sensitivities of the two techniques. The ICA clamps resulted in significant CBF decreases (-24.7 {+-} 7.3%) accompanied with cerebral deoxygenation at the surgical sides (n = 12). The post-CEA CBF were significantly higher (+43.2 {+-} 16.9%) than the pre-CEA CBF. The CBF responses to ICA clamping were significantly faster, larger and more sensitive than EEG responses. Simultaneous monitoring of CBF, cerebral oxygenation and EEG power provides a comprehensive evaluation of cerebral physiological status, thus showing potential for the adoption of acute interventions (e.g., shunting, medications) during CEA to reduce the risks of severe cerebral ischemia and cerebral hyperperfusion syndrome.

  19. Sample size calculations in human electrophysiology (EEG and ERP) studies: A systematic review and recommendations for increased rigor.

    Science.gov (United States)

    Larson, Michael J; Carbine, Kaylie A

    2017-01-01

    There is increasing focus across scientific fields on adequate sample sizes to ensure non-biased and reproducible effects. Very few studies, however, report sample size calculations or even the information needed to accurately calculate sample sizes for grants and future research. We systematically reviewed 100 randomly selected clinical human electrophysiology studies from six high impact journals that frequently publish electroencephalography (EEG) and event-related potential (ERP) research to determine the proportion of studies that reported sample size calculations, as well as the proportion of studies reporting the necessary components to complete such calculations. Studies were coded by the two authors blinded to the other's results. Inter-rater reliability was 100% for the sample size calculations and kappa above 0.82 for all other variables. Zero of the 100 studies (0%) reported sample size calculations. 77% utilized repeated-measures designs, yet zero studies (0%) reported the necessary variances and correlations among repeated measures to accurately calculate future sample sizes. Most studies (93%) reported study statistical values (e.g., F or t values). Only 40% reported effect sizes, 56% reported mean values, and 47% reported indices of variance (e.g., standard deviations/standard errors). Absence of such information hinders accurate determination of sample sizes for study design, grant applications, and meta-analyses of research and whether studies were adequately powered to detect effects of interest. Increased focus on sample size calculations, utilization of registered reports, and presenting information detailing sample size calculations and statistics for future researchers are needed and will increase sample size-related scientific rigor in human electrophysiology research.

  20. EEG abnormalities in clinically diagnosed brain death organ donors in Iranian tissue bank.

    Science.gov (United States)

    Tavakoli, Seyed Amir Hossein; Khodadadi, Abbas; Azimi Saein, Amir Reza; Bahrami-Nasab, Hasan; Hashemi, Behnam; Tirgar, Niloufar; Nozary Heshmati, Behnaz

    2012-01-01

    Brain death is defined as the permanent, irreversible and concurrent loss of all brain and brain stem functions. Brain death diagnosis is based on clinical criteria and it is not routine to use paraclinical studies. In some countries, electroencephalogram (EEG) is performed in all patients for the determination of brain death while there is some skepticism in relying on EEG as a confirmatory test for brain death diagnosis. In this study, we assessed the validity of EEG and its abnormalities in brain death diagnosis. In this retrospective study, we used 153 EEGs from medical records of 89 brain death patients in organ procurement unit of the Iranian Tissue Bank admitted during 2002-2008. We extracted and analyzed information including EEGs, which were examined by a neurologist for waves, artifacts and EEG abnormalities. The mean age of the patients was 27.2±12.7 years. The most common cause of brain death was multiple traumas due to accident (65%). The most prevalent artifact was electrical transformer. 125 EEGs (82%) were isoelectric (ECS) and seven EEGs (5%) were depictive of some cerebral activity which upon repeat EEGs, they showed ECS patterns too. There was no relationship between cause of brain death and cerebral activity in EEGs of the patients. In this study, we could confirm ECS patterns in all brain death patients whose status had earlier been diagnosed clinically. Considering the results of this study, it seems sensible to perform EEG as a final confirmatory test as an assurance to the patients' families.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    Xiao-gang KANG

    2015-11-01

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

  3. [Classification of human sleep stages based on EEG processing using hidden Markov models].

    Science.gov (United States)

    Doroshenkov, L G; Konyshev, V A; Selishchev, S V

    2007-01-01

    The goal of this work was to describe an automated system for classification of human sleep stages. Classification of sleep stages is an important problem of diagnosis and treatment of human sleep disorders. The developed classification method is based on calculation of characteristics of the main sleep rhythms. It uses hidden Markov models. The method is highly accurate and provides reliable identification of the main stages of sleep. The results of automatic classification are in good agreement with the results of sleep stage identification performed by an expert somnologist using Rechtschaffen and Kales rules. This substantiates the applicability of the developed classification system to clinical diagnosis.

  4. Estimation of the effective and functional human cortical connectivity with structural equation modeling and directed transfer function applied to high-resolution EEG.

    Science.gov (United States)

    Astolfi, Laura; Cincotti, Febo; Mattia, Donatella; Salinari, Serenella; Babiloni, Claudio; Basilisco, Alessandra; Rossini, Paolo Maria; Ding, Lei; Ni, Ying; He, Bin; Marciani, Maria Grazia; Babiloni, Fabio

    2004-12-01

    Different brain imaging devices are presently available to provide images of the human functional cortical activity, based on hemodynamic, metabolic or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions are interconnected. The concept of brain connectivity plays a central role in the neuroscience, and different definitions of connectivity, functional and effective, have been adopted in literature. While the functional connectivity is defined as the temporal coherence among the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally among cortical sites. The structural equation modeling (SEM) is the most used method to estimate effective connectivity in neuroscience, and its typical application is on data related to brain hemodynamic behavior tested by functional magnetic resonance imaging (fMRI), whereas the directed transfer function (DTF) method is a frequency-domain approach based on both a multivariate autoregressive (MVAR) modeling of time series and on the concept of Granger causality. This study presents advanced methods for the estimation of cortical connectivity by applying SEM and DTF on the cortical signals estimated from high-resolution electroencephalography (EEG) recordings, since these signals exhibit a higher spatial resolution than conventional cerebral electromagnetic measures. To estimate correctly the cortical signals, we used a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual MRI, a distributed source model and a regularized linear inverse source estimates of cortical current density. Before the application of SEM and DTF methodology to the cortical waveforms estimated from high-resolution EEG data, we performed a simulation study, in which different main factors

  5. Intracranial EEG correlates of expectancy and memory formation in the human hippocampus and nucleus accumbens

    NARCIS (Netherlands)

    Axmacher, N.; Cohen, M.X.; Fell, J.; Haupt, S.; Dümpelmann, M.; Elger, C.E.; Schlaepfer, T.E.; Lenartz, D.; Sturm, V.; Ranganath, C.

    2010-01-01

    The human brain is adept at anticipating upcoming events, but in a rapidly changing world, it is essential to detect and encode events that violate these expectancies. Unexpected events are more likely to be remembered than predictable events, but the underlying neural mechanisms for these effects

  6. The Detection of Hidden Periodicities in EEG

    Institute of Scientific and Technical Information of China (English)

    YOU Rong-yi

    2007-01-01

    Abstract.A novel method for detecting the hidden periodicities in EEG is proposed.By using a width-varying window in the time domain, the structure function of EEG time series is defined. It is found that the minima of the structure function, within a finite window width, can be found regularly, which indicate that there are some certain periodicities associated with EEG time series. Based on the structure function, a further quadratic structure function of EEG time series is defined. By quadratic structure function, it can be seen that the periodicities of EEG become more obvious, moreover, the period of EEG can be determined accurately. These results will be meaningful for studying the neuron activity inside the human brain.

  7. High-Resolution Movement EEG Classification

    Directory of Open Access Journals (Sweden)

    Jakub Štastný

    2007-01-01

    Full Text Available The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%, but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

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

    Science.gov (United States)

    1994-11-01

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  9. Approximate entropy and support vector machines for electroencephalogram signal classification*****

    Institute of Scientific and Technical Information of China (English)

    Zhen Zhang; Yi Zhou; Ziyi Chen; Xianghua Tian; Shouhong Du; Ruimei Huang

    2013-01-01

    The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epi-leptic seizures were included in this study. They were al diagnosed with neocortex localized epi-lepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was con-structed with the approximate entropy extracted from one epileptic case, and then electroence-phalogram waves of the other three cases were classified, reaching a 93.33%accuracy rate. Our findings suggest that the use of approximate entropy al ows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.

  10. 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.

  11. Estimation of coupling between oscillators from short time series via phase dynamics modeling: limitations and application to EEG data

    CERN Document Server

    Smirnov, D A; Velazquez, J L P; Wennberg, R A; Bezruchko, B P

    2005-01-01

    We demonstrate in numerical experiments that estimators of strength and directionality of coupling between oscillators based on modeling of their phase dynamics [D.A. Smirnov and B.P. Bezruchko, Phys. Rev. E 68, 046209 (2003)] are widely applicable. Namely, although the expressions for the estimators and their confidence bands are derived for linear uncoupled oscillators under the influence of independent sources of Gaussian white noise, they turn out to allow reliable characterization of coupling from relatively short time series for different properties of noise, significant phase nonlinearity of the oscillators, and non-vanishing coupling between them. We apply the estimators to analyze a two-channel human intracranial epileptic electroencephalogram (EEG) recording with the purpose of epileptic focus localization.

  12. Chaotic time series analysis of vision evoked EEG

    Science.gov (United States)

    Zhang, Ningning; Wang, Hong

    2010-01-01

    To investigate the human brain activities for aesthetic processing, beautiful woman face picture and ugly buffoon face picture were applied. Twelve subjects were assigned the aesthetic processing task while the electroencephalogram (EEG) was recorded. Event-related brain potential (ERP) was required from the 32 scalp electrodes and the ugly buffoon picture produced larger amplitudes for the N1, P2, N2, and late slow wave components. Average ERP from the ugly buffoon picture were larger than that from the beautiful woman picture. The ERP signals shows that the ugly buffoon elite higher emotion waves than the beautiful woman face, because some expression is on the face of the buffoon. Then, chaos time series analysis was carried out to calculate the largest Lyapunov exponent using small data set method and the correlation dimension using G-P algorithm. The results show that the largest Lyapunov exponents of the ERP signals are greater than zero, which indicate that the ERP signals may be chaotic. The correlations dimensions coming from the beautiful woman picture are larger than that from the ugly buffoon picture. The comparison of the correlations dimensions shows that the beautiful face can excite the brain nerve cells. The research in the paper is a persuasive proof to the opinion that cerebrum's work is chaotic under some picture stimuli.

  13. Comparative analysis of cognitive tasks for modeling mental workload with electroencephalogram.

    Science.gov (United States)

    Hwang, Taeho; Kim, Miyoung; Hwangbo, Minsu; Oh, Eunmi

    2014-01-01

    Previous electroencephalogram (EEG) studies have shown that cognitive workload can be estimated by using several types of cognitive tasks. In this study, we attempted to characterize cognitive tasks that have been used to manipulate workload for generating classification models. We carried out a comparative analysis between two representative types of working memory tasks: the n-back task and the mental arithmetic task. Based on experiments with 7 healthy subjects using Emotiv EPOC, we compared the consistency, robustness, and efficiency of each task in determining cognitive workload in a short training session. The mental arithmetic task seems consistent and robust in manipulating clearly separable high and low levels of cognitive workload with less training. In addition, the mental arithmetic task shows consistency despite repeated usage over time and without notable task adaptation in users. The current study successfully quantifies the quality and efficiency of cognitive workload modeling depending on the type and configuration of training tasks.

  14. A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements

    Science.gov (United States)

    Kilicarslan, Atilla; Grossman, Robert G.; Contreras-Vidal, Jose Luis

    2016-04-01

    Objective. Non-invasive measurement of human neural activity based on the scalp electroencephalogram (EEG) allows for the development of biomedical devices that interface with the nervous system for scientific, diagnostic, therapeutic, or restorative purposes. However, EEG recordings are often considered as prone to physiological and non-physiological artifacts of different types and frequency characteristics. Among them, ocular artifacts and signal drifts represent major sources of EEG contamination, particularly in real-time closed-loop brain-machine interface (BMI) applications, which require effective handling of these artifacts across sessions and in natural settings. Approach. We extend the usage of a robust adaptive noise cancelling (ANC) scheme ({H}∞ filtering) for removal of eye blinks, eye motions, amplitude drifts and recording biases simultaneously. We also characterize the volume conduction, by estimating the signal propagation levels across all EEG scalp recording areas due to ocular artifact generators. We find that the amplitude and spatial distribution of ocular artifacts vary greatly depending on the electrode location. Therefore, fixed filtering parameters for all recording areas would naturally hinder the true overall performance of an ANC scheme for artifact removal. We treat each electrode as a separate sub-system to be filtered, and without the loss of generality, they are assumed to be uncorrelated and uncoupled. Main results. Our results show over 95-99.9% correlation between the raw and processed signals at non-ocular artifact regions, and depending on the contamination profile, 40-70% correlation when ocular artifacts are dominant. We also compare our results with the offline independent component analysis and artifact subspace reconstruction methods, and show that some local quantities are handled better by our sample-adaptive real-time framework. Decoding performance is also compared with multi-day experimental data from 2 subjects

  15. A case of Dravet syndrome with cortical myoclonus indicated by jerk-locked back-averaging of electroencephalogram data.

    Science.gov (United States)

    Kobayashi, Yoshinori; Hanaoka, Yoshiyuki; Akiayma, Tomoyuki; Ohmori, Iori; Ouchida, Mamoru; Yamamoto, Toshiyuki; Oka, Makio; Yoshinaga, Harumi; Kobayashi, Katsuhiro

    2017-01-01

    We report a female patient with Dravet syndrome (DS) with erratic segmental myoclonus, the origin of which was first identified in the cerebral cortex by the detection of myoclonus-associated cortical discharges. The discharges were disclosed through jerk-locked back-averaging of electroencephalogram (EEG) data using the muscle activity of myoclonus as triggers. The detected spikes on the contralateral parieto-central region preceded myoclonic muscle activity in the forearms by 28-46ms. The patient was six months old at the time of examination, and was developing normally before seizure onset at two months of age. She suffered from recurrent afebrile or febrile generalized tonic-clonic seizures that often developed into status epilepticus. Interictal EEG and brain magnetic resonance imaging (MRI) showed no significant findings. The amplitudes of the somatosensory-evoked potentials were not extremely large. She has a chromosomal microdeletion involving SCN1A and adjacent genes.

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    subsequently stratified by age group (± 50 years), gender, duration of diabetes (± 20 years), and hypoglycemia awareness status (normal/impaired awareness of hypoglycemia). Results: An increase in the log amplitude of the delta, theta, and alpha band and a decrease in the alpha band centroid frequency...... and the peak frequency of the unified theta-alpha band constituted the most significant hypoglycemia indicators (all p changes remained stable across all strata. Conclusions: Hypoglycemia-associated EEG changes remain stable across age group, gender, duration of diabetes......Introduction: Neuroglycopenia in type 1 diabetes mellitus (T1DM) results in reduced cognition, unconsciousness, seizures, and possible death. Characteristic changes in the electroencephalogram (EEG) can be detected even in the initial stages. This may constitute a basis for a hypoglycemia alarm...

  17. An SSVEP based BCI to control a humanoid robot by using portable EEG device.

    Science.gov (United States)

    Güneysu, Arzu; Akin, H Levent

    2013-01-01

    Brain Computer Interfaces (BCIs) are systems that allow human subjects to interact with the environment by interpreting brain signals into machine commands. This work provides a design for a BCI to control a humanoid robot by using signals obtained from the Emotiv EPOC, a portable electroencephalogram (EEG) device with 14 electrodes and sampling rate of 128 Hz. The main objective is to process the neuroelectric responses to an externally driven stimulus and generate control signals for the humanoid robot Nao accordingly. We analyze steady-state visually evoked potential (SSVEP) induced by one of four groups of light emitting diodes (LED) by using two distinct signals obtained from the two channels of the EEG device which reside on top of the occipital lobe. An embedded system is designed for generating pulse width modulated square wave signals in order to flicker each group of LEDs with different frequencies. The subject chooses the direction by looking at one of these groups of LEDs that represent four directions. Fast Fourier Transform and a Gaussian model are used to detect the dominant frequency component by utilizing harmonics and neighbor frequencies. Then, a control signal is sent to the robot in order to draw a fixed sized line in that selected direction by BCI. Experimental results display satisfactory performance where the correct target is detected 75% of the time on the average across all test subjects without any training.

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

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    2017-03-01

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

  19. Correlation of continuous electroencephalogram with clinical assessment scores in acute stroke patients

    Institute of Scientific and Technical Information of China (English)

    Xiyan Xin; Ying Gao; Hua Zhang; Kegang Cao; Yongmei Shi

    2012-01-01

    Objective To compare electroencephalogram (EEG) symmetry values between stroke patients with different 28-day outcomes,and to assess correlations between clinical characteristics and 28-day outcomes.Methods Twentytwo patients presenting with acute ischemic stroke and persistent neurological deficits at EEG recording were incrementally included.At 28 days after admission,the modified Rankin scale (mRS) was used to evaluate the outcomes,based on which the patients were divided into two a posteriori groups,mRS =6 and mRS <6.Student's t-test was used to compare these two groups in terms of brain symmetry index (BSI),National Institutes of Health stroke scale (NIHSS),Glasgow coma scale (GCS) and acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) assessed at admission.Then EEG parameters,NIHSS,GCS and APACHE Ⅱ were correlated with the mRS.Results There were significant differences in BSI,NIHSS,GCS,and APACHE Ⅱ between the two groups.Survivors had lower BSI,NIHSS and APACHE Ⅱ,and higher GCS values,compared with patients who died within 28 days after admission.Besides,BSI at admission had a positive correlation with mRS at 28 days (r =0.441,P =0.040).NIHSS and APACHE Ⅱ were also correlated with mRS (r =0.736,P <0.000 1;r =0.667,P =0.001,respectively).GCS at admission had a negative correlation with mRS (r =-0.656,P =0.001).Conclusion A higher BSI predicts a poorer short-term prognosis for stroke patients.Acute EEG monitoring may be of prognostic value for 28-day outcomes.The early prediction of functional outcomes after stroke may enhance clinical management and minimize short-term mortality.

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

    Science.gov (United States)

    Jäncke, Lutz; Alahmadi, Nsreen

    2016-04-13

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

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

    Science.gov (United States)

    Kawashima, Issaku; Kumano, Hiroaki

    2017-01-01

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

  2. Neural network classification of autoregressive features from electroencephalogram signals for brain computer interface design

    Science.gov (United States)

    Huan, Nai-Jen; Palaniappan, Ramaswamy

    2004-09-01

    In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.

  3. Pulsed ultrasound differentially stimulates somatosensory circuits in humans as indicated by EEG and FMRI.

    Directory of Open Access Journals (Sweden)

    Wynn Legon

    Full Text Available Peripheral somatosensory circuits are known to respond to diverse stimulus modalities. The energy modalities capable of eliciting somatosensory responses traditionally belong to mechanical, thermal, electromagnetic, and photonic domains. Ultrasound (US applied to the periphery has also been reported to evoke diverse somatosensations. These observations however have been based primarily on subjective reports and lack neurophysiological descriptions. To investigate the effects of peripherally applied US on human somatosensory brain circuit activity we recorded evoked potentials using electroencephalography and conducted functional magnetic resonance imaging of blood oxygen level-dependent (BOLD responses to fingertip stimulation with pulsed US. We found a pulsed US waveform designed to elicit a mild vibration sensation reliably triggered evoked potentials having distinct waveform morphologies including a large double-peaked vertex potential. Fingertip stimulation with this pulsed US waveform also led to the appearance of BOLD signals in brain regions responsible for somatosensory discrimination including the primary somatosensory cortex and parietal operculum, as well as brain regions involved in hierarchical somatosensory processing, such as the insula, anterior middle cingulate cortex, and supramarginal gyrus. By changing the energy profile of the pulsed US stimulus waveform we observed pulsed US can differentially activate somatosensory circuits and alter subjective reports that are concomitant with changes in evoked potential morphology and BOLD response patterns. Based on these observations we conclude pulsed US can functionally stimulate different somatosensory fibers and receptors, which may permit new approaches to the study and diagnosis of peripheral nerve injury, dysfunction, and disease.

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

    DEFF Research Database (Denmark)

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

    2012-01-01

    Evoked cortical responses do not follow a rigid input-output function but are dynamically shaped by intrinsic neural properties at the time of stimulation. Recent research has emphasized the role of oscillatory activity in determining cortical excitability. Here we employed EEG-guided transcranial...... magnetic stimulation (TMS) during non-rapid eye movement sleep to examine whether the spontaneous...

  5. No Effects of Acute Exposure to Wi-Fi Electromagnetic Fields on Spontaneous EEG Activity and Psychomotor Vigilance in Healthy Human Volunteers.

    Science.gov (United States)

    Zentai, Norbert; Csathó, Árpád; Trunk, Attila; Fiocchi, Serena; Parazzini, Marta; Ravazzani, Paolo; Thuróczy, György; Hernádi, István

    2015-12-01

    Mobile equipment use of wireless fidelity (Wi-Fi) signal modulation has increased exponentially in the past few decades. However, there is inconclusive scientific evidence concerning the potential risks associated with the energy deposition in the brain from Wi-Fi and whether Wi-Fi electromagnetism interacts with cognitive function. In this study we investigated possible neurocognitive effects caused by Wi-Fi exposure. First, we constructed a Wi-Fi exposure system from commercial parts. Dosimetry was first assessed by free space radiofrequency field measurements. The experimental exposure system was then modeled based on real geometry and physical characteristics. Specific absorption rate (SAR) calculations were performed using a whole-body, realistic human voxel model with values corresponding to conventional everyday Wi-Fi exposure (peak SAR10g level was 99.22 mW/kg with 1 W output power and 100% duty cycle). Then, in two provocation experiments involving healthy human volunteers we tested for two hypotheses: 1. Whether a 60 min long 2.4 GHz Wi-Fi exposure affects the spectral power of spontaneous awake electroencephalographic (sEEG) activity (N = 25); and 2. Whether similar Wi-Fi exposure modulates the sustained attention measured by reaction time in a computerized psychomotor vigilance test (PVT) (N = 19). EEG data were recorded at midline electrode sites while volunteers watched a silent documentary. In the PVT task, button press reaction time was recorded. No measurable effects of acute Wi-Fi exposure were found on spectral power of sEEG or reaction time in the psychomotor vigilance test. These results indicate that a single, 60 min Wi-Fi exposure does not alter human oscillatory brain function or objective measures of sustained attention.

  6. Mirror activity in the human brain while observing hand movements: a comparison between EEG desynchronization in the mu-range and previous fMRI results.

    Science.gov (United States)

    Perry, Anat; Bentin, Shlomo

    2009-07-28

    Mu (mu) rhythms are EEG oscillations between 8-13 Hz distinguished from alpha by having more anterior distribution and being desynchronized by motor rather than visual activity. Evidence accumulating during the last decade suggests that the desynchronization of mu rhythms (mu suppression) might be also a manifestation of a human Mirror Neuron System (MNS). To further explore this hypothesis we used a paradigm that, in a previous fMRI study, successfully activated this putative MNS in humans. Our direct goal was to provide further support for a link between modulation of mu rhythms and the MNS, by finding parallels between the reported patterns of fMRI activations and patterns of mu suppression. The EEG power in the mu range has been recorded while participants passively observed either a left or a right hand, reaching to and grasping objects, and compared it with that recorded while participants observed the movement of a ball, and while observing static grasping scenes or still objects. Mirroring fMRI results (Shmuelof, L., Zohary, E., 2005. Dissociation between ventral and dorsal fMRI activation during object and action recognition. Neuron 47, 457-470), mu suppression was larger in the hemisphere contra-lateral to the moving hand and larger when the hands grasped different objects in different ways than when the movement was repetitive. No suppression was found while participants observed still objects but mu suppression was also found while seeing static grasping postures. These data are discussed in light of similar parallels between modulations of alpha waves and fMRI while recording EEG in the magnet. The present data support a link between mu suppression and a human MNS.

  7. Automatic classification of sleep stages based on the time-frequency image of EEG signals.

    Science.gov (United States)

    Bajaj, Varun; Pachori, Ram Bilas

    2013-12-01

    In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.

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

    DEFF Research Database (Denmark)

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

    1996-01-01

    Methodology for assessment of depth of anaesthesia based on analysis of the electroencephalogram (EEG) is controversial. Techniques range from display of single measures, for example median value of the frequency spectrum, to dedicated pattern recognition systems based on measures of several EEG......) and (4) a depth of anaesthesia monitor based on EEG pattern recognition (ADAM). Dose-response curves are presented for stepwise increases in stable end-tidal concentrations of each agent. Results indicated considerable inter-patient variability and the limitations of single EEG measures, particularly...... with deeper anaesthesia producing a burst suppression pattern in the EEG. Pattern recognition techniques reduced these difficulties and appeared to be promising over a wide range of anaesthetic levels....

  9. Using S-transform in EEG analysis for measuring an alert versus mental fatigue state.

    Science.gov (United States)

    Tran, Yvonne; Thuraisingham, Ranjit; Wijesuriya, Nirupama; Craig, Ashley; Nguyen, Hung

    2014-01-01

    This paper presents research that investigated the effects of mental fatigue on brain activity using electroencephalogram (EEG) signals. Since EEG signals are considered to be non-stationary, time-frequency analysis has frequently been used for analysis. The S-transform is a time-frequency analysis method and is used in this paper to analyze EEG signals during alert and fatigue states during a driving simulator task. Repeated-measure MANOVA results show significant differences between alert and fatigue states within the alpha (8-13Hz) frequency band. The two sites demonstrating the greatest increases in alpha activity during fatigue were the Cz and P4 sites. The results show that S-transform analysis can be used to distinguish between alert and fatigue states in the EEG and also supports the use of the S-transform for EEG analysis.

  10. Comparison of EEG and MEG in source localization of induced human gamma-band oscillations during visual stimulus.

    Science.gov (United States)

    Mideksa, K G; Hoogenboom, N; Hellriegel, H; Krause, H; Schnitzler, A; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

    2015-08-01

    High frequency gamma oscillations are indications of information processing in cortical neuronal networks. Recently, non-invasive detection of these oscillations have become one of the main research areas in magnetoencephalography (MEG) and electroencephalography (EEG) studies. The aim of this study, which is a continuation of our previous MEG study, is to compare the capability of the two modalities (EEG and MEG) in localizing the source of the induced gamma activity due to a visual stimulus, using a spatial filtering technique known as dynamic imaging of coherent sources (DICS). To do this, the brain activity was recorded using simultaneous MEG and EEG measurement and the data were analyzed with respect to time, frequency, and location of the strongest response. The spherical head modeling technique, such as, the three-shell concentric spheres and an overlapping sphere (local sphere) have been used as a forward model to calculate the external electromagnetic potentials and fields recorded by the EEG and MEG, respectively. Our results from the time-frequency analysis, at the sensor level, revealed that the parieto-occipital electrodes and sensors from both modalities showed a clear and sustained gamma-band activity throughout the post-stimulus duration and that both modalities showed similar strongest gamma-band peaks. It was difficult to interpret the spatial pattern of the gamma-band oscillatory response on the scalp, at the sensor level, for both modalities. However, the source analysis result revealed that MEG3 sensor type, which measure the derivative along the longitude, showed the source more focally and close to the visual cortex (cuneus) as compared to that of the EEG.

  11. Discriminative Ocular Artifact Correction for Feature Learning in EEG Analysis.

    Science.gov (United States)

    Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

    2016-11-16

    Electrooculogram (EOG) artifact contamination is a common critical issue in general electroencephalogram (EEG) studies as well as in brain computer interface (BCI) research. It is especially challenging when dedicated EOG channels are unavailable or when there are very few EEG channels available for ICA-based ocular artifact removal. It is even more challenging to avoid loss of the signal of interest during the artifact correction process, where the signal of interest can be multiple magnitudes weaker than the artifact. To address these issues, we propose a novel discriminative ocular artifact correction approach for feature learning in EEG analysis.Without extra ocular movement measurements, the artifact is extracted from raw EEG data, which is totally automatic and requires no visual inspection of artifacts. Then, artifact correction is optimized jointly with feature extraction by maximizing oscillatory correlations between trials from the same class and minimizing them between trials from different classes. We evaluate this approach on a real world EEG data set comprising 68 subjects performing cognitive tasks. The results showed that the approach is capable of not only suppressing the artifact components but also improving the discriminative power of a classifier with statistical significance. We also demonstrate that the proposed method addresses the confounding issues induced by ocular movements in cognitive EEG study.

  12. An EEG-Based Fatigue Detection and Mitigation System.

    Science.gov (United States)

    Huang, Kuan-Chih; Huang, Teng-Yi; Chuang, Chun-Hsiang; King, Jung-Tai; Wang, Yu-Kai; Lin, Chin-Teng; Jung, Tzyy-Ping

    2016-06-01

    Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual's internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant's EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject's cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

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

    Science.gov (United States)

    Akrami, Amin; Nazeri, Sina

    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. PMID:27699169

  14. Adaptive extraction of emotion-related EEG segments using multidimensional directed information in time-frequency domain.

    Science.gov (United States)

    Petrantonakis, Panagiotis C; Hadjileontiadis, Leontios J

    2010-01-01

    Emotion discrimination from electroencephalogram (EEG) has gained attention the last decade as a user-friendly and effective approach to EEG-based emotion recognition (EEG-ER) systems. Nevertheless, challenging issues regarding the emotion elicitation procedure, especially its effectiveness, raise. In this work, a novel method, which not only evaluates the degree of emotion elicitation but localizes the emotion information in the time-frequency domain, as well, is proposed. The latter, incorporates multidimensional directed information at the time-frequency EEG representation, extracted using empirical mode decomposition, and introduces an asymmetry index for adaptive emotion-related EEG segment selection. Experimental results derived from 16 subjects visually stimulated with pictures from the valence/arousal space drawn from the International Affective Picture System database, justify the effectiveness of the proposed approach and its potential contribution to the enhancement of EEG-ER systems.

  15. Effects of Vitamin E on seizure frequency, electroencephalogram findings, and oxidative stress status of refractory epileptic patients.

    Science.gov (United States)

    Mehvari, Jafar; Motlagh, Fataneh Gholami; Najafi, Mohamad; Ghazvini, Mohammad Reza Aghaye; Naeini, Amirmansour Alavi; Zare, Mohamad

    2016-01-01

    Oxidative stress has been a frequent finding in epileptic patients receiving antiepileptic drugs (AEDs). In this study, the influence of Vitamin E on the antiseizure activity and redox state of patients treated with carbamazepine, sodium valproate, and levetiracetam has been investigated. This double-blind, placebo-controlled trial was carried out on 65 epileptic patients with chronic antiepileptic intake. The subjects received 400 IU/day of Vitamin E or placebo for 6 months. Seizure frequency, electroencephalogram (EEG), and redox state markers were measured monthly through the study. Total antioxidant capacity, catalase and glutathione were significantly higher in Vitamin E received group compared with controls (P < 0.05) whereas malodialdehyde levels did not differ between two groups (P < 0.07). Vitamin E administration also caused a significant decrease in the frequency of seizures (P < 0.001) and improved EEG findings (P = 0.001). Of 32 patients in case group, the positive EEG decreased in 16 patients (50%) whereas among 33 patients in control group only 4 patients (12.1%) showed decreased positive EEG. The results of this preliminary study indicate that coadministration of antioxidant Vitamin E with AEDs improves seizure control and reduces oxidative stress.

  16. [Analysis of Electroencephalogram Sample Entropy Measurement in Frontal Association Cortex Based on Heroin-induced Conditioned Place Preference in Rats].

    Science.gov (United States)

    Huang, Lei; Pan, Qunwan; Zhu, Zaiman; Li, Jing; Gao, Chunfang; Li, Tian; Xu, Xiaoyan

    2015-04-01

    To explore the relationship between the drug-seeking behavior, motivation of conditioned place preference (CPP) rats and the frontal association cortex (FrA) electroencephalogram (EEG) sample entropy, we in this paper present our studies on the FrA EEG sample entropy of control group rats and CPP group rats, respectively. We invested different behavior in four situations of the rat activities, i. e. rats were staying in black chamber of videoed boxes, those staying in white chamber of videoed boxes, those shuttling between black-white chambers and those shuttling between white-black chambers. The experimental results showed that, compared with the control group rats, the FrA EEG sample entropy of CPP rats staying in black chamber of video box and shuttling between white-black chambers had no significant difference. However, sample entropy is significantly smaller (P heroin-induced group rats stayed in white chamber of video box and shuttled between black-white chambers. Consequently, the drug-seeking behavior and motivation of CPP rats correlated closely with the EEG sample entropy changes.

  17. Inter-hemispheric oscillations in human sleep.

    Directory of Open Access Journals (Sweden)

    Lukas L Imbach

    Full Text Available Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep.

  18. Inter-hemispheric oscillations in human sleep.

    Science.gov (United States)

    Imbach, Lukas L; Werth, Esther; Kallweit, Ulf; Sarnthein, Johannes; Scammell, Thomas E; Baumann, Christian R

    2012-01-01

    Sleep is generally categorized into discrete stages based on characteristic electroencephalogram (EEG) patterns. This traditional approach represents sleep architecture in a static way, but it cannot reflect variations in sleep across time and across the cortex. To investigate these dynamic aspects of sleep, we analyzed sleep recordings in 14 healthy volunteers with a novel, frequency-based EEG analysis. This approach enabled comparison of sleep patterns with low inter-individual variability. We then implemented a new probability dependent, automatic classification of sleep states that agreed closely with conventional manual scoring during consolidated sleep. Furthermore, this analysis revealed a previously unrecognized, interhemispheric oscillation during rapid eye movement (REM) sleep. This quantitative approach provides a new way of examining the dynamic aspects of sleep, shedding new light on the physiology of human sleep.

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

    Science.gov (United States)

    Li, Yingli; O'Boyle, Michael

    2013-01-01

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

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

    NARCIS (Netherlands)

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

    2014-01-01

    It has long been postulated that exogenous cannabinoids have a profound effect on human cognitive functioning. These cannabinoid effects are thought to depend, at least in parts, on alterations of phase-locking of local field potential neuronal firing. The latter can be measured as activity in the t

  1. Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals

    Science.gov (United States)

    Prabhakar, Sunil Kumar; Rajaguru, Harikumar

    2015-12-01

    The most common and frequently occurring neurological disorder is epilepsy and the main method useful for the diagnosis of epilepsy is electroencephalogram (EEG) signal analysis. Due to the length of EEG recordings, EEG signal analysis method is quite time-consuming when it is processed manually by an expert. This paper proposes the application of Linear Graph Embedding (LGE) concept as a dimensionality reduction technique for processing the epileptic encephalographic signals and then it is classified using Sparse Representation Classifiers (SRC). SRC is used to analyze the classification of epilepsy risk levels from EEG signals and the parameters such as Sensitivity, Specificity, Time Delay, Quality Value, Performance Index and Accuracy are analyzed.

  2. Wearable ear EEG for brain interfacing

    Science.gov (United States)

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

    2017-02-01

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

  3. Evaluation of mental workload and familiarity in human computer interaction with integrated development environments using single-channel EEG

    OpenAIRE

    2015-01-01

    With modern developments in sensing technology it has become possible to detect and classify brain activity into distinct states such as attention and relaxation using commercially avail- able EEG devices. These devices provide a low-cost and minimally intrusive method to observe a subject’s cognitive load whilst interacting with a computer system, thus providing a basis for deter- mining the overall effectiveness of the design of a computer interface. In this paper, a single-channel dry sens...

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

    Directory of Open Access Journals (Sweden)

    I. A. Sadekov

    2017-01-01

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

  5. Early detection of hand movements from electroencephalograms for stroke therapy applications

    Science.gov (United States)

    Muralidharan, A.; Chae, J.; Taylor, D. M.

    2011-08-01

    Movement-assist devices such as neuromuscular stimulation systems can be used to generate movements in people with chronic hand paralysis due to stroke. If detectable, motor planning activity in the cortex could be used in real time to trigger a movement-assist device and restore a person's ability to perform many of the activities of daily living. Additionally, re-coupling motor planning in the cortex with assisted movement generation in the periphery may provide an even greater benefit—strengthening relevant synaptic connections over time to promote natural motor recovery. This study examined the potential for using electroencephalograms (EEGs) as a means of rapidly detecting the intent to open the hand during movement planning in individuals with moderate chronic hand paralysis following a subcortical ischemic stroke. On average, attempts to open the hand could be detected from EEGs approximately 100-500 ms prior to the first signs of movement onset. This earlier detection would minimize device activation delays and allow for tighter coupling between initial formation of the motor plan in the cortex and augmentation of that plan in the periphery by a movement-assist device. This tight temporal coupling may be important or even essential for strengthening synaptic connections and enhancing natural motor recovery.

  6. TMS and TMS-EEG techniques in the study of the excitability, connectivity, and plasticity of the human motor cortex.

    Science.gov (United States)

    Ferreri, Florinda; Rossini, Paolo Maria

    2013-01-01

    Increasing evidence supports the notion that brain plasticity involves distinct functional and structural components, each entailing a number of cellular mechanisms operating at different time scales, synaptic loci, and developmental phases within an extremely complex framework. However, the exact relationship between functional and structural components of brain plasticity/connectivity phenomena is still unclear and its explanation is a major challenge within modern neuroscience. Transcranial magnetic stimulation (TMS), with or without electroencephalography (EEG), is a sensitive and objective measure of the effect of different kinds of noninvasive manipulation of the brain's activity, particularly of the motor cortex. Moreover, the key feature of TMS and TMS-EEG coregistration is their crucial role in tracking temporal dynamics and inner hierarchies of brain functional and effective connectivities, possibly clarifying some essential issues underlying brain plasticity. All together, the findings presented here are significant for the adoption of the TMS and TMS-EEG coregistration techniques as a tool for basic neurophysiologic research and, in the future, even for clinical diagnostics purposes.

  7. Effects of the Selective α5-GABAAR Antagonist S44819 on Excitability in the Human Brain: A TMS-EMG and TMS-EEG Phase I Study.

    Science.gov (United States)

    Darmani, Ghazaleh; Zipser, Carl M; Böhmer, Gabriele M; Deschet, Karine; Müller-Dahlhaus, Florian; Belardinelli, Paolo; Schwab, Matthias; Ziemann, Ulf

    2016-12-07

    Alpha-5 gamma-aminobutyric acid type A receptors (α5-GABAARs) are located extrasynaptically, regulate neuronal excitability through tonic inhibition, and are fundamentally important for processes such as plasticity and learning. For example, pharmacological blockade of α5-GABAAR in mice with ischemic stroke improved recovery of function by normalizing exaggerated perilesional α5-GABAAR-dependent tonic inhibition. S44819 is a novel competitive selective antagonist of the α5-GABAAR at the GABA-binding site. Pharmacological modulation of α5-GABAAR-mediated tonic inhibition has never been investigated in the human brain. Here, we used transcranial magnetic stimulation (TMS) to test the effects of a single oral dose of 50 and 100 mg of S44819 on electromyographic (EMG) and electroencephalographic (EEG) measures of cortical excitability in 18 healthy young adults in a randomized, double-blinded, placebo-controlled, crossover phase I study. A dose of 100 mg, but not 50 mg, of S44819 decreased active motor threshold, the intensity needed to produce a motor evoked potential of 0.5 mV, and the amplitude of the N45, a GABAAergic component of the TMS-evoked EEG response. The peak serum concentration of 100 mg S44819 correlated directly with the decrease in N45 amplitude. Short-interval intracortical inhibition, a TMS-EMG measure of synaptic GABAAergic inhibition, and other components of the TMS-evoked EEG response remained unaffected. These findings provide first time evidence that the specific α5-GABAAR antagonist S44819 reached human cortex to impose an increase in cortical excitability. These data warrant further development of S44819 in a human clinical trial to test its efficacy in enhancing recovery of function after ischemic stroke. The extrasynaptic α-5 gamma-aminobutyric acid type A receptor (α5-GABAAR) regulates neuronal excitability through tonic inhibition in the mammalian brain. Tonic inhibition is important for many fundamental processes such as

  8. Outcome of EEGs Ordered at a Regional Children’s Mental Health Service

    Science.gov (United States)

    Swart, Greta Toni; Wahab, Aryan

    2010-01-01

    Introduction: Clinical practice guidelines in child psychiatry recommend doing an EEG when warranted based upon a complete history and physical examination. The College of Physicians and Surgeons of Ontario published guidelines as to when an EEG is likely to provide useful information. Method: All the electroencephalograms ordered at a tertiary care children’s mental health centre over about a 2 year period were reviewed and compared to the guidelines published by the Ontario College of Physicians and Surgeons for ordering EEGs. The outcome of the EEGs and what the ordering physician did after receiving the results were also reviewed. Results: About 53% were ordered for reasons that the guidelines indicated would result in a significant probability of obtaining clinically useful information. EEG abnormalities were identified in 49% of the youth in this category. About 20% were ordered for reasons the guidelines indicated that an EEG was not likely to provide clinically useful information. EEG abnormalities were identified in 24% of the youth in this category. About 27% of EEGs were ordered for reasons not mentioned in the guidelines. EEG abnormalities were identified in 52% of those youth. Youth who had abnormal results were generally followed up with further investigations. Those youth with more severe abnormalities were often referred to a pediatric neurologist for assessment and treatment. Conclusions: Children with severe mental health problems have an increased probability of having neurological problems which might have an impact on the ability to assess and treat the mental health problem. PMID:20467542

  9. A pilot study of continuous limited-channel aEEG in term infants with encephalopathy.

    Science.gov (United States)

    Lawrence, Russell; Mathur, Amit; Nguyen The Tich, Sylvie; Zempel, John; Inder, Terrie

    2009-06-01

    To evaluate the accuracy, feasibility, and impact of limited-channel amplitude integrated electroencephalogram (aEEG) monitoring in encephalopathic infants. Encephalopathic infants were placed on limited-channel aEEG with a software-based seizure event detector for 72 hours. A 12-hour epoch of conventional EEG-video (cEEG) was simultaneously collected. Infants were randomly assigned to monitoring that was blinded or visible to the clinical team. If a seizure detection event occurred in the visible group, the clinical team interpreted whether the event was a seizure, based on review of the limited-channel aEEG. EEG data were reviewed independently offline. In more than 68 hours per infant of limited-channel aEEG monitoring, 1116 seizures occurred (>90% clinically silent), with 615 detected by the seizure event detector (55%). Detection improved with increasing duration of seizures (73% >30 seconds, 87% >60 seconds). Bedside physicians were able to accurately use this algorithm to differentiate true seizures from false-positives. The visible group had a 52% reduction in seizure burden (P = .114) compared with the blinded group. Monitoring for seizures with limited-channel aEEG can be accurately interpreted, compares favorably with cEEG, and is associated with a trend toward reduced seizure burden.

  10. Localization of brain activities using multiway analysis of EEG tensor via EMD and reassigned TF representation.

    Science.gov (United States)

    Pouryazdian, Saeed; Beheshti, Soosan; Krishnan, Sridhar

    2015-01-01

    Electroencephalogram (EEG) is widely used for monitoring, diagnosis purposes and also for study of brain's physiological, mental and functional abnormalities. Processing of information by the brain is reflected in dynamical changes of the electrical activity in time, frequency, and space. EEG signal processing tends to describe and quantify these variations in such a way that they are localized in temporal, spectral and spatial domain. Here we use multi-way (Tensor) analysis for localizing the EEG events. We used EMD process for decomposing EEG into distinct oscillatory modes, which are then mapped to TF plane using the near optimal Reassigned Spectrogram. Temporal, Spatial and Spectral information of the Multichannel EEG are then used to generate a three-way Frequency-Time-Space EEG tensor. Exploiting EMD also enables us to detrend the EEG recordings. Simulation results on both synthetic and real EEG data show that tensor analysis greatly improve separation and localization of overlapping events in EEG and it could be effectively exploited for detecting and characterizing the evoked potentials.

  11. 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.

  12. An IFS-based similarity measure to index electroencephalograms

    NARCIS (Netherlands)

    Berrada, Ghita; Zhexue Huang, Joshua; de Keijzer, Ander; Cao, Longbing; Srivastava, Jaideep

    EEG is a very useful neurological diagnosis tool, inasmuch as the EEG exam is easy to perform and relatively cheap. However, it generates large amounts of data, not easily interpreted by a clinician. Several methods have been tried to automate the interpretation of EEG recordings. However, their

  13. Changes in cerebral hemodynamics during a sleep-deprived video-electroencephalogram in healthy children.

    Science.gov (United States)

    Peng, Bingwei; Li, Jialing; Wang, Jing; Liang, Xiuqiong; Zheng, Zhiying; Mai, Jianning

    2016-07-01

    This study investigates the cerebral hemodynamic changes during a routine sleep-deprived video-electroencephalogram (SD-VEEG) in healthy children. Forty-two children with normal intelligence were examined. The children were 5-14 years of age, and their electroencephalograms (EEGs) were within the normal range. Each subject was deprived of a routine night's sleep and then examined during non-drug-induced sleep in the daytime. The awake and sleep stages were evaluated using EEGs, according to the American Academy of Sleep Medicine. Stable transcranial Doppler ultrasound (TCD) tracings through real-time TCD-VEEG monitoring were recorded. The mean systolic cerebral blood flow velocity (CBFV), diastolic CBFV, pulsatility index and resistance index of each artery were analyzed for 30 s per stage. A multivariate analysis of variance was conducted to compare the hemodynamic parameters for the awake stage versus light sleep and deep sleep stages. Non-rapid eye movement sleep was associated with an increased CBFV in the middle (164.38  ±  27.28) and anterior cerebral artery (131.81  ±  21.55) during light sleep (stages N1 and N2) (P  =  0.0001), a reduced systolic CBFV in all vascular arteries (LMCA, 138.73  ±  20.64; LACA, 108.33  ±  22.33; LPCA, 83.9  ±  18.6) during deep sleep (stage N3) compared with light sleep (P  =  0.0001), and a sustained increased PI (LMCA, 0.92  ±  0.13; LACA, 0.964  ±  0.18) during deep sleep (P  <  0.05). These findings indicate distinct cerebral hemodynamic alterations during SD-VEEG in children. This study utilized real-time TCD-VEEG monitoring during SD-EEG to further investigate neurovascular coupling in interictal epileptic discharges and understand its potential influence on cognition in the developing brain.

  14. 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.

  15. 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...

  16. 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...

  17. EEG frequency tagging using ultra-slow periodic heat stimulation of the skin reveals cortical activity specifically related to C fiber thermonociceptors

    Science.gov (United States)

    Colon, Elisabeth; Liberati, Giulia; Mouraux, André

    2017-01-01

    The recording of event-related brain potentials triggered by a transient heat stimulus is used extensively to study nociception and diagnose lesions or dysfunctions of the nociceptive system in humans. However, these responses are related exclusively to the activation of a specific subclass of nociceptive afferents: quickly-adapting thermonociceptors. In fact, except if the activation of Aδ fibers is avoided or if A fibers are blocked, these responses specifically reflect activity triggered by the activation of Type 2 quickly-adapting A fiber mechano-heat nociceptors (AMH-2). Here, we propose a novel method to isolate, in the human electroencephalogram (EEG), cortical activity related to the sustained periodic activation of heat-sensitive thermonociceptors, using very slow (0.2 Hz) and long-lasting (75 s) sinusoidal heat stimulation of the skin between baseline and 50°C. In a first experiment, we show that when such long-lasting thermal stimuli are applied to the hand dorsum of healthy volunteers, the slow rises and decreases of skin temperature elicit a consistent periodic EEG response at 0.2 Hz and its harmonics, as well as a periodic modulation of the magnitude of theta, alpha and beta band EEG oscillations. In a second experiment, we demonstrate using an A fiber block that these EEG responses are predominantly conveyed by unmyelinated C fiber nociceptors. The proposed approach constitutes a novel mean to study C fiber function in humans, and to explore the cortical processing of tonic heat pain in physiological and pathological conditions. PMID:27871921

  18. Online detection of fetal acidemia during labour by testing synchronization of EEG and heart rate: a prospective study in fetal sheep.

    Science.gov (United States)

    Wang, Xiaogang; Durosier, L Daniel; Ross, Michael G; Richardson, Bryan S; Frasch, Martin G

    2014-01-01

    Severe fetal acidemia during labour can result in life-lasting neurological deficits, but the timely detection of this condition is often not possible. This is because the positive predictive value (PPV) of fetal heart rate (FHR) monitoring, the mainstay of fetal health surveillance during labour, to detect concerning fetal acidemia is around 50%. In fetal sheep model of human labour, we reported that severe fetal acidemia (pHsynchronization of electroencephalogram (EEG) and FHR. However, EEG and FHR are cyclic and noisy, and although the synchronization might be visually evident, it is challenging to detect automatically, a necessary condition for bedside utility. Here we present and validate a novel non-parametric statistical method to detect fetal acidemia during labour by using EEG and FHR. The underlying algorithm handles non-stationary and noisy data by recording number of abnormal episodes in both EEG and FHR. A logistic regression is then deployed to test whether these episodes are significantly related to each other. We then apply the method in a prospective study of human labour using fetal sheep model (n = 20). Our results render a PPV of 68% for detecting impending severe fetal acidemia ∼60 min prior to pH drop to less than 7.00 with 100% negative predictive value. We conclude that this method has a great potential to improve PPV for detection of fetal acidemia when it is implemented at the bedside. We outline directions for further refinement of the algorithm that will be achieved by analyzing larger data sets acquired in prospective human pilot studies.

  19. Online detection of fetal acidemia during labour by testing synchronization of EEG and heart rate: a prospective study in fetal sheep.

    Directory of Open Access Journals (Sweden)

    Xiaogang Wang

    Full Text Available Severe fetal acidemia during labour can result in life-lasting neurological deficits, but the timely detection of this condition is often not possible. This is because the positive predictive value (PPV of fetal heart rate (FHR monitoring, the mainstay of fetal health surveillance during labour, to detect concerning fetal acidemia is around 50%. In fetal sheep model of human labour, we reported that severe fetal acidemia (pH<7.00 during repetitive umbilical cord occlusions (UCOs is preceded ∼60 minutes by the synchronization of electroencephalogram (EEG and FHR. However, EEG and FHR are cyclic and noisy, and although the synchronization might be visually evident, it is challenging to detect automatically, a necessary condition for bedside utility. Here we present and validate a novel non-parametric statistical method to detect fetal acidemia during labour by using EEG and FHR. The underlying algorithm handles non-stationary and noisy data by recording number of abnormal episodes in both EEG and FHR. A logistic regression is then deployed to test whether these episodes are significantly related to each other. We then apply the method in a prospective study of human labour using fetal sheep model (n = 20. Our results render a PPV of 68% for detecting impending severe fetal acidemia ∼60 min prior to pH drop to less than 7.00 with 100% negative predictive value. We conclude that this method has a great potential to improve PPV for detection of fetal acidemia when it is implemented at the bedside. We outline directions for further refinement of the algorithm that will be achieved by analyzing larger data sets acquired in prospective human pilot studies.

  20. Mobile EEG in epilepsy

    NARCIS (Netherlands)

    Askamp, Jessica; Putten, van M.J.A.M.

    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 t

  1. 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

  2. Higuchi fractal properties of onset epilepsy electroencephalogram.

    Science.gov (United States)

    Khoa, Truong Quang Dang; Ha, Vo Quang; Toi, Vo Van

    2012-01-01

    Epilepsy is a medical term which indicates a common neurological disorder characterized by seizures, because of abnormal neuronal activity. This leads to unconsciousness or even a convulsion. The possible etiologies should be evaluated and treated. Therefore, it is necessary to concentrate not only on finding out efficient treatment methods, but also on developing algorithm to support diagnosis. Currently, there are a number of algorithms, especially nonlinear algorithms. However, those algorithms have some difficulties one of which is the impact of noise on the results. In this paper, in addition to the use of fractal dimension as a principal tool to diagnose epilepsy, the combination between ICA algorithm and averaging filter at the preprocessing step leads to some positive results. The combination which improved the fractal algorithm become robust with noise on EEG signals. As a result, we can see clearly fractal properties in preictal and ictal period so as to epileptic diagnosis.

  3. Higuchi Fractal Properties of Onset Epilepsy Electroencephalogram

    Directory of Open Access Journals (Sweden)

    Truong Quang Dang Khoa

    2012-01-01

    Full Text Available Epilepsy is a medical term which indicates a common neurological disorder characterized by seizures, because of abnormal neuronal activity. This leads to unconsciousness or even a convulsion. The possible etiologies should be evaluated and treated. Therefore, it is necessary to concentrate not only on finding out efficient treatment methods, but also on developing algorithm to support diagnosis. Currently, there are a number of algorithms, especially nonlinear algorithms. However, those algorithms have some difficulties one of which is the impact of noise on the results. In this paper, in addition to the use of fractal dimension as a principal tool to diagnose epilepsy, the combination between ICA algorithm and averaging filter at the preprocessing step leads to some positive results. The combination which improved the fractal algorithm become robust with noise on EEG signals. As a result, we can see clearly fractal properties in preictal and ictal period so as to epileptic diagnosis.

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

    Directory of Open Access Journals (Sweden)

    Wei-bi CHEN

    2015-12-01

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

  5. STUDY OF INTERICTAL E.E.G IN EPILEPSY

    Directory of Open Access Journals (Sweden)

    Usha Rani

    2013-04-01

    Full Text Available ABSTRACT: The present study includes hundred consecutive patie nts with a clinical diagnosis of Epilepsy attending the department of Neurology. All these patients were evaluated clinically and data recorded in proforma. Electroencephalogram ( E.E.G was recorded with 16-channel Electroencephalograph and the standards of normality are established. The Aim of this study is to study the pattern of abnormalities in interictal EEG in Epilepsy. The Objectives of this study are 1. To confirm the diagnosis of epilepsy. 2. To s tudy the EEG abnormalities in relation to different types of epilepsy. 3. To know the distributi on of epilepsy in relation to age and sex. 4. To study the effect of antiepileptic drugs on EEG. 5 . To study the effect of hyperventilation on EEG. 6. To study the effect of photic stimulation on EEG. 7. To study the effect of interval between last seizure and EEG recording on EEG abnor malities. Epilepsy is a very common neurological illness accounting for 10% of patients attending the Neurology Outpatient department. It most commonly affects people in the fir st 3 decades of life. Males seem to be slightly more frequently affected. The diagnosis of epilepsy is most often on clinical grounds; EEG is helpful in supporting the diagnosis of epilepsy in difficult situation and classification of the seizures. Single awake interictal recording is helpful only in approximately 40% of the patients. Provocative procedures like hyperventilation and photic stimulation increase the diagnostic yield, particularly primarily generalized epilepsies. Sleep records to be more informative should be recorded in light sleep. Gener alized seizures either primary or secondary seem to be the commonest type. In patients with a cl inical diagnosis of generalized seizures, EEG may demonstrate focal abnormalities revealing t he true nature of the seizure. Partial seizures may not reveal focal onset in the EEG maki ng classification of the seizures interacts (on

  6. EEG biofeedback improves attentional bias in high trait anxiety individuals.

    Science.gov (United States)

    Wang, Sheng; Zhao, Yan; Chen, Sijuan; Lin, Guiping; Sun, Peng; Wang, Tinghuai

    2013-10-07

    Emotion-related attentional bias is implicated in the aetiology and maintenance of anxiety disorders. Electroencephalogram (EEG) biofeedback can obviously improve the anxiety disorders and reduce stress level, and can also enhance attention performance in healthy subjects. The present study examined the effects and mechanisms of EEG biofeedback training on the attentional bias of high trait anxiety (HTA) individuals toward negative stimuli. Event-related potentials were recorded while HTA (n=24) and nonanxious (n=21) individuals performed the color-word emotional Stroop task. During the emotional Stroop task, HTA participants showed longer reaction times and P300 latencies induced by negative words, compared to nonanxious participants.The EEG biofeedback significantly decreased the trait anxiety inventory score and reaction time in naming the color of negative words in the HTA group. P300 latencies evoked by negative stimuli in the EEG biofeedback group were significantly reduced after the alpha training, while no significant changes were observed in the sham biofeedback group after the intervention. The prolonged P300 latency is associated with attentional bias to negative stimuli in the HTA group. EEG biofeedback training demonstrated a significant improvement of negative emotional attentional bias in HTA individuals, which may be due to the normalization of P300 latency.

  7. RSE prediction by EEG patterns in adult GCSE patients.

    Science.gov (United States)

    Tian, Fei; Su, Yingying; Chen, Weibi; Gao, Ran; Zhang, Yunzhou; Zhang, Yan; Ye, Hong; Gao, Daiquan

    2013-07-01

    Electroencephalogram (EEG) can predict mortality in status epilepticus (SE) patients. However, we consider that the prediction for refractory status epilepticus (RSE) after SE initial treatment is more significant than long-term prognosis of SE. The objective of this study is to detect some predictive EEG patterns for RSE. Pooled data derived from two randomized controlled trials (RCTs) were prospectively analyzed in adult generalized convulsive status epilepticus (GCSE) patients. Etiology, GCSE duration and EEG patterns are three factors which were statistically different between non-RSE and RSE groups. However, when we introduced these factors into multivariable logistic regression model, only EEG pattern was an independent risk factor for RSE prediction. Comparing with rhythmic fast activities background (RFAB) pattern, there were positive correlations between interictal epileptiform discharges (IEDs), periodic epileptic discharges/subtle status epilepticus (PEDs/subtle SE) patterns and RSE incidence respectively. There was an increased risk of RSE incidence accompanied with IEDs and PEDs/subtle SE patterns appearance. Clinicians should adjust anti-epileptic strategies with the aid of these EEG patterns in order to reduce RSE incidence. Copyright © 2013. Published by Elsevier B.V.

  8. EEG-based lapse detection with high temporal resolution.

    Science.gov (United States)

    Davidson, Paul R; Jones, Richard D; Peiris, Malik T R

    2007-05-01

    A warning system capable of reliably detecting lapses in responsiveness (lapses) has the potential to prevent many fatal accidents. We have developed a system capable of detecting lapses in real-time with second-scale temporal resolution. Data was from 15 subjects performing a visuomotor tracking task for two 1-hour sessions with concurrent electroencephalogram (EEG) and facial video recordings. The detector uses a neural network with normalized EEG log-power spectrum inputs from two bipolar EEG derivations, though we also considered a multichannel detector. Lapses, identified using a combination of video rating and tracking behavior, were used to train our detector. We compared detectors employing tapped delay-line linear perceptron, tapped delay-line multilayer perceptron (TDL-MLP), and long short-term memory (LSTM) recurrent neural networks operating continuously at 1 Hz. Using estimates of EEG log-power spectra from up to 4 s prior to a lapse improved detection compared with only using the most recent estimate. We report the first application of a LSTM to an EEG analysis problem. LSTM performance was equivalent to the best TDL-MLP network but did not require an input buffer. Overall performance was satisfactory with area under the curve from receiver operating characteristic analysis of 0.84 +/- 0.02 (mean +/- SE) and area under the precision-recall curve of 0.41 +/- 0.08.

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

    Directory of Open Access Journals (Sweden)

    Kevin eNathan

    2016-01-01

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

  10. Interpreting EEG alpha activity.

    Science.gov (United States)

    Bazanova, O M; Vernon, D

    2014-07-01

    Exploring EEG alpha oscillations has generated considerable interest, in particular with regards to the role they play in cognitive, psychomotor, psycho-emotional and physiological aspects of human life. However, there is no clearly agreed upon definition of what constitutes 'alpha activity' or which of the many indices should be used to characterize it. To address these issues this review attempts to delineate EEG alpha-activity, its physical, molecular and morphological nature, and examine the following indices: (1) the individual alpha peak frequency; (2) activation magnitude, as measured by alpha amplitude suppression across the individual alpha bandwidth in response to eyes opening, and (3) alpha "auto-rhythmicity" indices: which include intra-spindle amplitude variability, spindle length and steepness. Throughout, the article offers a number of suggestions regarding the mechanism(s) of alpha activity related to inter and intra-individual variability. In addition, it provides some insights into the various psychophysiological indices of alpha activity and highlights their role in optimal functioning and behavior.

  11. Wearable EEG headband using printed electrodes and powered by energy harvesting for emotion monitoring in ambient assisted living

    Science.gov (United States)

    Matiko, Joseph W.; Wei, Yang; Torah, Russel; Grabham, Neil; Paul, Gordon; Beeby, Stephen; Tudor, John

    2015-12-01

    Globally, human life expectancy is steadily increasing causing an increase in the elderly population and consequently increased costs of supporting them. Ambient assisted living is an active research area aimed at supporting elderly people to live independently in their preferred living environment. This paper presents the design and testing of a self-powered wearable headband for electroencephalogram (EEG) based detection of emotions allowing the evaluation of the quality of life of assisted people. Printed active electrode fabrication and testing is discussed followed by the design of an energy harvester for powering the headband. The results show that the fabricated electrodes have similar performance to commercial electrodes and that the electronics embedded into the headband, as well as the wireless sensor node used for processing the EEG, can be powered by energy harvested from solar panels integrated on the headband. An average real time emotion classification accuracy of 90 (±9) % was obtained from 12 subjects. The results show that the self-powered wearable headband presented in this paper can be used to measure the wellbeing of assisted people with good accuracy.

  12. Correlation of invasive EEG and scalp EEG.

    Science.gov (United States)

    Ramantani, Georgia; Maillard, Louis; Koessler, Laurent

    2016-10-01

    Ever since the implementation of invasive EEG recordings in the clinical setting, it has been perceived that a considerable proportion of epileptic discharges present at a cortical level are missed by routine scalp EEG recordings. Several in vitro, in vivo, and simulation studies have been performed in the past decades aiming to clarify the interrelations of cortical sources with their scalp and invasive EEG correlates. The amplitude ratio of cortical potentials to their scalp EEG correlates, the extent of the cortical area involved in the discharge, as well as the localization of the cortical source and its geometry have been each independently linked to the recording of the cortical discharge with scalp electrodes. The need to elucidate these interrelations has been particularly imperative in the field of epilepsy surgery with its rapidly growing EEG-based localization technologies. Simultaneous multiscale EEG recordings with scalp, subdural and/or depth electrodes, applied in presurgical epilepsy workup, offer an excellent opportunity to shed some light to this fundamental issue. Whereas past studies have considered predominantly neocortical sources in the context of temporal lobe epilepsy, current investigations have included deep sources, as in mesial temporal epilepsy, as well as extratemporal sources. Novel computational tools may serve to provide surrogates for the shortcomings of EEG recording methodology and facilitate further developments in modern electrophysiology.

  13. EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis

    Directory of Open Access Journals (Sweden)

    Mahmoud I. Kamel

    2012-04-01

    Full Text Available Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT with the Regulated Fisher Linear Discriminant (RFLD classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.

  14. EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface

    Institute of Scientific and Technical Information of China (English)

    WU Ting; YAN Guo-zheng; YANG Bang-hua; SUN Hong

    2007-01-01

    Electroencephalogram (EEG) signal preprocessing is one of the most important techniques in brain computer interface (BCI). The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition. Wavelet transform is a method of multi-resolution time-frequency analysis, it can decompose the mixed signals which consist of different frequencies into different frequency band. EEG signal is analyzed and denoised using wavelet transform. Moreover, wavelet transform can be used for EEG feature extraction. The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features. The eigenvector for classification is obtained by combining the effective features from different channels. The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition, the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.

  15. Comparative evaluation of an ambulatory EEG platform vs. clinical gold standard.

    Science.gov (United States)

    Jackson, Gregory; Radhu, Natasha; Sun, Yinming; Tallevi, Kevin; Ritvo, Paul; Daskalakis, Zafiris J; Grundlehner, Bernard; Penders, Julien; Cafazzo, Joseph A

    2013-01-01

    Electroencephalography (EEG) testing in clinical labs makes use of large amplifiers and complex software for data acquisition. While there are new ambulatory electroencephalogram (EEG) systems, few have been directly compared to a gold standard system. Here, an ultra-low power wireless EEG system designed by Imec is tested against the gold standard Neuroscan SynAmps2 EEG system, recording simultaneously from the same laboratory cap prepared with electrode gel. The data was analyzed using correlation analysis for both time domain and frequency domain data. The analysis indicated a high Pearson's correlation coefficient (mean=0.957, median=0.985) with high confidence (mean P=0.002) for 10-second sets of data transformed to the frequency domain. The time domain results had acceptable Pearson's coefficient (mean=0.580, median =0.706) with high confidence (mean P=0.008).

  16. Quantitative EEG Brain Mapping In Psychotropic Drug Development, Drug Treatment Selection, and Monitoring.

    Science.gov (United States)

    Itil, Turan M.; Itil, Kurt Z.

    1995-05-01

    Quantification of standard electroencephalogram (EEG) by digital computers [computer-analyzed EEG (CEEG)] has transformed the subjective analog EEG into an objective scientific method. Until a few years ago, CEEG was only used to assist in the development of psychotropic drugs by means of the quantitative pharmaco EEG. Thanks to the computer revolution and the accompanying reductions in cost of quantification, CEEG can now also be applied in psychiatric practice. CEEG can assist the physician in confirming clinical diagnoses, selecting psychotropic drugs for treatment, and drug treatment monitoring. Advancements in communications technology allow physicians and researchers to reduce the costs of acquiring a high-technology CEEG brain mapping system by utilizing the more economical telephonic services.

  17. 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.

  18. Hybrid EEG--Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal.

    Science.gov (United States)

    Mannan, Malik M Naeem; Kim, Shinjung; Jeong, Myung Yung; Kamran, M Ahmad

    2016-02-19

    Contamination of eye movement and blink artifacts in Electroencephalogram (EEG) recording makes the analysis of EEG data more difficult and could result in mislead findings. Efficient removal of these artifacts from EEG data is an essential step in improving classification accuracy to develop the brain-computer interface (BCI). In this paper, we proposed an automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system. The performance of the proposed algorithm is illustrated using experimental and standard EEG datasets. The proposed algorithm not only removes the ocular artifacts from artifactual zone but also preserves the neuronal activity related EEG signals in non-artifactual zone. The comparison with the two state-of-the-art techniques namely ADJUST based ICA and REGICA reveals the significant improved performance of the proposed algorithm for removing eye movement and blink artifacts from EEG data. Additionally, results demonstrate that the proposed algorithm can achieve lower relative error and higher mutual information values between corrected EEG and artifact-free EEG data.

  19. Electroencephalogram (EEG) and Magnetoencephalogram (MEG) as Tools for Evaluation of Cognitive Function

    Science.gov (United States)

    Fender, Derek H.; Hestenes, John D.

    1985-01-01

    We have developed computerized analysis and display techniques to help identify the origins of visually evoked scalped potentials (VESP). The potentials are recorded simultaneously from many electrodes (usually 40 to 48) spaced over the region of the scalp where appreciable evoked potentials are found in response to particular stimulus. Contour mapping algorithms are then used to display the time behavior of equipotential surfaces on the scalp during the VESP. We then use an optimization technique to select the parameters of arrays of current dipole sources within the model until the model equipotential field distribution closely fits the measured data. Computer graphics are then used to display, as a movie, the actual and model scalp potential fields and the parameters of the dipole generators within the model head during the course of VESP activity. We have devised reaction time tests that involve potentially separable stages of cognitive processing and utilize stimuli that produce measurable cognition-related features in the late component of the evoked potential. We have used these techniques to determine the loci in the brain where known cognition-related features in the evoked potential are generated and we have explored the extent to which each of these features can be related to the reaction time tasks. We have also examined the temporal-spatial aspects of their cerebral involvement. Our current work is planned to characterize the age-related changes in the processes performed by such sources. We also use a neuromagnetometer to measure the evoked magnetic fields in similar circumstances; we will discuss the relative merits of the two methodologies.

  20. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

    Science.gov (United States)

    Acharya, U Rajendra; Sree, S Vinitha; Chattopadhyay, Subhagata; Yu, Wenwei; Ang, Peng Chuan Alvin

    2011-06-01

    Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

  1. A novel neural network with Non-Recursive IIR Filters on EEG Artifacts Elimination.

    Science.gov (United States)

    Miyazaki, Ryota; Ohshiro, Masakuni; Nishimura, Toshihiro; Tsubai, Masayoshi

    2005-01-01

    The artifacts caused by various factors, EOG (electrooculogram), blink and EMG (electromyogram), in EEG (Electroencephalogram) signals increase the difficulty in analyzing them. In addition, EEG signals containing artifacts often cannot be used in analyzing them. So, it is useful and indispensable to eliminate the artifacts from EEG signals. In this paper, a neural network with non-recursive IIR (Infinite Impulse Response) filters are used to eliminate the artifacts from EEG signals. The proposed method is a new approach that is respect to slotting a non-recursive IIR filter into individual neurons of a neural network. First of all, in order to investigate the usefulness of the proposed method in eliminating the artifacts from EEG signals, we apply it to the artificial EEG signals that are weakly stationary process. As the result, the artifacts can be eliminated from EEG signals almost exactly using the proposed method, and it is suggested the proposed method should be useful in eliminating the artifacts from EEG signals.

  2. Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals.

    Science.gov (United States)

    Tiwari, Ashwani Kumar; Pachori, Ram Bilas; Kanhangad, Vivek; Panigrahi, Bijaya Ketan

    2017-07-01

    The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.

  3. Comparison of different EEG features in estimation of hypnosis susceptibility level.

    Science.gov (United States)

    Baghdadi, Golnaz; Nasrabadi, Ali Motie

    2012-05-01

    Hypnosis has long been known to be associated with heightened control over physical processes and researchers put it under consideration because of its usage as a therapeutic tool in many medical and psychological problems. Determination of hypnosis susceptibility level is important before prescribing any hypnotic treatment. In this study different features are introduced to classify hypnotizability levels. These features were extracted from electroencephalogram (EEG) signals which were recorded from 32 subjects during hypnosis suggestion. Based on the obtained result, a method was suggested to estimate the hypnosis susceptibility level from hypnosis EEG signals instead of using traditional clinical subjective tests.

  4. Prediction of Synchrostate Transitions in EEG Signals Using Markov Chain Models

    CERN Document Server

    Jamal, Wasifa; Oprescu, Ioana-Anastasia; Maharatna, Koushik

    2014-01-01

    This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated for Markov chain modelling from 100 trials of 128-channel EEG signals during two different face perception tasks. Prediction accuracies with such finite Markov chain models for synchrostate transition are also compared, under a data-partitioning based cross-validation scheme.

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

    Directory of Open Access Journals (Sweden)

    Mahdi Moeini

    2014-12-01

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

  6. A Controlled Study of the Effectiveness of EEG Biofeedback Training on Children with Attention Deficit Hyperactivity Disorder

    Institute of Scientific and Technical Information of China (English)

    XIONG Zhonggui; SHI Shuhua; XU Haiqing

    2005-01-01

    Summary: In order to study the treatment of the children with attention deficit hyperactivity disorder (ADHD), the integrated visual and auditory continuous performance test (IVA-CPT) was clinically applied to evaluate the effectiveness of electroencephalogram (EEG) biofeedback training. Of all the 60 children with ADHD aged more than 6 years, the effective rate of EEG biofeedback training was 91.6 % after 40 sessions of EEG biofeedback training. Before and after treatment by EEG biofeedback training, the overall indexes of IVA were significantly improved among predominately inattentive, hyperactive, and combined subtype of children with ADHD (P<0.001). It was suggested that EEG biofeedback training was an effective and vital treatment on children with ADHD.

  7. The Fingerprint of Rapid Eye Movement: Its Algorithmic Detection in the Sleep Electroencephalogram Using a Single Derivation.

    Science.gov (United States)

    McCarty, David E; Kim, Paul Y; Frilot, Clifton; Chesson, Andrew L; Marino, Andrew A

    2016-10-01

    The strong associations of rapid eye movement (REM) sleep with dreaming and memory consolidation imply the existence of REM-specific brain electrical activity, notwithstanding the visual similarity of the electroencephalograms (EEGs) in REM and wake states. Our goal was to detect REM sleep by means of algorithmic analysis of the EEG. We postulated that novel depth and fragmentation variables, defined in relation to temporal changes in the signal (recurrences), could be statistically combined to allow disambiguation of REM epochs. The cohorts studied were consecutive patients with obstructive sleep apnea (OSA) recruited from a sleep medicine clinic, and clinically normal participants selected randomly from a national database (N = 20 in each cohort). Individual discriminant analyses were performed, for each subject based on 4 recurrence biomarkers, and used to classify every 30-second epoch in the subject's overnight polysomnogram as REM or NotREM (wake or any non-REM sleep stage), using standard clinical staging as ground truth. The primary outcome variable was the accuracy of algorithmic REM classification. Average accuracies of 90% and 87% (initial and cross-validation analyses) were achieved in the OSA cohort; corresponding results in the normal cohort were 87% and 85%. Analysis of brain recurrence allowed identification of REM sleep, disambiguated from wake and all other stages, using only a single EEG lead, in subjects with or without OSA.

  8. Effects of electromagnetic fields emitted from W-CDMA-like mobile phones on sleep in humans.

    Science.gov (United States)

    Nakatani-Enomoto, Setsu; Furubayashi, Toshiaki; Ushiyama, Akira; Groiss, Stefan Jun; Ueshima, Kazumune; Sokejima, Shigeru; Simba, Ally Y; Wake, Kanako; Watanabe, So-ichi; Nishikawa, Masami; Miyawaki, Kaori; Taki, Masao; Ugawa, Yoshikazu

    2013-12-01

    In this study, we investigated subjective and objective effects of mobile phones using a Wideband Code Division Multiple Access (W-CDMA)-like system on human sleep. Subjects were 19 volunteers. Real or sham electromagnetic field (EMF) exposures for 3 h were performed before their usual sleep time on 3 consecutive days. They were exposed to real EMF on the second or third experimental day in a double-blind design. Sleepiness and sleep insufficiency were evaluated the next morning. Polysomnograms were recorded for analyses of the sleep variables and power spectra of electroencephalograms (EEG). No significant differences were observed between the two conditions in subjective feelings. Sleep parameters including sleep stage percentages and EEG power spectra did not differ significantly between real and sham exposures. We conclude that continuous wave EMF exposure for 3 h from a W-CDMA-like system has no detectable effects on human sleep.

  9. Scaling Property in the Alpha Predominant EEG

    CERN Document Server

    Lin, D C; Kwan, H; Lin, Der Chyan; Sharif, Asif; Kwan, Hon

    2004-01-01

    The $\\alpha$ predominant electroencephalographic (EEG) recording of the human brain during eyes open and closed is studied using the zero-crossing time statistics. A model is presented to demonstrate and compare the key characteristics of the brain state. We found the zero-crossing time statistic is more accurate than the power spectral analysis and the detrend fluctuation analysis. Our results indicate different EEG fractal scaling in eyes closed and open for individuals capable of strong $\\alpha$ rhythm.

  10. EEG Signal Classification: Introduction to the Problem

    OpenAIRE

    Stancak, A.; P. Sovka; J. Stastny

    2003-01-01

    The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic shapes of human brain activity (EEG signal). The great variability of EEG potentials requires using...

  11. Oseltamivir reduces hippocampal abnormal EEG activities after a virus infection (influenza) in isoflurane-anesthetized rats

    OpenAIRE

    Inoue,, S.; Kido, Hiroshi

    2012-01-01

    Youssouf Cissé,1 Isao Inoue,2 Hiroshi Kido11Division of Enzyme Chemistry, 2Division of Molecular Neurobiology, Institute for Enzyme Research, University of Tokushima, Tokushima, JapanBackground: Oseltamivir phosphate (OP, Tamiflu®) is a widely used drug in the treatment of influenza with fever. However, case reports have associated OP intake with sudden abnormal behaviors. In rats infected by the influenza A virus (IAV), the electroencephalogram (EEG) displayed abnormal hig...

  12. Dorsal stream vulnerability in preterm infants – A longitudinal EEG study of visual motion perception

    OpenAIRE

    Zotcheva, Ekaterina

    2015-01-01

    High-density electroencephalogram (EEG) was used to longitudinally investigate evoked and induced brain electrical activity as a function of visual motion in full-term and preterm infants at 4-5 and 12 months of age. The infants were presented with two visual motion paradigms, optic flow and looming. The optic flow experiment simulated structured forwards and reversed optic flow and random visual motion, while the looming experiment simulated a looming object approaching on a direct collision...

  13. Correlated Components of Ongoing EEG Point to Emotionally Laden Attention – A Possible Marker of Engagement?

    OpenAIRE

    Dmochowski, Jacek P.; Sajda, Paul; Dias, Joao; Parra, Lucas C.

    2012-01-01

    Recent evidence from functional magnetic resonance imaging suggests that cortical hemodynamic responses coincide in different subjects experiencing a common naturalistic stimulus. Here we utilize neural responses in the electroencephalogram (EEG) evoked by multiple presentations of short film clips to index brain states marked by high levels of correlation within and across subjects. We formulate a novel signal decomposition method which extracts maximally correlated signal components from mu...

  14. EEG Neurofeedback as a Tool to Modulate Creativity in Music Performance

    OpenAIRE

    2014-01-01

    For millennia, anecdotal reports have described how creative insights have been experienced during the transition from wake to sleep (hypnagogia). In the 1970’s, it was reported that the fleeting moments in which hypnagogia and creativity interact are accompanied by characteristic neuroelectric activity, which is disclosed by a specific feature of the Electroencephalogram (EEG): the increase of spectral power in the theta range (5–8 Hz) in relation to alpha (8–11 Hz). Consequently, prior rese...

  15. Analysis of generalized interictal discharges using quantitative EEG.

    Science.gov (United States)

    da Silva Braga, Aline Marques; Fujisao, Elaine Keiko; Betting, Luiz Eduardo

    2014-12-01

    Experimental evidence from animal models of the absence seizures suggests a focal source for the initiation of generalized spike-and-wave (GSW) discharges. Furthermore, clinical studies indicate that patients diagnosed with idiopathic generalized epilepsy (IGE) exhibit focal electroencephalographic abnormalities, which involve the thalamo-cortical circuitry. This circuitry is a key network that has been implicated in the initiation of generalized discharges, and may contribute to the pathophysiology of GSW discharges. Quantitative electroencephalogram (qEEG) analysis may be able to detect abnormalities associated with the initiation of GSW discharges. The objective of this study was to determine whether interictal GSW discharges exhibit focal characteristics using qEEG analysis. In this study, 75 EEG recordings from 64 patients were analyzed. All EEG recordings analyzed contained at least one GSW discharge. EEG recordings were obtained by a 22-channel recorder with electrodes positioned according to the international 10-20 system of electrode placement. EEG activity was recorded for 20 min including photic stimulation and hyperventilation. The EEG recordings were visually inspected, and the first unequivocally confirmed generalized spike was marked for each discharge. Three methods of source imaging analysis were applied: dipole source imaging (DSI), classical LORETA analysis recursively applied (CLARA), and equivalent dipole of independent components with cluster analysis. A total of 753 GSW discharges were identified and spatiotemporally analyzed. Source evaluation analysis using all three techniques revealed that the frontal lobe was the principal source of GSW discharges (70%), followed by the parietal and occipital lobes (14%), and the basal ganglia (12%). The main anatomical sources of GSW discharges were the anterior cingulate cortex (36%) and the medial frontal gyrus (23%). Source analysis did not reveal a common focal source of GSW discharges. However

  16. EEG artifact removal—state-of-the-art and guidelines

    Science.gov (United States)

    Urigüen, Jose Antonio; Garcia-Zapirain, Begoña

    2015-06-01

    This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts. We first introduce background knowledge on the characteristics of EEG activity, of the artifacts and of the EEG measurement model. Then, we present algorithms commonly employed in the literature and describe their key features. Lastly, principally on the basis of the results provided by various researchers, but also supported by our own experience, we compare the state-of-the-art methods in terms of reported performance, and provide guidelines on how to choose a suitable artifact removal algorithm for a given scenario. With this review we have concluded that, without prior knowledge of the recorded EEG signal or the contaminants, the safest approach is to correct the measured EEG using independent component analysis—to be precise, an algorithm based on second-order statistics such as second-order blind identification (SOBI). Other effective alternatives include extended information maximization (InfoMax) and an adaptive mixture of independent component analyzers (AMICA), based on higher order statistics. All of these algorithms have proved particularly effective with simulations and, more importantly, with data collected in controlled recording conditions. Moreover, whenever prior knowledge is available, then a constrained form of the chosen method should be used in order to incorporate such additional information. Finally, since which algorithm is the best performing is highly dependent on the type of the EEG signal, the artifacts and the signal to contaminant ratio, we believe that the optimal method for removing artifacts from the EEG consists in combining more than one algorithm to correct the signal using multiple processing stages, even though this is an option largely unexplored by researchers in the area.

  17. Decoding of visual information from human brain activity: A review of fMRI and EEG studies.

    Science.gov (United States)

    Zafar, Raheel; Malik, Aamir Saeed; Kamel, Nidal; Dass, Sarat C; Abdullah, Jafri M; Reza, Faruque; Abdul Karim, Ahmad Helmy

    2015-06-01

    Brain is the command center for the body and contains a lot of information which can be extracted by using different non-invasive techniques. Electroencephalography (EEG), Magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) are the most common neuroimaging techniques to elicit brain behavior. By using these techniques different activity patterns can be measured within the brain to decode the content of mental processes especially the visual and auditory content. This paper discusses the models and imaging techniques used in visual decoding to investigate the different conditions of brain along with recent advancements in brain decoding. This paper concludes that it's not possible to extract all the information from the brain, however careful experimentation, interpretation and powerful statistical tools can be used with the neuroimaging techniques for better results.

  18. EEG and Sonic Platforms to Enhance Mindfulness Meditation

    Directory of Open Access Journals (Sweden)

    Caitilin de Berigny

    2016-09-01

    Full Text Available This paper explores interactive applications that encourage mindfulness through sensors and novel input technology. Research in psychology and neuroscience demonstrating the benefits of mindfulness is initiating a new movement in interactive design. As cutting edge technologies become more accessible they are being employed to research and explore the practice of mindfulness. We examine three interactive installation artworks that promote mindfulness. In order to contextualize the interactive artworks discussed we first examine the historical background of the Electroencephalogram (EEG. We then discuss the physiological processes of meditation and the history behind the clinical practice of mindfulness. We show how artists and designers employ EEG sensors, to record the electrical activity of the brain to visualize mindfulness meditation practices. Lastly, we conclude the paper by discussing the future of the three artworks.

  19. EEG Signal Classification With Super-Dirichlet Mixture Model

    DEFF Research Database (Denmark)

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

    2012-01-01

    Classification of the Electroencephalogram (EEG) signal is a challengeable task in the brain-computer interface systems. The marginalized discrete wavelet transform (mDWT) coefficients extracted from the EEG signals have been frequently used in researches since they reveal features related to the...... vector machine (SVM) based classifier, the SDMM based classifier performs more stable and shows a promising improvement, with both channel selection strategies....... by the Dirichlet distribution and the distribution of the mDWT coefficients from more than one channels is described by a super-Dirichletmixture model (SDMM). The Fisher ratio and the generalization error estimation are applied to select relevant channels, respectively. Compared to the state-of-the-art support...

  20. 基于TGAM模块和脑电波对音响音量的控制%Research on the Electroencephalogram to Control the Volume Based on TGAM Module

    Institute of Scientific and Technical Information of China (English)

    肖迪; 章文韬

    2015-01-01

    Electroencephalogram(EEG) signal now has been studied as a biological signal input for human-computer interaction. It can be used for games of lifting the attention, can also be used in the treatment of the disabled. TGAM module uses a dry-type elec⁃trode to detect the weak EEG signal from human brain. This paper studies the TGAM module, controlling the degree of focus and relaxation degree values. Software development bases on MDT-based, researching the unity and particularity of brainwave signals. Apply the TGAM module on the player, through the application interface, establishing communication, carrying out data analysis and process, to achieve better control effect.%脑电波信号现今已被研究作为一个生物信号输入用于人机交互。它可以用来开发用于提升注意力的游戏,也可以被应用在残疾人治疗上。TGAM模块利用一个干式电极就可以从人脑中检测到微弱的脑电信号。该文对TGAM模块的研究和实验,控制专注度和放松度等数值。基于MDT开发包进行软件开发,研究脑电波信号的统一性与特殊性,将TGAM模块应用于播放器,通过应用程序接口,建立通讯,进行数据分析处理,实现了比较好的调控效果。

  1. Dysesthesia symptoms produced by sensorimotor incongruence in healthy volunteers: an electroencephalogram study

    Directory of Open Access Journals (Sweden)

    Katayama O

    2016-12-01

    Full Text Available Osamu Katayama,1,2 Michihiro Osumi,3 Takayuki Kodama,4 Shu Morioka,1,3 1Department of Neurorehabilitation, Graduate School of Health Sciences, Kio University, Nara, 2Department of Rehabilitation, Watanabe Hospital, Aichi, 3Department of Neurorehabilitation Research Center, Kio University, Nara, 4Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, Kyoto, Japan Objectives: Pathological pain such as phantom limb pain is caused by sensorimotor incongruence. Several studies with healthy participants have clearly indicated that dysesthesia, which is similar to pathological pain, is caused by incongruence between proprioception and/or motor intention and visual feedback. It is not clear to what extent dysesthesia may be caused by incongruence between motor intention and visual feedback or by incongruence between proprioception and visual feedback. The aim of this study was to clarify the neurophysiology of these factors by analyzing electroencephalograms (EEGs.Methods: In total, 18 healthy participants were recruited for this study. Participants were asked to perform repetitive flexion/extension exercises with their elbows in a congruent/incongruent position while viewing the activity in a mirror. EEGs were performed to determine cortical activation during sensorimotor congruence and incongruence.Results: In the high-frequency alpha band (10–12 Hz, numeric rating scale scores of a feeling of peculiarity were significantly correlated with event-related desynchronization/synchronization under the incongruence and proprioception conditions associated with motor intention and visual feedback (right inferior parietal region; r=−0.63, P<0.01 and between proprioception and visual feedback (right temporoparietal region; r=−0.49 and r=−0.50, P<0.05. In these brain regions, there was a region in which incongruence between proprioception and visual feedback and between motor intention and visual feedback caused

  2. Human brain activity patterns beyond the isoelectric line of extreme deep coma.

    Directory of Open Access Journals (Sweden)

    Daniel Kroeger

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

  3. Human brain activity patterns beyond the isoelectric line of extreme deep coma.

    Science.gov (United States)

    Kroeger, Daniel; Florea, Bogdan; Amzica, Florin

    2013-01-01

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

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

    NARCIS (Netherlands)

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

    2011-01-01

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

  5. A Study of the effect of sound on EEG

    Directory of Open Access Journals (Sweden)

    Renu Bhoria,

    2013-01-01

    Full Text Available This paper presents a brief study of various effects of sound on the human brain activity. This can be shown through the study of EEG signal recorded. The effect is in the form of variation in either frequency or in the power of different EEG bands. A biomedical signal electroencephalography (EEG reflects the state of mind and is often used to verify the influence of music on human brain activity. In fact EEG signals are related to the characteristic parameters of brain electrical activity. Moreover as our mind state changes EEG changes accordingly. The raw EEG cannot be observed or used efficiently. Hence various techniques like time frequency analysis has been employed to read the effects.

  6. EFFICACY OF ACTIVATION PROCEDURES TO ILLUSTRATE EEG CHANGES IN EPILEPSY

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    Rimpy Bhuyan

    2017-04-01

    Full Text Available BACKGROUND EEG or Electroencephalogram, which is the most important diagnostic procedure to evaluate Epilepsy patients, may sometimes fall short of accurate sensitivity and may require few Activation Procedures such as ‘Hyperventilation’ and ‘Sleep’ to bring out the active changes of an Epileptic brain. The present study was done with the aim of knowing the efficacy of such Activation Procedures like ‘Hyperventilation’ and ‘Sleep’ in illustrating the EEG wave pattern changes of an Epileptic brain during the interictal period. MATERIALS AND METHODS The present study was done in the Department of Physiology in association with the Department of Neurology, Assam Medical College & Hospital, Dibrugarh, Assam from June 2014 to May 2015. ‘113’ clinically diagnosed cases of Epilepsy were studied and analysed through Electroencephalogram using the internationally accepted 10-20 electrode placement method. Hyperventilation was used in 28 Epilepsy cases and Sleep was used in 14 Epilepsy cases. History & Physical examination findings were recorded in a Proforma. Chi-square analysis was done through GraphPad Prism 6 software to assess the significance of the activation procedures used. RESULTS Our study found that EEG of 42 cases out of the total 113 cases required Activation Procedures to elicit the wave pattern changes of the Epileptic brain. Hyperventilation was helpful in adult age group and sleep was useful in children age group. Hyperventilation had overall 53.57% sensitivity in detecting Epilepsy, and Sleep had 64.29% sensitivity in detecting Epilepsy. Hyperventilation was specifically helpful to elicit absence seizures where it had 75% sensitivity. CONCLUSION The sensitivity of EEG in detecting Epilepsy can thus be increased by using activation procedures like sleep & Hyperventilation to ensure that no epilepsy cases are missed out in diagnosis & treatment.

  7. A derived transfer of eliciting emotional functions using differences among electroencephalograms as a dependent measure.

    Science.gov (United States)

    Amd, Micah; Barnes-Holmes, Dermot; Ivanoff, Jason

    2013-05-01

    Emotional responses have specific electroencephalographic (EEG) signatures that arise within a few hundred milliseconds post-stimulus onset. In this experiment, EEG measures were employed to assess for transfer of emotional functions across three 3-member equivalence classes in an extension of Dougher, Auguston, Markham, Greenway, & Wulfert's (1994) seminal work on the transfer of arousal functions. Specifically, 12 human participants were trained in the following match-to-sample performances A1 = B1, A2 = B2, A3 = B3 and B1 = C1, B2 = C2, B3 = C3. After successfully testing for the emergence of symmetry relations (B1 = A1, B2 = A2, B3 = A3 and C1 = B1, C2 = B2, C3 = B3), visual images depicting emotionally positive and emotionally negative content were presented with A1 and A3, respectively, using a mixed stimulus pairing-compounding procedure. A2 was paired with emotionally neutral images. Next, EEG data were recorded as participants were exposed to a forced-choice recognition task with stimuli A1, B1, C1, A2, B2, C2, A3, B3, C3 and three novel stimuli A4, B4 and C4. Results yielded differential EEG effects for stimuli paired directly with emotional versus neutral images. Critically, differential EEG effects were also recorded across the C stimuli that were equivalently related to the A stimulus set. The EEG data coincide with previous reports of emotion-specific EEG effects, indicating that the initial emotional impact of a stimulus may emerge based on direct stimulus pairing and derived stimulus relations. © Society for the Experimental Analysis of Behavior.

  8. Classification of 5-S Epileptic EEG Recordings Using Distribution Entropy and Sample Entropy

    Science.gov (United States)

    Li, Peng; Karmakar, Chandan; Yan, Chang; Palaniswami, Marimuthu; Liu, Changchun

    2016-01-01

    Epilepsy is an electrophysiological disorder of the brain, the hallmark of which is recurrent and unprovoked seizures. Electroencephalogram (EEG) measures electrical activity of the brain that is commonly applied as a non-invasive technique for seizure detection. Although a vast number of publications have been published on intelligent algorithms to classify interictal and ictal EEG, it remains an open question whether they can be detected using short-length EEG recordings. In this study, we proposed three protocols to select 5 s EEG segment for classifying interictal and ictal EEG from normal. We used the publicly-accessible Bonn database, which consists of normal, interical, and ictal EEG signals with a length of 4097 sampling points (23.6 s) per record. In this study, we selected three segments of 868 points (5 s) length from each recordings and evaluated results for each of them separately. The well-studied irregularity measure—sample entropy (SampEn)—and a more recently proposed complexity measure—distribution entropy (DistEn)—were used as classification features. A total of 20 combinations of input parameters m and τ for the calculation of SampEn and DistEn were selected for compatibility. Results showed that SampEn was undefined for half of the used combinations of input parameters and indicated a large intra-class variance. Moreover, DistEn performed robustly for short-length EEG data indicating relative independence from input parameters and small intra-class fluctuations. In addition, it showed acceptable performance for all three classification problems (interictal EEG from normal, ictal EEG from normal, and ictal EEG from interictal) compared to SampEn, which showed better results only for distinguishing normal EEG from interictal and ictal. Both SampEn and DistEn showed good reproducibility and consistency, as evidenced by the independence of results on analysing protocol. PMID:27148074

  9. Spatiotemporal Analysis of Multichannel EEG: CARTOOL

    Directory of Open Access Journals (Sweden)

    Denis Brunet

    2011-01-01

    Full Text Available This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

  10. Spatiotemporal analysis of multichannel EEG: CARTOOL.

    Science.gov (United States)

    Brunet, Denis; Murray, Micah M; Michel, Christoph M

    2011-01-01

    This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way.

  11. 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.

  12. Improved diagnosis in children with partial epilepsy using a multivariable prediction model based on EEG network characteristics.

    Directory of Open Access Journals (Sweden)

    Eric van Diessen

    Full Text Available BACKGROUND: Electroencephalogram (EEG acquisition is routinely performed to support an epileptic origin of paroxysmal events in patients referred with a possible diagnosis of epilepsy. However, in children with partial epilepsies the interictal EEGs are often normal. We aimed to develop a multivariable diagnostic prediction model based on electroencephalogram functional network characteristics. METHODOLOGY/PRINCIPAL FINDINGS: Routinely performed interictal EEG recordings at first presentation of 35 children diagnosed with partial epilepsies, and of 35 children in whom the diagnosis epilepsy was excluded (control group, were used to develop the prediction model. Children with partial epilepsy were individually matched on age and gender with children from the control group. Periods of resting-state EEG, free of abnormal slowing or epileptiform activity, were selected to construct functional networks of correlated activity. We calculated multiple network characteristics previously used in functional network epilepsy studies and used these measures to build a robust, decision tree based, prediction model. Based on epileptiform EEG activity only, EEG results supported the diagnosis of with a sensitivity and specificity of 0.77 and 0.91 respectively. In contrast, the prediction model had a sensitivity of 0.96 [95% confidence interval: 0.78-1.00] and specificity of 0.95 [95% confidence interval: 0.76-1.00] in correctly differentiating patients from controls. The overall discriminative power, quantified as the area under the receiver operating characteristic curve, was 0.89, defined as an excellent model performance. The need of a multivariable network analysis to improve diagnostic accuracy was emphasized by the lack of discriminatory power using single network characteristics or EEG's power spectral density. CONCLUSIONS/SIGNIFICANCE: Diagnostic accuracy in children with partial epilepsy is substantially improved with a model combining functional

  13. Detection the Character Wave in Epileptic EEG by Wavelet

    Institute of Scientific and Technical Information of China (English)

    CHEN Huafu; NIU Hai

    2004-01-01

    Human epilepsy is an intrinsic brain pathology,which can be characterized by repetitive high-amplitude electroencephalograph (EEG) activity.The wavelet transform provides an important tool in signal analysis and feature extraction.In this paper,the modulus maximum pair of a wavelet transform is used to detect the singularity value of the sharps and the spikes embedded in the background activities of the epilepsy EEG.The efficacy of the proposed method has been tested with clinical EEG.

  14. Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent component analysis based recursive least squares in brain-computer interface

    Institute of Scientific and Technical Information of China (English)

    Bang-hua YANG; Liang-fei HE; Lin LIN; Qian WANG

    2015-01-01

    Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

  15. Nonlinear signal processing of electroencephalograms for automated sleep monitoring

    Science.gov (United States)

    Wilson, D.; Rowlands, D. D.; James, Daniel A.; Cutmore, T.

    2005-02-01

    An automated classification technique is desirable to identify the different stages of sleep. In this paper a technique for differentiating the characteristics of each sleep phase has been developed. This is an ideal pre-processor stage for classifying systems such as neural networks. A wavelet based continuous Morlet transform was developed to analyse the EEG signal in both the time and frequency domain. Test results using two 100 epoch EEG test data sets from pre-recorded EEG data are presented. Key rhythms in the EEG signal were identified and classified using the continuous wavelet transform. The wavelet results indicated each sleep phase contained different rhythms and artefacts (noise from muscle movement in the EEG); providing proof that an EEG can be classified accordingly. The coefficients founded by the wavelet transform have been emphasised by statistical techniques. Hypothesis testing was used to highlight major differences between adjacent sleep stages. Various signal processing methods such as power spectrum density and the discrete wavelet transform have been used to emphasise particular characteristics in an EEG. By implementing signal processing methods on an EEG data set specific rules for each sleep stage have been developed suitable for a neural network classification solution.

  16. 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

    2017-05-08

    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.

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Science.gov (United States)

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

    2002-01-30

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

  19. EEG seizure detection and prediction algorithms: a survey

    Science.gov (United States)

    Alotaiby, Turkey N.; Alshebeili, Saleh A.; Alshawi, Tariq; Ahmad, Ishtiaq; Abd El-Samie, Fathi E.

    2014-12-01

    Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

  20. [Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].

    Science.gov (United States)

    Zhang, Meiyun; Zhang, Benshu; Chen, Ying

    2014-08-01

    Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pentropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pentropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, Pentropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.