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Sample records for group eeg show

  1. On the "Dependence" of "Independent" Group EEG Sources; an EEG Study on Two Large Databases.

    OpenAIRE

    Congedo, Marco; John, Roy; RIDDER, Dirk De; Prichep, Leslie; Isenhart, Robert

    2010-01-01

    International audience; The aim of this work is to study the coherence profile (dependence) of robust eyes-closed resting EEG sources isolated by group blind source separation (gBSS). We employ a test-retest strategy using two large sample normative databases (N = 57 and 84). Using a BSS method in the complex Fourier domain, we show that we can rigourously study the out-of-phase dependence of the extracted components, albeit they are extracted so as to be in-phase independent (by BSS definiti...

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

    DEFF Research Database (Denmark)

    Bruun, Peter

    1998-01-01

    The intention of this paper is to clarify if and how an ExperienceExchange Group (EEG) can be involved in a research process in the areaof industrial management. For exemplification of the topic an ongoingresearch in global manufacturing is referred to. In this research itwas after a series...... activities aredescribed and a tentative coupling to the phases in a research processis proposed. Following this is a discussion of methodological andquality requirements. It is considered how EEG activities couldpossible contribute to an industrial rooted research. The paper endsup looking at future research...

  3. 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 ...

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

    Directory of Open Access Journals (Sweden)

    Jin-Jia Wang

    2015-01-01

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

  5. Simultaneous channel and feature selection of fused EEG features based on Sparse Group Lasso.

    Science.gov (United States)

    Wang, Jin-Jia; Xue, Fang; Li, Hui

    2015-01-01

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

  6. Group-level component analyses of EEG: validation and evaluation

    Directory of Open Access Journals (Sweden)

    Rene eHuster

    2015-07-01

    Full Text Available Multi-subject or group-level component analysis provides a data-driven approach to study properties of brain networks. Algorithms for group-level data decomposition of functional magnetic resonance imaging data have been brought forward more than a decade ago and have significantly matured since. Similar applications for electroencephalographic data are at a comparatively early stage of development though, and their sensitivity to topographic variability of the electroencephalogram or loose time-locking of neuronal responses has not yet been assessed. This study investigates the performance of independent component analysis (ICA and second order blind source identification (SOBI for data decomposition, and their combination with either temporal or spatial concatenation of data sets, for multi-subject analyses of electroencephalographic data. indent Analyses of simulated sources with different spatial, frequency, and time-locking profiles, revealed that temporal concatenation of data sets with either ICA or SOBI served well to reconstruct sources with both strict and loose time-locking, whereas performance decreased in the presence of topographical variability. The opposite pattern was found with a spatial concatenation of subject-specific data sets.This study proofs that procedures for group-level decomposition of electroencephalographic data can be considered valid and promising approaches to infer the latent structure of multi-subject data sets. Yet, specific implementations need further adaptations to optimally address sources of inter-subject and inter-trial variance commonly found in EEG recordings.

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

    DEFF Research Database (Denmark)

    Bruun, Peter

    1997-01-01

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

  8. EEGIFT: Group Independent Component Analysis for Event-Related EEG Data

    Directory of Open Access Journals (Sweden)

    Tom Eichele

    2011-01-01

    Full Text Available Independent component analysis (ICA is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

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

    DEFF Research Database (Denmark)

    Bruun, Peter

    1997-01-01

    The intention of this paper is to clarify if and how an Experience Exchange Group(EEG) can be involved in a research process in the area of industrial management. For exemplification of the topic an ongoing research in global manufacturing is referred to. In this research it was after a series...

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

    National Research Council Canada - National Science Library

    Michael Lim; Justin M Ales; Benoit R Cottereau; Trevor Hastie; Anthony M Norcia

    2017-01-01

    .... The approach is based on the group lasso, a sparse-prior inverse that has been adapted to take advantage of functionally-defined regions of interest for the definition of physiologically meaningful...

  11. The sleep EEG topography in children and adolescents shows sex differences in language areas.

    Science.gov (United States)

    Ringli, Maya; Kurth, Salomé; Huber, Reto; Jenni, Oskar G

    2013-08-01

    The topographic distribution of slow wave activity (SWA, EEG power between 0.75 and 4.5 Hz) during non-rapid eye movement (NREM) sleep was proposed to mirror cortical maturation with a typical age-related pattern. Here, we examined whether sex differences occur in SWA topography of children and adolescents (22 age-matched subjects, 11 boys, mean age 13.4 years, range: 8.7-19.4, and 11 girls, mean age 13.4 years, range: 9.1-19.0 years). In females, SWA during the first 60 min of NREM sleep was higher over bilateral cortical areas that are related to language functions, while in males SWA was increased over the right prefrontal cortex, a region also involved in spatial abilities. We conclude that cortical areas governing functions in which one sex outperforms the other exhibit increased sleep SWA and, thus, may indicate maturation of sex-specific brain function and higher cortical plasticity during development.

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

    Science.gov (United States)

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

    2014-05-01

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

  13. Brain and cognitive functions in two groups of naïve HIV patients selected for a different plan of antiretroviral therapy: A qEEG study.

    Science.gov (United States)

    Babiloni, Claudio; Pennica, Alfredo; Capotosto, Paolo; Onorati, Paolo; Muratori, Chiara; Ferracuti, Stefano; Roma, Paolo; Correr, Valentina; Piccinni, Elisa; Noce, Giuseppe; Del Percio, Claudio; Cordone, Susanna; Limatola, Cristina; Soricelli, Andrea; Di Campli, Francesco; Gianserra, Laura; Ciullini, Lorenzo; Aceti, Antonio; Viscione, Magdalena; Teti, Elisabetta; Sarmati, Loredana; Andreoni, Massimo

    2016-11-01

    Cortical sources of electroencephalographic (EEG) rhythms were investigated in two sub-populations of naïve HIV subjects, grouped based on clinical criteria to receive different combination anti-retroviral therapies (cARTs). These EEG sources were hypothesized to reflect beneficial effects of both regimes. Eyes-closed resting state EEG data were collected in 19 (Group A) and 39 (Group B) naïve HIV subjects at baseline (i.e. pre-treatment; T0) and after 5months of cART (T5). Compared with the Group A, the Group B was characterized by slightly worse serological parameters and higher cardiovascular risk. At T0, mean viral load (VL) and CD4 count were 87,694copies/ml and 435cells/μl in the Group A and 187,370copies/ml and 331cells/μl in the Group B. The EEG data were also collected in 50 matched control HIV-negative subjects. Cortical EEG sources were assessed by LORETA software. Compared to the Control Group, the HIV Groups showed lower alpha (8-12Hz) source activity at T0 while the Group B also exhibited higher delta source activity. The treatment partially normalized alpha and delta source activity in the Group A and B, respectively, in association with improved VL, CD4, and cognitive functions. Different cART regimens induced diverse beneficial effects in delta or alpha source activity in the two naïve HIV Groups. These sources might unveil different neurophysiological effects of diverse cART on brain function in naïve HIV Groups as a function of clinical status and/or therapeutic compounds. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  14. 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.

  15. The impacts of hypnotic susceptibility on chaotic dynamics of EEG signals during standard tasks of Waterloo-Stanford Group Scale.

    Science.gov (United States)

    Yargholi, Elahe'; Nasrabadi, Ali Motie

    2013-05-01

    Chaotic features of hypnotic EEG (electroencephalograph), recorded during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility (WSGS), were used to investigate the underlying dynamic of tasks and analyse the effect of hypnotic depth and concentration on EEG signals. Results demonstrate: (1) More efficiency of Higuchi dimension in comparison with Correlation dimension to distinguish subjects from different hypnotizable groups, (2) Channels with significantly different chaotic features among people from various hypnotizability levels in tasks, (3) High level of consistency among discriminating channels of tasks with function of brain's lobes, (4) Most affectability of medium hypnotizable subjects and (5) Rise in fractal dimensions due to increase in hypnosis depth.

  16. 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

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

    Science.gov (United States)

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

    2012-01-01

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

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

    NARCIS (Netherlands)

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

    2008-01-01

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

  19. EEG correlates of virtual reality hypnosis.

    Science.gov (United States)

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

    2009-01-01

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

  20. Only Three Fingers Write, but the Whole Brain Works: A High-Density EEG Study Showing Advantages of Drawing Over Typing for Learning.

    Science.gov (United States)

    van der Meer, Audrey L H; van der Weel, F R Ruud

    2017-01-01

    Are different parts of the brain active when we type on a keyboard as opposed to when we draw visual images on a tablet? Electroencephalogram (EEG) was used in young adults to study brain electrical activity as they were typing or describing in words visually presented Pictionary(TM) words using a keyboard, or as they were drawing pictures of the same words on a tablet using a stylus. Analyses of temporal spectral evolution (time-dependent amplitude changes) were performed on EEG data recorded with a 256-channel sensor array. We found that when drawing, brain areas in the parietal and occipital regions showed event related desynchronization activity in the theta/alpha range. Existing literature suggests that such oscillatory neuronal activity provides the brain with optimal conditions for learning. When describing the words using the keyboard, upper alpha/beta/gamma range activity in the central and frontal brain regions were observed, especially during the ideation phase. However, since this activity was highly synchronized, its relation to learning remains unclear. We concluded that because of the benefits for sensory-motor integration and learning, traditional handwritten notes are preferably combined with visualizations (e.g., small drawings, shapes, arrows, symbols) to facilitate and optimize learning.

  1. 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

  2. EEG biofeedback

    OpenAIRE

    Dvořáček, Michael

    2010-01-01

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

  3. Quantitative EEG evaluation in patients with acute encephalopathy

    Directory of Open Access Journals (Sweden)

    Aline Souza Marques da Silva Braga

    2013-12-01

    Full Text Available Objective To investigate the use of quantitative EEG (qEEG in patients with acute encephalopathies (AEs and EEG background abnormalities. Method Patients were divided into favorable outcome (group A, 43 patients and an unfavorable outcome (group B, 5 patients. EEGLAB software was used for the qEEG analysis. A graphic of the spectral power from all channels was generated for each participant. Statistical comparisons between the groups were performed. Results In group A, spectral analysis revealed spectral peaks (theta and alpha frequency bands in 84% (38/45 of the patients. In group B, a spectral peak in the delta frequency range was detected in one patient. The remainder of the patients in both groups did not present spectral peaks. Statistical analysis showed lower frequencies recorded from the posterior electrodes in group B patients. Conclusion qEEG may be useful in the evaluations of patients with AEs by assisting with the prognostic determination.

  4. Is EEG-biofeedback an effective treatment in autism spectrum disorders? A randomized controlled trial.

    Science.gov (United States)

    Kouijzer, Mirjam E J; van Schie, Hein T; Gerrits, Berrie J L; Buitelaar, Jan K; de Moor, Jan M H

    2013-03-01

    EEG-biofeedback has been reported to reduce symptoms of autism spectrum disorders (ASD) in several studies. However, these studies did not control for nonspecific effects of EEG-biofeedback and did not distinguish between participants who succeeded in influencing their own EEG activity and participants who did not. To overcome these methodological shortcomings, this study evaluated the effects of EEG-biofeedback in ASD in a randomized pretest-posttest control group design with blinded active comparator and six months follow-up. Thirty-eight participants were randomly allocated to the EEG-biofeedback, skin conductance (SC)-biofeedback or waiting list group. EEG- and SC-biofeedback sessions were similar and participants were blinded to the type of feedback they received. Assessments pre-treatment, post-treatment, and after 6 months included parent ratings of symptoms of ASD, executive function tasks, and 19-channel EEG recordings. Fifty-four percent of the participants significantly reduced delta and/or theta power during EEG-biofeedback sessions and were identified as EEG-regulators. In these EEG-regulators, no statistically significant reductions of symptoms of ASD were observed, but they showed significant improvement in cognitive flexibility as compared to participants who managed to regulate SC. EEG-biofeedback seems to be an applicable tool to regulate EEG activity and has specific effects on cognitive flexibility, but it did not result in significant reductions in symptoms of ASD. An important finding was that no nonspecific effects of EEG-biofeedback were demonstrated.

  5. Whole scalp resting state EEG of oscillatory brain activity shows no parametric relationship with psychoacoustic and psychosocial assessment of tinnitus: A repeated measures study.

    Science.gov (United States)

    Pierzycki, Robert H; McNamara, Adam J; Hoare, Derek J; Hall, Deborah A

    2016-01-01

    Tinnitus is a perception of sound that can occur in the absence of an external stimulus. A brief review of electroencephalography (EEG) and magnetoencephalography (MEG) literature demonstrates that there is no clear relationship between tinnitus presence and frequency band power in whole scalp or source oscillatory activity. Yet a preconception persists that such a relationship exists and that resting state EEG could be utilised as an outcome measure for clinical trials of tinnitus interventions, e.g. as a neurophysiological marker of therapeutic benefit. To address this issue, we first examined the test-retest correlation of EEG band power measures in tinnitus patients (n = 42). Second we examined the evidence for a parametric relationship between numerous commonly used tinnitus variables (psychoacoustic and psychosocial) and whole scalp EEG power spectra, directly and after applying factor reduction techniques. Test-retest correlation for both EEG band power measures and tinnitus variables were high. Yet we found no relationship between whole scalp EEG band powers and psychoacoustic or psychosocial variables. We conclude from these data that resting state whole scalp EEG should not be used as a biomarker for tinnitus and that greater caution should be exercised in regard to reporting of findings to avoid confirmation bias. The data was collected during a randomised controlled trial registered at ClinicalTrials.gov (Identifier: NCT01541969).

  6. EEG activity during estral cycle in the rat.

    Science.gov (United States)

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

    1992-10-01

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

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

    Science.gov (United States)

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

    2014-08-01

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

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

    Science.gov (United States)

    Trautmann-Lengsfeld, Sina Alexa; Herrmann, Christoph Siegfried

    2013-01-01

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

  9. Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department

    OpenAIRE

    Omurtag, Ahmet; Baki, Samah G Abdel; Chari, Geetha; Cracco, Roger Q; Zehtabchi, Shahriar; Fenton, André A.; Grant, Arthur C

    2012-01-01

    Background We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). Methods The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the...

  10. EEG frequency-amplitude characteristics of the successful recognition of emotional speech.

    Science.gov (United States)

    Kislova, O O; Rusalova, M N

    2010-07-01

    EEG frequency-amplitude characteristics were studied in two groups of subjects, with high and low "emotional hearing" measures. Comparison of power over the whole EEG range between the two groups of subjects led to the conclusion that the EEG activation level was significantly higher in subjects with low "emotional hearing" measures than in those with high levels. This group also showed a higher level of activation in the posterior temporal areas of the cortex of the right hemisphere on recognition of emotions in speech. Thus, high initial levels of cortical activation and greater EEG reactivity on hearing emotional phrases are factors hindering the recognition of emotional expression in speech.

  11. Normalization of Pain-Evoked Neural Responses Using Spontaneous EEG Improves the Performance of EEG-Based Cross-Individual Pain Prediction

    Science.gov (United States)

    Bai, Yanru; Huang, Gan; Tu, Yiheng; Tan, Ao; Hung, Yeung Sam; Zhang, Zhiguo

    2016-01-01

    An effective physiological pain assessment method that complements the gold standard of self-report is highly desired in pain clinical research and practice. Recent studies have shown that pain-evoked electroencephalography (EEG) responses could be used as a readout of perceived pain intensity. Existing EEG-based pain assessment is normally achieved by cross-individual prediction (i.e., to train a prediction model from a group of individuals and to apply the model on a new individual), so its performance is seriously hampered by the substantial inter-individual variability in pain-evoked EEG responses. In this study, to reduce the inter-individual variability in pain-evoked EEG and to improve the accuracy of cross-individual pain prediction, we examined the relationship between pain-evoked EEG, spontaneous EEG, and pain perception on a pain EEG dataset, where a large number of laser pulses (>100) with a wide energy range were delivered. Motivated by our finding that an individual's pain-evoked EEG responses is significantly correlated with his/her spontaneous EEG in terms of magnitude, we proposed a normalization method for pain-evoked EEG responses using one's spontaneous EEG to reduce the inter-individual variability. In addition, a nonlinear relationship between the level of pain perception and pain-evoked EEG responses was obtained, which inspired us to further develop a new two-stage pain prediction strategy, a binary classification of low-pain and high-pain trials followed by a continuous prediction for high-pain trials only, both of which used spontaneous-EEG-normalized magnitudes of evoked EEG responses as features. Results show that the proposed normalization strategy can effectively reduce the inter-individual variability in pain-evoked responses, and the two-stage pain prediction method can lead to a higher prediction accuracy. PMID:27148028

  12. [The EEG and thinking].

    Science.gov (United States)

    Petsche, H

    1990-12-01

    The on-going EEG contains information on thinking strategies during cognitive and creative tasks and during listening to music. This was demonstrated by a method taking use of the fact that both the amount of local current production and the degree of electric coupling of brain regions is characteristically changed by mental tasks. In groups of volunteers the significant changes of absolute power and coherence caused by different mental tasks are computed and entered into schematic brain maps (EEG probability maps). The results indicate the existence of general brain strategies even in mental activities as specific as those referred to above. Moreover, several relationships between EEG, psychological test scores, degree of special education and intelligence were found. Studies with extreme value validation according to intelligence and creativity test scores yielded significant differences between the groups of the best and the poorest performers during a creative task in the EEG. The EEG thus can be conceived of as deterministic chaos with different degrees of organization according to its information content. In this context, the question arises as to a possible function of the EEG for the optimization of thinking processes.

  13. Common marmosets show social plasticity and group-level similarity in personality.

    Science.gov (United States)

    Koski, Sonja E; Burkart, Judith M

    2015-03-06

    The social environment influences animal personality on evolutionary and immediate time scales. However, studies of animal personality rarely assess the effects of the social environment, particularly in species that live in stable groups with individualized relationships. We assessed personality experimentally in 17 individuals of the common marmoset, living in four groups. We found their personality to be considerably modified by the social environment. Marmosets exhibited relatively high plasticity in their behaviour, and showed 'group-personality', i.e. group-level similarity in the personality traits. In exploratory behaviour this was maintained only in the social environment but not when individuals were tested alone, suggesting that exploration tendency is subjected to social facilitation. Boldness, in contrast, showed higher consistency across the social and solitary conditions, and the group-level similarity in trait scores was sustained also outside of the immediate social environment. The 'group-personality' was not due to genetic relatedness, supporting that it was produced by social effects. We hypothesize that 'group-personality' may be adaptive for highly cooperative animals through facilitating cooperation among individuals with similar behavioural tendency.

  14. Studying the default mode and its mindfulness-induced changes using EEG functional connectivity

    OpenAIRE

    2013-01-01

    The default mode network (DMN) has been largely studied by imaging, but not yet by neurodynamics, using electroencephalography (EEG) functional connectivity (FC). mindfulness meditation (MM), a receptive, non-elaborative training is theorized to lower DMN activity. We explored: (i) the usefulness of EEG-FC for investigating the DMN and (ii) the MM-induced EEG-FC effects. To this end, three MM groups were compared with controls, employing EEG-FC (–MPC, mean phase coherence). Our results show t...

  15. Topographic quantitative EEG amplitude in recovered alcoholics.

    Science.gov (United States)

    Pollock, V E; Schneider, L S; Zemansky, M F; Gleason, R P; Pawluczyk, S

    1992-05-01

    Topographic measures of electroencephalographic (EEG) amplitude were used to compare recovered alcoholics (n = 14) with sex- and age-matched control subjects. Delta, alpha, and beta activity did not distinguish the groups, but regional differences in theta distribution did. Recovered alcoholics showed more uniform distributions of theta amplitudes in bilateral anterior and posterior regions compared with controls. Because a minimum of 5 years had elapsed since the recovered alcoholic subjects fulfilled DSM-III-R criteria for alcohol abuse or dependence, it is unlikely these EEG theta differences reflect the effects of withdrawal.

  16. Fingerprinting using extrolite profiles and physiological data shows sub-specific groupings of Penicillium crustosum strains

    DEFF Research Database (Denmark)

    Sonjak, Silva; Frisvad, Jens Christian; Gunde-Cimerman, Nina

    2009-01-01

    by previous amplified fragment length polymorphism (AFLP) study. We thus demonstrate here for the first time that combined qualitative and quantitative extrolite profiles can be used as a tool for phenotypic fingerprinting, to complement, or replace, molecular fingerprinting techniques....... water activity. Principal component analysis (PCA) was performed using micromorphological data, temperature- and water-dependent growth rates, and extrolite profiles obtained by HPLC analysis. The micromorphological data were less informative, while the growth-rate data were informative only...... if the strains investigated already showed slight adaptations to the selected external parameter. In contrast, PCA analyses of the extrolite data showed groupings of the strains according to their origins and known physiological differences. These groupings are in full agreement with the clustering obtained...

  17. 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.

  18. The characteristics of ictal EEG showed in local or lateralization of tuberous sclerosis with infantile spasms(A report about 31 cases)%结节性硬化所致婴儿痉挛症发作期脑电局灶或一侧优势现象31例报告

    Institute of Scientific and Technical Information of China (English)

    杨梅华; 张琴; 蔡方成; 黄轶; 刘仕勇; 杨辉; 安宁; 黄婷; 刘立红; 石先俊; 肖农

    2011-01-01

    目的:探讨结节性硬化症(TS)所致婴儿痉挛症患者发作期脑电活动局灶改变及其分布优势侧的特征现象,以及与术后疗效的关系.方法:回顾分析了31例TS术前脑电图定位在发作期呈现的局灶、一侧多灶,或双侧均有病灶而以一侧占优势的脑电图现象,并结合术后随访结果对比分析其差异.结果:局灶性放电(单灶)6例,一侧多灶14例,双侧改变以一侧占优势的11例.经统计学处理,局灶与一侧多灶比较差异有显著意义(P<0.05),与两侧均有以一侧占优势相比差异有极显著意义(P<0.01),结论:TS所致婴儿痉挛症患者间歇期表现为多灶性弥散放电甚至高度失律,但发作期起源灶相对明确,呈现局灶、一侧多灶或者两侧均有以一侧占优势,给外科干预提供有力的依据,并获得良好疗效,尤其对于发作期局灶改变者效果尤好.%Objective: To investigate the characteristics of ictal EEG and correlation between outcomes after surgery and local or lateralization in ictal EEG of tuberous sclerosis with infantile spasms . Methods: The ictal EEG of 31 pediatric TS patienets with infantile spasms were reviewed. Comparative analysis of local,multiple lobes of unilateral hemisphere and lateralization of epileptiform discharges in the ictal EEG. Was made. Results:The reseach showed single locus in 6 patients, multiple loci of unilateral hemisphere in 14 cases and lateralization of epileptiform discharges in 11 cases. Through statistic analysis, there was difference (P<0. 05) between the local groups and multiple lobes of unilateral hemisphere groups, There were obvious difference (P<0. 01) between the local groups and lateralization groups. Conclusion; General discharge or hypsarrhythmia is showed in interictal EEG of tuberous sclerosis with infantile spasms , but the ictal EEG is relatively optimistic. Epilepsy surgery in tuberous sclerosis with infantile spasms shows a favourable outcome, especially of

  19. 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.

  20. EEG Power Spectrum Analysis in Children with ADHD

    Science.gov (United States)

    Kamida, Akira; Shimabayashi, Kenta; Oguri, Masayoshi; Takamori, Toshihiro; Ueda, Naoyuki; Koyanagi, Yuki; Sannomiya, Naoko; Nagira, Haruki; Ikunishi, Saeko; Hattori, Yuiko; Sato, Kengo; Fukuda, Chisako; Hirooka, Yasuaki; Maegaki, Yoshihiro

    2016-01-01

    Background Attention deficit disorder/hyperactivity disorder (ADHD) is a pathological condition that is not fully understood. In this study, we investigated electroencephalographic (EEG) power differences between children with ADHD and healthy control children. Methods EEGs were recorded as part of routine medical care received by 80 children with ADHD aged 4–15 years at the Department of Pediatric Neurology in Tottori University Hospital. Additionally, we recorded in 59 control children aged 4–15 years after obtaining informed consent. Specifically, awake EEG signals were recorded from each child using the international 10–20 system, and we used ten 3-s epochs on the EEG power spectrum to calculate the powers of individual EEG frequency bands. Results The powers of different EEG bands were significantly higher in the frontal brain region of those in the ADHD group compared with the control group. In addition, the power of the beta band in the ADHD group was significantly higher in all brain regions, except for the occipital region, compared with control children. With regard to developmental changes, the power of the alpha band in the occipital region showed an age-dependent decrease in both groups, with slightly lower power in the ADHD group. Additionally, the intergroup difference decreased in children aged 11 years or older. As with the alpha band in the occipital region, the beta band in the frontal region showed an age-dependent decrease in both groups. Unlike the alpha band, the power of the beta band was higher in the ADHD group than in the control group for children of all ages. Conclusion The observed intergroup differences in EEG power may provide insight into the brain function of children with ADHD. PMID:27493489

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

    Directory of Open Access Journals (Sweden)

    Haneul Lee

    2011-01-01

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

  2. Music effects on EEG in intrusive and withdrawn mothers with depressive symptoms.

    Science.gov (United States)

    Tornek, Alexandra; Field, Tiffany; Hernandez-Reif, Maria; Diego, Miguel; Jones, Nancy

    2003-01-01

    The EEG patterns of 48 intrusive and withdrawn mothers with depressive symptoms were assessed following a 20-minute music session to determine if the music had mood-altering effects. Half the mothers listened to classical music while half listened to rock music. Intrusive mothers were expected to have more positive responses and more symmetrical EEG following the calming classical music, while withdrawn mothers were expected to have a more positive response and symmetrical EEG following the energizing rock music. Although more positive EEGs were noted for rock music in both groups, only the withdrawn mothers showed a significant change in EEG toward symmetry following rock music, and only the intrusive mothers showed a decrease in cortisol levels following the rock music. Their State Anxiety Inventory (STAI) anxiety levels also decreased, while the Profile of Mood States (POMS) depressed mood levels decreased significantly for all four groups following music.

  3. Mental EEG Analysis Based on Infomax Algorithm

    Institute of Scientific and Technical Information of China (English)

    WUXiao-pei; GuoXiao-jing; ZANGDao-xin; SHENQian

    2004-01-01

    The patterns of EEG will change with mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental tasks has the significant scientific meaning and great application value. But for the reasons of different artifacts existing in EEG, the pattern detection of EEG under normal mental states is a very difficult problem. In this paper, Independent Component Analysisis applied to EEG signals collected from performing different mental tasks. The experiment results show that when one subject performs a single mental task in different trials, the independent components of EEG are very similar. It means that the independent components can be used as the mental EEG patterns to classify the different mental tasks.

  4. Control groups in paediatric epilepsy research: do first-degree cousins show familial effects?

    Science.gov (United States)

    Hanson, Melissa; Morrison, Blaise; Jones, Jana E; Jackson, Daren C; Almane, Dace; Seidenberg, Michael; Zhao, Qianqian; Rathouz, Paul J; Hermann, Bruce P

    2017-03-01

    To determine whether first-degree cousins of children with idiopathic focal and genetic generalized epilepsies show any association across measures of cognition, behaviour, and brain structure. The presence/absence of associations addresses the question of whether and to what extent first-degree cousins may serve as unbiased controls in research addressing the cognitive, psychiatric, and neuroimaging features of paediatric epilepsies. Participants were children (aged 8-18) with epilepsy who had at least one first-degree cousin control enrolled in the study (n=37) and all enrolled cousin controls (n=100). Participants underwent neuropsychological assessment and brain imaging (cortical, subcortical, and cerebellar volumes), and parents completed the Child Behaviour Checklist (CBCL). Data (based on 42 outcome measures) from cousin controls were regressed on the corresponding epilepsy cognitive, behavioural, and imaging measures in a linear mixed model and case/control correlations were examined. Of the 42 uncorrected correlations involving cognitive, behavioural, and neuroimaging measures, only two were significant (p0.25). Similar results held for the cognition/behaviour and brain imaging measures separately. Given the lack of association between cases and first-degree cousin performances on measures of cognition, behaviour, and neuroimaging, the results suggest a non-significant genetic influence on control group performance. First-degree cousins appear to be unbiased controls for cognitive, behavioural, and neuroimaging research in paediatric epilepsy.

  5. Technical and clinical analysis of microEEG: a miniature wireless EEG device designed to record high-quality EEG in the emergency department.

    Science.gov (United States)

    Omurtag, Ahmet; Baki, Samah G Abdel; Chari, Geetha; Cracco, Roger Q; Zehtabchi, Shahriar; Fenton, André A; Grant, Arthur C

    2012-09-24

    We describe and characterize the performance of microEEG compared to that of a commercially available and widely used clinical EEG machine. microEEG is a portable, battery-operated, wireless EEG device, developed by Bio-Signal Group to overcome the obstacles to routine use of EEG in emergency departments (EDs). The microEEG was used to obtain EEGs from healthy volunteers in the EEG laboratory and ED. The standard system was used to obtain EEGs from healthy volunteers in the EEG laboratory, and studies recorded from patients in the ED or ICU were also used for comparison. In one experiment, a signal splitter was used to record simultaneous microEEG and standard EEG from the same electrodes. EEG signal analysis techniques indicated good agreement between microEEG and the standard system in 66 EEGs recorded in the EEG laboratory and the ED. In the simultaneous recording the microEEG and standard system signals differed only in a smaller amount of 60 Hz noise in the microEEG signal. In a blinded review by a board-certified clinical neurophysiologist, differences in technical quality or interpretability were insignificant between standard recordings in the EEG laboratory and microEEG recordings from standard or electrode cap electrodes in the ED or EEG laboratory. The microEEG data recording characteristics such as analog-to-digital conversion resolution (16 bits), input impedance (>100MΩ), and common-mode rejection ratio (85 dB) are similar to those of commercially available systems, although the microEEG is many times smaller (88 g and 9.4 × 4.4 × 3.8 cm). Our results suggest that the technical qualities of microEEG are non-inferior to a standard commercially available EEG recording device. EEG in the ED is an unmet medical need due to space and time constraints, high levels of ambient electrical noise, and the cost of 24/7 EEG technologist availability. This study suggests that using microEEG with an electrode cap that can be applied easily and quickly can

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

    Science.gov (United States)

    Bhattacharya, Joydeep; Lee, Eun-Jeong

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

  7. Quantitative electroencephalography (qEEG to discriminate primary degenerative dementia from major depressive disorder (depression

    Directory of Open Access Journals (Sweden)

    Deslandes Andréa

    2004-01-01

    Full Text Available Electroencephalography (EEG can be a valuable technique to assess electrophysiological changes related to dementia. In patients suspected of having dementia, the EEG is often quite informative. The sensitivity of the EEG to detect correlates of psychiatric disorders has been enhanced by means of quantitative methods of analysis (quantitative EEG. Quantitative features are extracted from, at least, 2 minutes of artifact-free, eyes closed, resting EEG, log-transformed to obtain Gaussianity, age-regressed, and Z-transformed relative to population norms (Neurometrics database. Using a subset of quantitative EEG (qEEG features, forward stepwise discriminant analyses are used to construct classifier functions. Along this vein, the main objective of this experiment is to distinguish profiles of qEEG, which differentiate depressive from demented patients (n = 125. The results showed that demented patients present deviations above the control group in variables associated to slow rhythms: Normed Monopolar Relative Power Theta for Cz and Normed Bipolar Relative Power Theta for Head. On the other hand, the deviation below the control group occurs with the variable associated to alpha rhythm: Normed Monopolar Relative Power Alpha for P3, in dementia. Using this method, the present investigation demonstrated high discriminant accuracy in separating Primary Degenerative Dementia from Major Depressive Disorder (Depression.

  8. Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study.

    Science.gov (United States)

    Yu, Qingbao; Wu, Lei; Bridwell, David A; Erhardt, Erik B; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D

    2016-01-01

    The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.

  9. Building an EEG-fMRI multi-modal brain graph: a concurrent EEG-fMRI study

    Directory of Open Access Journals (Sweden)

    Qingbao Yu

    2016-09-01

    Full Text Available The topological architecture of brain connectivity has been well characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO and eyes closed (EC resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA. EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma. EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.

  10. Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study

    Science.gov (United States)

    Yu, Qingbao; Wu, Lei; Bridwell, David A.; Erhardt, Erik B.; Du, Yuhui; He, Hao; Chen, Jiayu; Liu, Peng; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.

    2016-01-01

    The topological architecture of brain connectivity has been well-characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EO) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics. PMID:27733821

  11. Are There Any Specific EEG Findings in Autoimmune Epilepsies?

    Science.gov (United States)

    Baysal-Kirac, Leyla; Tuzun, Erdem; Altindag, Ebru; Ekizoglu, Esme; Kinay, Demet; Bilgic, Basar; Tekturk, Pinar; Baykan, Betul

    2016-07-01

    This study evaluated the EEG findings of patients whose seizures were associated with a possible autoimmune etiology. Our aim was to find clues to distinguish patients with antineuronal antibodies (Ab) through EEG studies. We reviewed our database and identified antineuronal Ab positive epilepsy patients with or without autoimmune encephalitis. These patients had Abs to N-methyl-d-aspartate receptor (NMDAR) (n = 5), glycine receptor (GLY-R) (n = 5), contactin-associated protein-like 2 (CASPR-2) (n = 4), uncharacterized voltage-gated potassium channel complex (VGKC) antigens (n = 2), glutamic acid decarboxylase (GAD) (n = 2), Hu (n = 1), and amphiphysin (n = 1). The control group consisted of 21 seronegative epilepsy or encephalopathy patients with similar clinical features. EEG findings were compared between the groups in a blindfolded design. We did not find any significant difference in EEG findings between antineuronal Ab positive epilepsy patients and seronegative control group. It was remarkable that four seropositive but none of the seronegative patients presented with nonconvulsive status epilepticus (NCSE) or focal motor status epilepticus. Continuous theta and delta rhythms were observed in 5 (71%) seropositive patients with autoimmune encephalitis and 2 (25%) seronegative patients. Eight (40 %) seropositive patients showed a frontal intermittent rhythmic delta activity (FIRDA) pattern as opposed to 5 (24%) seronegative patients. Two patients with NMDAR Ab positivity showed rhythmic delta waves superimposed with beta frequency activity resembling "delta brush" pattern. EEG seems as a limited diagnostic tool in differentiating epilepsy and/or encephalopathy patients with a possible autoimmune etiology from those without. However, antineuronal Abs associated with encephalitis should be considered in the etiology of status epilepticus forms. A possible autoimmune etiology for seizures may be considered in the presence of continuous slow waves, FIRDA, and

  12. 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.

  13. 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.

  14. Disturbance gradient shows logging affects plant functional groups more than fire.

    Science.gov (United States)

    Blair, David P; McBurney, Lachlan M; Blanchard, Wade; Banks, Sam C; Lindenmayer, David B

    2016-10-01

    Understanding the impacts of natural and human disturbances on forest biota is critical for improving forest management. Many studies have examined the separate impacts on fauna and flora of wildfire, conventional logging, and salvage logging, but empirical comparisons across a broad gradient of simultaneous disturbances are lacking. We quantified species richness and frequency of occurrence of vascular plants, and functional group responses, across a gradient of disturbances that occurred concurrently in 2009 in the mountain ash forests of southeastern Australia. Our study encompassed replicated sites in undisturbed forest (~70 yr post fire), forest burned at low severity, forest burned at high severity, unburned forest that was clearcut logged, and forest burned at high severity that was clearcut salvage logged post-fire. All sites were sampled 2 and 3 yr post fire. Mean species richness decreased across the disturbance gradient from 30.1 species/site on low-severity burned sites and 28.9 species/site on high-severity burned sites, to 25.1 species/site on clearcut sites and 21.7 species/site on salvage logged sites. Low-severity burned sites were significantly more species-rich than clearcut sites and salvage logged sites; high-severity burned sites supported greater species richness than salvage logged sites. Specific traits influenced species' sensitivity to disturbance. Resprouting species dominated undisturbed mountain ash forests, but declined significantly across the gradient. Fern and midstory trees decreased significantly in frequency of occurrence across the gradient. Ferns (excluding bracken) decreased from 34% of plants in undisturbed forest to 3% on salvage logged sites. High-severity burned sites supported a greater frequency of occurrence and species richness of midstory trees compared to clearcut and salvage logged sites. Salvage logging supported fewer midstory trees than any other disturbance category, and were distinctly different from

  15. Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection

    Directory of Open Access Journals (Sweden)

    Jia Gao

    2016-01-01

    Full Text Available This paper investigated the interregional correlation changed by sport training through electroencephalography (EEG signals using the techniques of classification and feature selection. The EEG data are obtained from students with long-time professional sport training and normal students without sport training as baseline. Every channel of the 19-channel EEG signals is considered as a node in the brain network and Pearson Correlation Coefficients are calculated between every two nodes as the new features of EEG signals. Then, the Partial Least Square (PLS is used to select the top 10 most varied features and Pearson Correlation Coefficients of selected features are compared to show the difference of two groups. Result shows that the classification accuracy of two groups is improved from 88.13% by the method using measurement of EEG overall energy to 97.19% by the method using EEG correlation measurement. Furthermore, the features selected reveal that the most important interregional EEG correlation changed by training is the correlation between left inferior frontal and left middle temporal with a decreased value.

  16. 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.

  17. 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 ...

  18. 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 ...

  19. Large-Scale Functional Networks Identified from Resting-State EEG Using Spatial ICA.

    Directory of Open Access Journals (Sweden)

    Stéphane Sockeel

    Full Text Available Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1 An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2 A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.

  20. Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

    Science.gov (United States)

    Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

    2017-09-08

    EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

  1. 40-Hz EEG activity during hypnotic induction and hypnotic testing.

    Science.gov (United States)

    DePascalis, V; Penna, P M

    1990-04-01

    The present study evaluates changes in left and right 40-Hz EEG production for 19 high and 20 low hypnotizable female Ss during the hypnotic induction and the administration of the Stanford Hypnotic Susceptibility Scale, Form C (SHSS:C) of the Weitzenhoffer and Hilgard (1962). Scalp recorded 40-Hz EEG density was obtained from the middle of the O1-P3-T5 and O2-P4-T6 triangles. As the hypnotic induction proceeded, high hypnotizable Ss exhibited a shift to greater right-hemisphere activity as compared to a waking-state rest condition. In contrast, low hypnotizable Ss, showed a reduction in left- and right-hemisphere activity. No differences between groups for SHSS:C ideomotor items were observed. A main effect for Hypnotizability among SHSS:C imaginative items was found. A Hypnotizability x Hemisphere x Trial interaction was found for both sensory distortion and imaginative SHSS:C items. A comparison was made between low versus high hypnotizable Ss of 40-Hz EEG activity while they passed the same item. The results of these comparisons indicate that differences in brain activity might be partially related to the differences between experiencing a hypnotic suggestion or failing to do so. Significant relationships between 40-Hz EEG production and hypnotizability and 40-Hz EEG production and level of amnesia were also found.

  2. Studying the default mode and its mindfulness-induced changes using EEG functional connectivity.

    Science.gov (United States)

    Berkovich-Ohana, Aviva; Glicksohn, Joseph; Goldstein, Abraham

    2014-10-01

    The default mode network (DMN) has been largely studied by imaging, but not yet by neurodynamics, using electroencephalography (EEG) functional connectivity (FC). mindfulness meditation (MM), a receptive, non-elaborative training is theorized to lower DMN activity. We explored: (i) the usefulness of EEG-FC for investigating the DMN and (ii) the MM-induced EEG-FC effects. To this end, three MM groups were compared with controls, employing EEG-FC (-MPC, mean phase coherence). Our results show that: (i) DMN activity was identified as reduced overall inter-hemispheric gamma MPC during the transition from resting state to a time production task and (ii) MM-induced a state increase in alpha MPC as well as a trait decrease in EEG-FC. The MM-induced EEG-FC decrease was irrespective of expertise or band. Specifically, there was a relative reduction in right theta MPC, and left alpha and gamma MPC. The left gamma MPC was negatively correlated with MM expertise, possibly related to lower internal verbalization. The trait lower gamma MPC supports the notion of MM-induced reduction in DMN activity, related with self-reference and mind-wandering. This report emphasizes the possibility of studying the DMN using EEG-FC as well as the importance of studying meditation in relation to it.

  3. EEG, alpha waves and coherence

    Science.gov (United States)

    Ascolani, Gianluca

    This thesis addresses some theoretical issues generated by the results of recent analysis of EEG time series proving the brain dynamics are driven by abrupt changes making them depart from the ordinary Poisson condition. These changes are renewal, unpredictable and non-ergodic. We refer to them as crucial events. How is it possible that this form of randomness be compatible with the generation of waves, for instance alpha waves, whose observation seems to suggest the opposite view the brain is characterized by surprisingly extended coherence? To shed light into this apparently irretrievable contradiction we propose a model based on a generalized form of Langevin equation under the influence of a periodic stimulus. We assume that there exist two different forms of time, a subjective form compatible with Poisson statistical physical and an objective form that is accessible to experimental observation. The transition from the former to the latter form is determined by the brain dynamics interpreted as emerging from the cooperative interaction among many units that, in the absence of cooperation would generate Poisson fluctuations. We call natural time the brain internal time and we make the assumption that in the natural time representation the time evolution of the EEG variable y(t) is determined by a Langevin equation perturbed by a periodic process that in this time representation is hardly distinguishable from an erratic process. We show that the representation of this random process in the experimental time scale is characterized by a surprisingly extended coherence. We show that this model generates a sequence of damped oscillations with a time behavior that is remarkably similar to that derived from the analysis of real EEG's. The main result of this research work is that the existence of crucial events is not incompatible with the alpha wave coherence. In addition to this important result, we find another result that may help our group, or any other research

  4. Ictal EEG modifications in temporal lobe epilepsy.

    Science.gov (United States)

    Pelliccia, Veronica; Mai, Roberto; Francione, Stefano; Gozzo, Francesca; Sartori, Ivana; Nobili, Lino; Lo Russo, Giorgio; Pizzanelli, Chiara; Tassi, Laura

    2013-12-01

    Temporal lobe epilepsy is the most common type of epilepsy in adults with medically intractable, localisation-related epilepsy, amenable to surgery. Together with clinical and neuroimaging data, presurgical ictal scalp-EEG findings are often sufficient to define the epileptogenic zone. It is widely believed that ictal scalp-EEG findings in temporal lobe epilepsy are represented by 5-9-Hz lateralised rhythmic theta activity or 2-5-Hz lateralised rhythmic delta activity. On the basis of experimental models and experience with intra-cerebral EEG recordings, the pattern of low-voltage fast activity is considered to be the electrophysiological hallmark of the epileptogenic zone. We reviewed the ictal scalp-EEG data relating to 111 seizures in 47 patients with temporal lobe epilepsy who underwent video-EEG recordings during presurgical work-up. We found that 35 patients (74.4%) showed flattening, low-voltage fast activity or fast activity as the initial EEG pattern. When visible, the rhythmic delta or theta activity followed the fast activity. Low-voltage fast activity, flattening or fast activity occurs in the majority of patients with temporal lobe epilepsy and represents the main ictal EEG pattern. Low-voltage fast activity (or similar) is also identifiable as the initial ictal EEG pattern in scalp-EEG recordings.

  5. EXPERIMENTAL STUDY ON THE EFFECT OF ACUPOINT-CATGUT-EMBEDDING ON EEG OF EPILEPSY RATS

    Institute of Scientific and Technical Information of China (English)

    彭尧书; 杨运高

    2003-01-01

    Aim: To observe the therapeutic effect of acupoint-catgut-embedding in the treatment of epilepsy rats. Methods: 40 Wistar rats were randomly and evenly divided into normal control, model, medication (Natrii Valproas syrup), and catgut-embedding groups. Epilepsy model was established by intraperitoneal injection of sodium penicillin (0.4 mu/100 g), twice every week, continuously for 4 weeks. Electroencephalograph (EEG) was used as the index for assessing the effect of catgut-embedding on epilepsy. Results: In epilepsy rats of model group, the amplitude and frequency of EEG increased significantly in comparison with control group (P<0.05). While the amplitude and frequency of EEG of catgut-embedding group and medication group were all significantly lower than those of model group (P<0.05, 0.01). The frequency of EEG of catgut-embedding group was significantly lower than that of medication group (P<0.05). These findings show that both acupoint-catgut-embedding and medication can apparently improve epileptic electrical activities of the brain in the rat, and the effect of catgut-embedding is better than that of medication in lowering the frequency of EEG.

  6. Nonlinear Analysis of Clinical Epileptic EEG by Approximate Entropy

    Institute of Scientific and Technical Information of China (English)

    LIU Yan-su; XIA Yang; XU Hong-ru; ZHOU Dong; YAO De-zhong

    2005-01-01

    By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0. 002). Meanwhile, dynamic ApEn shows consistent change of EEG signal withdischarges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism.

  7. 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...

  8. 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...

  9. 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

  10. 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

  11. [Application of SVM and wavelet analysis in EEG classification].

    Science.gov (United States)

    Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei

    2011-04-01

    We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.

  12. 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.

  13. 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.

  14. Engagement Assessment Using EEG Signals

    Science.gov (United States)

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

    2012-01-01

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

  15. 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.

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

    Science.gov (United States)

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

    2015-01-22

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

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

    Directory of Open Access Journals (Sweden)

    Samantha J Broyd

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

  18. Subject position affects EEG magnitudes.

    Science.gov (United States)

    Rice, Justin K; Rorden, Christopher; Little, Jessica S; Parra, Lucas C

    2013-01-01

    EEG (electroencephalography) has been used for decades in thousands of research studies and is today a routine clinical tool despite the small magnitude of measured scalp potentials. It is widely accepted that the currents originating in the brain are strongly influenced by the high resistivity of skull bone, but it is less well known that the thin layer of CSF (cerebrospinal fluid) has perhaps an even more important effect on EEG scalp magnitude by spatially blurring the signals. Here it is shown that brain shift and the resulting small changes in CSF layer thickness, induced by changing the subject's position, have a significant effect on EEG signal magnitudes in several standard visual paradigms. For spatially incoherent high-frequency activity the effect produced by switching from prone to supine can be dramatic, increasing occipital signal power by several times for some subjects (on average 80%). MRI measurements showed that the occipital CSF layer between the brain and skull decreases by approximately 30% in thickness when a subject moves from prone to supine position. A multiple dipole model demonstrated that this can indeed lead to occipital EEG signal power increases in the same direction and order of magnitude as those observed here. These results suggest that future EEG studies should control for subjects' posture, and that some studies may consider placing their subjects into the most favorable position for the experiment. These findings also imply that special consideration should be given to EEG measurements from subjects with brain atrophy due to normal aging or neurodegenerative diseases, since the resulting increase in CSF layer thickness could profoundly decrease scalp potential measurements.

  19. Tl(+) showed negligible interaction with inner membrane sulfhydryl groups of rat liver mitochondria, but formed complexes with matrix proteins.

    Science.gov (United States)

    Korotkov, Sergey M; Brailovskaya, Irina V; Kormilitsyn, Boris N; Furaev, Viktor V

    2014-04-01

    The effects of Tl(+) on protein sulfhydryl (SH) groups, swelling, and respiration of rat liver mitochondria (RLM) were studied in a medium containing TlNO3 and sucrose, or TlNO3 and KNO3 as well as glutamate plus malate, or succinate plus rotenone. Detected with Ellman's reagent, an increase in the content of the SH groups was found in the inner membrane fraction, and a simultaneous decline was found in the content of the matrix-soluble fraction for RLM, incubated and frozen in 25-75 mM TlNO3 . This increase was greater in the medium containing KNO3 regardless of the presence of Ca(2+) . It was eliminated completely for RLM injected in the medium containing TlNO3 and then washed and frozen in the medium containing KNO3 . Calcium-loaded RLM showed increased swelling and decreased respiration. These results suggest that a ligand interaction of Tl(+) with protein SH groups, regardless of the presence of calcium, may underlie the mechanism of thallium toxicity.

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

  2. EEG in Sarcoidosis Patients Without Neurological Findings.

    Science.gov (United States)

    Bilgin Topçuoğlu, Özgür; Kavas, Murat; Öztaş, Selahattin; Arınç, Sibel; Afşar, Gülgün; Saraç, Sema; Midi, İpek

    2017-01-01

    Sarcoidosis is a multisystem granulomatous disease affecting nervous system in 5% to 10% of patients. Magnetic resonance imaging (MRI) is accepted as the most sensitive method for detecting neurosarcoidosis. However, the most common findings in MRI are the nonspecific white matter lesions, which may be unrelated to sarcoidosis and can occur because of hypertension, diabetes mellitus, smoking, and other inflammatory or infectious disorders, as well. Autopsy studies report more frequent neurological involvement than the ante mortem studies. The aim of this study is to assess electroencephalography (EEG) in sarcoidosis patients without neurological findings in order to display asymptomatic neurological dysfunction. We performed EEG on 30 sarcoidosis patients without diagnosis of neurosarcoidosis or prior neurological comorbidities. Fourteen patients (46.7%) showed intermittant focal and/or generalized slowings while awake and not mentally activated. Seven (50%) of these 14 patients with EEG slowings had nonspecific white matter changes while the other half showed EEG slowings in the absence of MRI changes. We conclude that EEG slowings, when normal variants (psychomotor variant, temporal theta of elderly, frontal theta waves) are eliminated, may be an indicator of dysfunction in brain activity even in the absence of MRI findings. Hence, EEG may contribute toward detecting asymptomatic neurological dysfunction or probable future neurological involvement in sarcoidosis patients. © EEG and Clinical Neuroscience Society (ECNS) 2016.

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

    Science.gov (United States)

    Khaburzania, M; Beridze, M

    2013-06-01

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

  4. EEG and Coma.

    Science.gov (United States)

    Ardeshna, Nikesh I

    2016-03-01

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

  5. THETA AND ALPHA EEG FREQUENCY INTERPLAY IN SUBJECTS WITH MILD COGNITIVE IMPAIRMENT: EVIDENCE FROM EEG, MRI AND SPECT BRAIN MODIFICATIONS

    Directory of Open Access Journals (Sweden)

    Davide Vito Moretti

    2015-03-01

    Full Text Available Background: reduction of regional cerebral perfusion in hippocampus as well as temporo-parietal and medial temporal cortex atrophy are associated to mild cognitive impairment (MCI due to Alzheimer disease (AD. Methods: 74 adult subjects with MCI underwent clinical and neuropsychological evaluation, electroencephalogram (EEG recording and high resolution 3D magnetic resonance imaging (MRI. Among the patients, a subset of 27 subjects underwent also perfusion single-photon emission computed tomography (SPECT and hippocampal atrophy evaluation. Alpha3/alpha2 power ratio as well as cortical thickness was computed for each subject. Three MCI groups were detected according to increasing tertile values of alpha3/alpha2 power ratio and difference of cortical thickness among the groups estimated. Results: higher alpha3/alpha2 power ratio group had wider cortical thinning than other groups, mapped to the Supramarginal and Precuneus bilaterally. Subjects with higher alpha3/alpha2 frequency power ratio showed a constant trend to a lower perfusion than lower alpha3/alpha2 group. Moreover, this group correlates with both a bigger hippocampal atrophy and an increase of theta frequency power.Conclusion: Higher EEG alpha3/alpha2 power ratio was associated with temporo-parietal cortical thinning, hippocampal atrophy and reduction of regional cerebral perfusion in medial temporal cortex. In this group an increase of theta frequency power was detected inMCI subjects. The combination of higher EEG alpha3/alpha2 power ratio, cortical thickness measure and regional cerebral perfusion reveals a complex interplay between EEG cerebral rhythms, structural and functional brain modifications.

  6. Target Speaker Detection with Concealed EEG Around the Ear

    Science.gov (United States)

    Mirkovic, Bojana; Bleichner, Martin G.; De Vos, Maarten; Debener, Stefan

    2016-01-01

    Target speaker identification is essential for speech enhancement algorithms in assistive devices aimed toward helping the hearing impaired. Several recent studies have reported that target speaker identification is possible through electroencephalography (EEG) recordings. If the EEG system could be reduced to acceptable size while retaining the signal quality, hearing aids could benefit from the integration with concealed EEG. To compare the performance of a multichannel around-the-ear EEG system with high-density cap EEG recordings an envelope tracking algorithm was applied in a competitive speaker paradigm. The data from 20 normal hearing listeners were concurrently collected from the traditional state-of-the-art laboratory wired EEG system and a wireless mobile EEG system with two bilaterally-placed around-the-ear electrode arrays (cEEGrids). The results show that the cEEGrid ear-EEG technology captured neural signals that allowed the identification of the attended speaker above chance-level, with 69.3% accuracy, while cap-EEG signals resulted in the accuracy of 84.8%. Further analyses investigated the influence of ear-EEG signal quality and revealed that the envelope tracking procedure was unaffected by variability in channel impedances. We conclude that the quality of concealed ear-EEG recordings as acquired with the cEEGrid array has potential to be used in the brain-computer interface steering of hearing aids. PMID:27512364

  7. 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.

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

    Directory of Open Access Journals (Sweden)

    Lee TS

    2015-01-01

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

  9. Quantitative EEG and medial temporal lobe atrophy in Alzheimer′s dementia: Preliminary study

    Directory of Open Access Journals (Sweden)

    Soo-Ji Lee

    2015-01-01

    Full Text Available Backgrounds: The electroencephalogram (EEG abnormalities in Alzheimer′s disease (AD have been widely reported, and medial temporal lobe atrophy (MTLA is one of the hallmarks in early stage of AD. We aimed to assess the relationship between EEG abnormalities and MTLA and its clinical validity. Materials and Methods: A total of 18 patients with AD were recruited (the mean age: 77.83 years. Baseline EEGs were analyzed with quantitative spectral analysis. MTLA was assessed by a T1-axial visual rating scale (VRS. Results: In relative power spectrum analysis according to the right MTLA severity, the power of theta waves in C4, T4, F4, F8, and T5 increased significantly and the power of beta waves in T6, C4, T4, F8, T5, P3, T3, and F7 decreased significantly in severe atrophy group. In relative power spectrum analysis according to the left MTLA severity, the power of theta waves in T3 increased significantly and that of beta waves in P4, T6, C4, F4, F8, T5, P3, C3, T3, F3, and F7 decreased significantly in severe atrophy group. Conclusion: The severe MTLA group, regardless of laterality, showed more severe quantitative EEG alterations. These results suggest that quantitative EEG abnormalities are correlated with the MTLA, which may play an important role in AD process.

  10. Replications of Two Closely Related Groups of Jumbo Phages Show Different Level of Dependence on Host-encoded RNA Polymerase

    Science.gov (United States)

    Matsui, Takeru; Yoshikawa, Genki; Mihara, Tomoko; Chatchawankanphanich, Orawan; Kawasaki, Takeru; Nakano, Miyako; Fujie, Makoto; Ogata, Hiroyuki; Yamada, Takashi

    2017-01-01

    Ralstonia solanacearum phages ΦRP12 and ΦRP31 are jumbo phages isolated in Thailand. Here we show that they exhibit similar virion morphology, genome organization and host range. Genome comparisons as well as phylogenetic and proteomic tree analyses support that they belong to the group of ΦKZ-related phages, with their closest relatives being R. solanacearum phages ΦRSL2 and ΦRSF1. Compared with ΦRSL2 and ΦRSF1, ΦRP12 and ΦRP31 possess larger genomes (ca. 280 kbp, 25% larger). The replication of ΦRP12 and ΦRP31 was not affected by rifampicin treatment (20 μg/ml), suggesting that phage-encoded RNAPs function to start and complete the infection cycle of these phages without the need of host-encoded RNAPs. In contrast, ΦRSL2 and ΦRSF1, encoding the same set of RNAPs, did not produce progeny phages in the presence of rifampicin (5 μg/ml). This observation opens the possibility that some ΦRP12/ΦRP31 factors that are absent in ΦRSL2 and ΦRSF1 are involved in their host-independent transcription. PMID:28659872

  11. EEG: Origin and measurement

    NARCIS (Netherlands)

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

    2010-01-01

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

  12. 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

  13. 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

  14. What can be found in scalp EEG spectrum beyond common frequency bands. EEG-fMRI study

    Science.gov (United States)

    Marecek, R.; Lamos, M.; Mikl, M.; Barton, M.; Fajkus, J.; I, Rektor; Brazdil, M.

    2016-08-01

    Objective. The scalp EEG spectrum is a frequently used marker of neural activity. Commonly, the preprocessing of EEG utilizes constraints, e.g. dealing with a predefined subset of electrodes or a predefined frequency band of interest. Such treatment of the EEG spectrum neglects the fact that particular neural processes may be reflected in several frequency bands and/or several electrodes concurrently, and can overlook the complexity of the structure of the EEG spectrum. Approach. We showed that the EEG spectrum structure can be described by parallel factor analysis (PARAFAC), a method which blindly uncovers the spatial-temporal-spectral patterns of EEG. We used an algorithm based on variational Bayesian statistics to reveal nine patterns from the EEG of 38 healthy subjects, acquired during a semantic decision task. The patterns reflected neural activity synchronized across theta, alpha, beta and gamma bands and spread over many electrodes, as well as various EEG artifacts. Main results. Specifically, one of the patterns showed significant correlation with the stimuli timing. The correlation was higher when compared to commonly used models of neural activity (power fluctuations in distinct frequency band averaged across a subset of electrodes) and we found significantly correlated hemodynamic fluctuations in simultaneously acquired fMRI data in regions known to be involved in speech processing. Further, we show that the pattern also occurs in EEG data which were acquired outside the MR machine. Two other patterns reflected brain rhythms linked to the attentional and basal ganglia large scale networks. The other patterns were related to various EEG artifacts. Significance. These results show that PARAFAC blindly identifies neural activity in the EEG spectrum and that it naturally handles the correlations among frequency bands and electrodes. We conclude that PARAFAC seems to be a powerful tool for analysis of the EEG spectrum and might bring novel insight to the

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

    Science.gov (United States)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-01-01

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

  17. 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...

  18. Action of glycosyl transferases upon "Bombay" (Oh) erythrocytes. Conversion to cells showing blood-group H and A specificities.

    Science.gov (United States)

    Schenkel-Brunner, H; Prohaska, R; Tuppy, H

    1975-08-15

    Individuals of the rare "Bombay" (Oh) blood-group phenotype lacking, due to a genetic defect, the alpha(1-2)fucosyl transferase, which is responsible for converting blood-group H precursor substances to H-specific structures. Treatment with GDP-fucose and alpha(1-2)fucosyl transferase prepared from gastric mucosa of O individuals to transform native or ficin-treated "Bombay" erythrocytes into cells phenotypically resembling O cells. The transformation was achieved, however, after prior incubation of the "Bombay" erythrocytes with neuraminidase, indicating that blood-group H precursor molecules on the surface of these cells are masked by sialyl residues. Blood-group A specificity was conferred upon neuraminidase-treated "Bombay" cells by enzymatic transfer of alpha-N-acetylgalactosamine residues, in addition to alpha-fucose residues.

  19. The Etiology and Outcome Analysis of Neonatal Burst Suppression EEG

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lian; ZHOU Yanxia; XU Sanqing

    2007-01-01

    The neonatal burst suppression is a severe EEG pattern and always demonstrates serious damage of nerve system. But the outcome of these patients depends on the different etiology. A total of 256 cases of video EEG recordings were analyzed in order to summarize the etiology and outcome of burst suppression. The results showed that some patients in all 17 cases of burst suppression showed EEG improvement. The etiology was the dominant factor in long term outcome. It was sug-gested that effective video EEG monitoring is helpful for etiologic study and prognosis evaluation.

  20. A test of the intergenerational conflict model in Indonesia shows no evidence of earlier menopause in female-dispersing groups.

    Science.gov (United States)

    Snopkowski, Kristin; Moya, Cristina; Sear, Rebecca

    2014-08-07

    Menopause remains an evolutionary puzzle, as humans are unique among primates in having a long post-fertile lifespan. One model proposes that intergenerational conflict in patrilocal populations favours female reproductive cessation. This model predicts that women should experience menopause earlier in groups with an evolutionary history of patrilocality compared with matrilocal groups. Using data from the Indonesia Family Life Survey, we test this model at multiple timescales: deep historical time, comparing age at menopause in ancestrally patrilocal Chinese Indonesians with ancestrally matrilocal Austronesian Indonesians; more recent historical time, comparing age at menopause in ethnic groups with differing postmarital residence within Indonesia and finally, analysing age at menopause at an individual-level, assuming a woman facultatively adjusts her age at menopause based on her postmarital residence. We find a significant effect only at the intermediate timescale where, contrary to predictions, ethnic groups with a history of multilocal postnuptial residence (where couples choose where to live) have the slowest progression to menopause, whereas matrilocal and patrilocal ethnic groups have similar progression rates. Multilocal residence may reduce intergenerational conflicts between women, thus influencing reproductive behaviour, but our results provide no support for the female-dispersal model of intergenerational conflict as an explanation of menopause.

  1. A test of the intergenerational conflict model in Indonesia shows no evidence of earlier menopause in female-dispersing groups

    Science.gov (United States)

    Snopkowski, Kristin; Moya, Cristina; Sear, Rebecca

    2014-01-01

    Menopause remains an evolutionary puzzle, as humans are unique among primates in having a long post-fertile lifespan. One model proposes that intergenerational conflict in patrilocal populations favours female reproductive cessation. This model predicts that women should experience menopause earlier in groups with an evolutionary history of patrilocality compared with matrilocal groups. Using data from the Indonesia Family Life Survey, we test this model at multiple timescales: deep historical time, comparing age at menopause in ancestrally patrilocal Chinese Indonesians with ancestrally matrilocal Austronesian Indonesians; more recent historical time, comparing age at menopause in ethnic groups with differing postmarital residence within Indonesia and finally, analysing age at menopause at an individual-level, assuming a woman facultatively adjusts her age at menopause based on her postmarital residence. We find a significant effect only at the intermediate timescale where, contrary to predictions, ethnic groups with a history of multilocal postnuptial residence (where couples choose where to live) have the slowest progression to menopause, whereas matrilocal and patrilocal ethnic groups have similar progression rates. Multilocal residence may reduce intergenerational conflicts between women, thus influencing reproductive behaviour, but our results provide no support for the female-dispersal model of intergenerational conflict as an explanation of menopause. PMID:24966311

  2. Quantitative EEG analysis of the maturational changes associated with childhood absence epilepsy

    Science.gov (United States)

    Rosso, O. A.; Hyslop, W.; Gerlach, R.; Smith, R. L. L.; Rostas, J. A. P.; Hunter, M.

    2005-10-01

    This study aimed to examine the background electroencephalography (EEG) in children with childhood absence epilepsy, a condition whose presentation has strong developmental links. EEG hallmarks of absence seizure activity are widely accepted and there is recognition that the bulk of inter-ictal EEG in this group is normal to the naked eye. This multidisciplinary study aimed to use the normalized total wavelet entropy (NTWS) (Signal Processing 83 (2003) 1275) to examine the background EEG of those patients demonstrating absence seizure activity, and compare it with children without absence epilepsy. This calculation can be used to define the degree of order in a system, with higher levels of entropy indicating a more disordered (chaotic) system. Results were subjected to further statistical analyses of significance. Entropy values were calculated for patients versus controls. For all channels combined, patients with absence epilepsy showed (statistically significant) lower entropy values than controls. The size of the difference in entropy values was not uniform, with certain EEG electrodes consistently showing greater differences than others.

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

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    Plamen D. Dimitrov

    2017-01-01

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

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

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    Francisco J Fraga

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

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

    Science.gov (United States)

    Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

    2017-03-01

    We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom.

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

    Science.gov (United States)

    Poulsen, Andreas Trier; Kamronn, Simon; Dmochowski, Jacek; Parra, Lucas C.; Hansen, Lars Kai

    2017-01-01

    We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional modulation in earlier studies that used high-grade equipment in laboratory settings. Here we reproduce many of the results from these studies using portable low-cost equipment, focusing on the robustness of using ISC for subjects experiencing naturalistic stimuli. The present data shows that stimulus-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom. PMID:28266588

  7. Ballistic gelatin as a putative substrate for EEG phantom devices

    CERN Document Server

    Hairston, W David; Yu, Alfred B

    2016-01-01

    Phantom devices allow the human variable to be controlled for in order to allow clear comparison and validation of biomedical imaging hardware and software. There is currently no standard phantom for electroencephalography (EEG). To be useful, such a device would need to: (a) accurately recreate the real and imaginary components of scalp electrical impedance, (b) contain internal emitters to create electrical dipoles, and (c) be easily replicable across various labs and research groups. Cost-effective materials, which are conductive, repeatable, and easily formed are a missing key enabler for EEG phantoms. Here, we explore the use of ballistics gelatin, an inexpensive, easily-formable and repeatable material, as a putative substrate by examining its electrical properties and physical stability over time. We show that varied concentrations of NaCl salt relative to gelatin powder shifts the phase/frequency response profile, allowing for selective tuning of the material electrical properties.

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

    Directory of Open Access Journals (Sweden)

    Yehudit Meir-Hasson

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

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

    Science.gov (United States)

    Kinreich, Sivan; Jackont, Gilan; Cohen, Avihay; Podlipsky-Klovatch, Ilana; Hendler, Talma; Intrator, Nathan

    2016-01-01

    Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations. PMID:27163677

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

  12. On the Existence of Synchrostates in Multichannel EEG Signals during Face-perception Tasks

    CERN Document Server

    Jamal, Wasifa; Maharatna, Koushik; Apicella, Fabio; Chronaki, Georgia; Sicca, Federico; Cohen, David; Muratori, Filippo

    2016-01-01

    Phase synchronisation in multichannel EEG is known as the manifestation of functional brain connectivity. Traditional phase synchronisation studies are mostly based on time average synchrony measures hence do not preserve the temporal evolution of the phase difference. Here we propose a new method to show the existence of a small set of unique phase synchronised patterns or "states" in multi-channel EEG recordings, each "state" being stable of the order of ms, from typical and pathological subjects during face perception tasks. The proposed methodology bridges the concepts of EEG microstates and phase synchronisation in time and frequency domain respectively. The analysis is reported for four groups of children including typical, Autism Spectrum Disorder (ASD), low and high anxiety subjects - a total of 44 subjects. In all cases, we observe consistent existence of these states - termed as synchrostates - within specific cognition related frequency bands (beta and gamma bands), though the topographies of these...

  13. 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.

  14. Mindfulness-of-breathing exercise modulates EEG alpha activity during cognitive performance.

    Science.gov (United States)

    Bing-Canar, Hanaan; Pizzuto, Jacquelyne; Compton, Rebecca J

    2016-09-01

    The present study investigated whether engaging in a mindful breathing exercise would affect EEG oscillatory activity associated with self-monitoring processes, based on the notion that mindfulness enhances attentional awareness. Participants were assigned to either an audio exercise in mindful breathing or an audio control condition, and then completed a Stroop task while EEG was recorded. The primary EEG measure of interest was error-related alpha suppression (ERAS), an index of self-monitoring in which alpha power is reduced, suggesting mental engagement, following errors compared to correct responses. Participants in the mindful-breathing condition showed increased alpha power during the listening exercise and enhanced ERAS during the subsequent Stroop task. These results indicate enhanced error-monitoring among those in the mindful-breathing group.

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

    Directory of Open Access Journals (Sweden)

    Marco eLeite

    2013-01-01

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

  16. EEG generator--a model of potentials in a volume conductor.

    Science.gov (United States)

    Avitan, Lilach; Teicher, Mina; Abeles, Moshe

    2009-11-01

    EEG generator-a model of potentials in a volume conductor. The potential recorded over the cortex electro-corticogram (ECoG) or over the scalp [electroencephalograph (EEG)] derives from the activity of many sources known as "EEG generators." The recorded amplitude is basically a function of the unitary potential of a generator and the statistical relationship between different EEG generators in the recorded population. In this study, we first suggest a new definition of the EEG generator. We use the theory of potentials in a volume conductor and model the contribution of a single synapse activated to the surface potential. We then model the contribution of the generator to the surface potential. Once the generator and its contribution are well defined, we can quantitatively assess the degree of synchronization among generators. The measures obtained by the model for a real life scenario of a group of generators organized in a specific statistical way were consistent with the expected values that were reported experimentally. The study sheds new light on macroscopic modeling approaches which make use of mean soma membrane potential. We showed major contribution of activity of superficial apical synapses to the ECoG signal recorded relative to lower somatic or basal synapses activity.

  17. Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

    Directory of Open Access Journals (Sweden)

    D. V. Moretti

    2012-01-01

    Full Text Available We evaluated the relationship between brain rhythmicity and both the cerebrovascular damage (CVD and amygdalohippocampal complex (AHC atrophy, as revealed by scalp electroencephalography (EEG in a cohort of subjects with mild cognitive impairment (MCI. All MCI subjects underwent EEG recording and magnetic resonance imaging. EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 frequency bands. In the spectral band power the severity of CVD was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volumes are separately considered, within amygdalohippocampal complex (AHC, the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy. CVD and AHC damages are associated with specific EEG markers. So far, these EEG markers could have a prospective value in differential diagnosis between vascular and degenerative MCI.

  18. EEG in the neonatal unit.

    Science.gov (United States)

    Lamblin, M D; de Villepin-Touzery, A

    2015-03-01

    The execution and interpretation of neonatal EEG adheres to strict and specific criteria related to this very early age. In preterm newborns, the dedicated healthcare staff needs to respect EEG indications and chronology of EEG recordings in order to diagnose and manage various pathologies, and use EEG in addition to cerebral imaging. EEG analysis focuses on a global vision of the recording according to the neonate's state of alertness and various age-related patterns. Monitoring of continuous conventional EEG and simplified EEG signal processing can help screen for seizures and monitor the effect of antiepileptic treatment, as well as appreciating changes in EEG background activity, for diagnostic and prognostic purposes. EEG reports should be highly explanatory to meet the expectations of the physician's clinical request.

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

    Directory of Open Access Journals (Sweden)

    Vangelis Sakkalis

    2008-01-01

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

  20. 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).

  1. Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days.

    Science.gov (United States)

    Cannon, Rex L; Baldwin, Debora R; Shaw, Tiffany L; Diloreto, Dominic J; Phillips, Sherman M; Scruggs, Annie M; Riehl, Timothy C

    2012-06-14

    There is a growing interest for using quantitative EEG and LORETA current source density in clinical and research settings. Importantly, if these indices are to be employed in clinical settings then the reliability of these measures is of great concern. Neuroguide (Applied Neurosciences) is sophisticated software developed for the analyses of power, and connectivity measures of the EEG as well as LORETA current source density. To date there are relatively few data evaluating topographical EEG reliability contrasts for all 19 channels and no studies have evaluated reliability for LORETA calculations. We obtained 4 min eyes-closed and eyes-opened EEG recordings at 30-day intervals. The EEG was analyzed in Neuroguide and FFT power, coherence and phase was computed for traditional frequency bands (delta, theta, alpha and beta) and LORETA current source density was calculated in 1 Hz increments and summed for total power in eight regions of interest (ROI). In order to obtain a robust measure of reliability we utilized a random effects model with an absolute agreement definition. The results show very good reproducibility for total absolute power and coherence. Phase shows lower reliability coefficients. LORETA current source density shows very good reliability with an average 0.81 for ECB and 0.82 for EOB. Similarly, the eight regions of interest show good to very good agreement across time. Implications for future directions and use of qEEG and LORETA in clinical populations are discussed.

  2. 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.

  3. Pain Ratings, Psychological Functioning and Quantitative EEG in a Controlled Study of Chronic Back Pain Patients

    Science.gov (United States)

    Schmidt, Stefan; Naranjo, José Raúl; Brenneisen, Christina; Gundlach, Julian; Schultz, Claudia; Kaube, Holger; Hinterberger, Thilo; Jeanmonod, Daniel

    2012-01-01

    Objectives Several recent studies report the presence of a specific EEG pattern named Thalamocortical Dysrhythmia (TCD) in patients with severe chronic neurogenic pain. This is of major interest since so far no neuroscientific indicator of chronic pain could be identified. We investigated whether a TCD-like pattern could be found in patients with moderate chronic back pain, and we compared patients with neuropathic and non-neuropathic pain components. We furthermore assessed the presence of psychopathology and the degree of psychological functioning and examined whether the strength of the TCD-related EEG markers is correlated with psychological symptoms and pain ratings. Design Controlled clinical trial with age and sex matched healthy controls. Methods Spontaneous EEG was recorded in 37 back pain patients and 37 healthy controls. Results We were not able to observe a statistically significant TCD effect in the EEG data of the whole patient group, but a subsample of patients with evidence for root damage showed a trend in this direction. Pain patients showed markedly increased psychopathology. In addition, patients' ratings of pain intensity within the last 1 to 12 months showed strong correlations with EEG power, while psychopathology was correlated to the peak frequency. Conclusion Out of several possible interpretations the most likely conclusion is that only patients with severe pain as well as root lesions with consecutive thalamic deafferentation develop the typical TCD pattern. Our primary method of defining ‘neuropathic pain’ could not reliably determine if such a deafferentation was present. Nevertheless the analysis of a specific subsample as well as correlations between pain ratings, psychopathology and EEG power and peak frequency give some support to the TCD concept. Trial Registration ClinicalTrials.gov NCT00744575 PMID:22431961

  4. Artificial apnea classification with quantitative sleep EEG synchronization.

    Science.gov (United States)

    Akṣahin, Mehmet; Aydın, Serap; Fırat, Hikmet; Eroǧul, Osman

    2012-02-01

    In the present study, both linear and nonlinear EEG synchronization methods so called Coherence Function (CF) and Mutual Information (MI) are performed to obtain high quality signal features in discriminating the Central Sleep Apnea (CSA) and Obstructive Sleep Apnea (OSA) from controls. For this purpose, sleep EEG series recorded from patients and healthy volunteers are classified by using several Feed Forward Neural Network (FFNN) architectures with respect to synchronic activities between C3 and C4 recordings. Among the sleep stages, stage2 is considered in tests. The NN approaches are trained with several numbers of neurons and hidden layers. The results show that the degree of central EEG synchronization during night sleep is closely related to sleep disorders like CSA and OSA. The MI and CF give us cooperatively meaningful information to support clinical findings. Those three groups determined with an expert physician can be classified by addressing two hidden layers with very low absolute error where the average area of CF curves ranged form 0 to 10 Hz and the average MI values are assigned as two features. In a future work, these two features can be combined to create an integrated single feature for error free apnea classification.

  5. EEG Correlates of Ten Positive Emotions

    Science.gov (United States)

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

    2017-01-01

    Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants’ ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as ‘encouragement’ for awe, gratitude, hope, inspiration, pride, ‘playfulness’ for amusement, joy, interest, and ‘harmony’ for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions. PMID:28184194

  6. [EEG recordings during episodes of palinacousis and palinopsia].

    Science.gov (United States)

    Auzou, P; Parain, D; Ozsancak, C; Weber, J; Hannequin, D

    1997-11-01

    Palinopsia (visual perseveration) and palinacousis (auditory perseveration) are reported in a 51-year-old woman with a left temporo parietal astrocytoma. EEG showed a left temporal delta-focus with sharp waves. EEG was not modified during neither palinacousis nor palinopsia. The relationship between the two hallucinatory phenomena and epilepsy is discussed.

  7. Invasive EEG explorations.

    Science.gov (United States)

    Taussig, D; Montavont, A; Isnard, J

    2015-03-01

    The Wada test was adapted from the procedure described by Wada in 1964. It still has a role in the prognostic evaluation of memory disorders after mesial temporal lobectomy. The test consists of injecting a short-acting anesthetic into one hemisphere, under continuous EEG monitoring and during carotid catheterization, to verify the function of contralateral structures. Intracranial EEG recordings deliver signals with few artifacts, and which are quite specific of the zone explored. Three types of electrodes are in common use: (a) foramen ovale (FO) electrodes: electrodes can be inserted directly, without any stereotactic procedure, to provide easy and comparative EEG recordings of the lower and middle portions of the temporal lobe close to the hippocampus. These allow validation of the temporal lobe origin of seizures using FO electrodes recording coupled with scalp EEG; (b): subdural strip or grip electrodes. This relatively aggressive technique carries infectious and hemorrhagic risks and does not allow the exploration of deep cortical structures. However, it permits precise functional cortical mapping via electrical stimulation because of dense and regular positioning of electrodes over the cortical convexity; (c) stereotactically implanted depth electrodes (stereo-electroencephalography [SEEG]). Electrodes are individually planned and inserted within the brain parenchyma through small burr holes. This technique is less aggressive than subdural grid exploration. However it offers relatively limited spatial sampling that may be less well adapted to precise functional evaluation. It allows recording from deep cortical structures and can be argued to be the gold standard of presurgical EEG exploration.

  8. Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

    Science.gov (United States)

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

    2001-01-01

    The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

  9. Utility of initial EEG in first complex febrile seizure.

    Science.gov (United States)

    Harini, Chellamani; Nagarajan, Elanagan; Kimia, Amir A; de Carvalho, Rachel Marin; An, Sookee; Bergin, Ann M; Takeoka, Masanori; Pearl, Phillip L; Loddenkemper, Tobias

    2015-11-01

    The risk of developing epilepsy following febrile seizures (FS) varies between 2% and 10%, with complex febrile seizures (CFS) having a higher risk. We examined the utility of detected epileptiform abnormalities on the initial EEG following a first CFS in predicting subsequent epilepsy. This was a retrospective study of consecutive patients (ages 6-60 months) who were neurologically healthy or mildly delayed, seen in the ED following a first CFS and had both an EEG and minimum of 2-year follow-up. Data regarding clinical characteristics, EEG report, development of subsequent epilepsy, and type of epilepsy were collected. Established clinical predictors for subsequent epilepsy in children with FS and EEG status were evaluated for potential correlation with the development of subsequent epilepsy. Sensitivity, specificity, and positive and negative predictive values of an abnormal EEG (epileptiform EEG) were calculated. A group of 154 children met our inclusion criteria. Overall, 20 (13%) children developed epilepsy. The prevalence of epilepsy was 13% (CI 8.3-19.6%). Epileptiform abnormalities were noted in 21 patients (13.6%), EEG slowing in 23 patients (14.9%), and focal asymmetry in six (3.8%). Epileptiform EEGs were noted in 20% (4/20) of patients with epilepsy and 13% (17/134) of patients without epilepsy (p=0.48). At an estimated risk of subsequent epilepsy of 10% (from population-based studies of children with FS), we determined that the PPV of an epileptiform EEG for subsequent epilepsy was 15%. None of the clinical variables (presence of more than 1 complex feature, family history of epilepsy, or status epilepticus) predicted epilepsy. An epileptiform EEG was not a sensitive measure and had a poor positive predictive value for the development of epilepsy among neurologically healthy or mildly delayed children with a first complex febrile seizure. The practice of obtaining routine EEG for predicting epilepsy after the first CFS needs clarification by well

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

    Science.gov (United States)

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

    2012-08-01

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

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

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  12. Instrumental and vocal music effects on EEG and EKG in neonates of depressed and non-depressed mothers.

    Science.gov (United States)

    Hernandez-Reif, Maria; Diego, Miguel; Field, Tiffany

    2006-12-01

    Neonates (M age=16 days) born to depressed and non-depressed mothers were randomly assigned to hear an audiotaped lullaby of instrumental music with vocals or without vocals. Neonatal EEG and EKG were recorded for 2min (baseline) of silence and for 2min of one or the other music presentation. Neonates of non-depressed mothers showed greater relative right frontal EEG asymmetry to both types of music, suggesting a withdrawal response. Neonates of depressed mothers on the other hand showed greater relative left frontal EEG asymmetry to the instrumental without vocal segment, suggesting an approach response, and greater relative right frontal EEG asymmetry to the instrumental with vocal segment, suggesting a withdrawal response. Heart rate decelerations occurred following the music onset for both groups of infants, however, compared to infants of non-depressed mothers, infants of depressed mothers showed a delayed heart rate deceleration, suggesting slower processing and/or delayed attention. These findings suggest that neonates of depressed and non-depressed mothers show different EKG and EEG responses to instrumental music with versus without vocals.

  13. Iron deficiency (ID) at both birth and 9 months predicts right frontal EEG asymmetry in infancy.

    Science.gov (United States)

    Armony-Sivan, Rinat; Zhu, Bingquan; Clark, Katy M; Richards, Blair; Ji, Chai; Kaciroti, Niko; Shao, Jie; Lozoff, Betsy

    2016-05-01

    This study considered effects of timing and duration of iron deficiency (ID) on frontal EEG asymmetry in infancy. In healthy term Chinese infants, EEG was recorded at 9 months in three experimental conditions: baseline, peek-a-boo, and stranger approach. Eighty infants provided data for all conditions. Prenatal ID was defined as low cord ferritin or high ZPP/H. Postnatal ID was defined as ≥ two abnormal iron measures at 9 months. Study groups were pre- and postnatal ID, prenatal ID only, postnatal ID only, and not ID. GLM repeated measure analysis showed a main effect for iron group. The pre- and postnatal ID group had negative asymmetry scores, reflecting right frontal EEG asymmetry (mean ± SE: -.18 ± .07) versus prenatal ID only (.00 ± .04), postnatal ID only (.03 ± .04), and not ID (.02 ± .04). Thus, ID at both birth and 9 months was associated with right frontal EEG asymmetry, a neural correlate of behavioral withdrawal and negative emotions.

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

    Directory of Open Access Journals (Sweden)

    Jia-lei YANG

    2016-05-01

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

  15. 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.

  16. The EEG novella 2009; Die EEG-Novelle 2009

    Energy Technology Data Exchange (ETDEWEB)

    Altrock, M.; Lehnert, W. [Kanzlei Becker Buettner Held, Berlin (Germany)

    2008-08-15

    The Renewable Energy Resources Act (EEG), already created in the year 2000, was reformed comprehensively in the year 2004. In the year 2009, a new novella of the EEG (EEG 2009) will become effective. This novella plans both an adjustment of numerous reimbursements and changes of general regulations. Furthermore, a regulation authorization was created. Due to this regulation authorization, the country wide balance regulation can be amended. At first, the authors of the contribution under consideration give an overview according to the process of legislative procedure and according to the substantial new regulations of the EEG 2009 in comparison to EEG 2004. Subsequently, individual important new regulations are discussed deeply.

  17. 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.

  18. 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.

  19. Association of Electroencephalography (EEG) Power Spectra with Corneal Nerve Fiber Injury in Retinoblastoma Patients.

    Science.gov (United States)

    Liu, Jianliang; Sun, Juanjuan; Diao, Yumei; Deng, Aijun

    2016-09-04

    BACKGROUND In our clinical experience we discovered that EEG band power may be correlated with corneal nerve injury in retinoblastoma patients. This study aimed to investigate biomarkers obtained from electroencephalography (EEG) recordings to reflect corneal nerve injury in retinoblastoma patients. MATERIAL AND METHODS Our study included 20 retinoblastoma patients treated at the Department of Ophthalmology, Affiliated Hospital of Weifang Medical University between 2010 and 2014. Twenty normal individuals were included in the control group. EEG activity was recorded continuously with 32 electrodes using standard EEG electrode placement for detecting EEG power. A cornea confocal microscope was used to examine corneal nerve injury in retinoblastoma patients and normal individuals. Spearman rank correlation analysis was used to analyze the correlation between corneal nerve injury and EEG power changes. The sensitivity and specificity of changed EEG power in diagnosis of corneal nerve injury were also analyzed. RESULTS The predominantly slow EEG oscillations changed gradually into faster waves in retinoblastoma patients. The EEG pattern in retinoblastoma patients was characterized by a distinct increase of delta (PEEG spectra power and negatively correlated with theta EEG spectra power. The diagnostic sensitivity and specificity by compounding in the series were 60% and 67%, respectively. CONCLUSIONS Changes in delta and theta of EEG appear to be associated with occurrence of corneal nerve injury. Useful information can be provided for evaluating corneal nerve damage in retinoblastoma patients through analyzing EEG power bands.

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

    Directory of Open Access Journals (Sweden)

    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.

  1. Comparing tomographic EEG neurofeedback and EMG biofeedback in children with attention-deficit/hyperactivity disorder.

    Science.gov (United States)

    Maurizio, Stefano; Liechti, Martina Daniela; Heinrich, Hartmut; Jäncke, Lutz; Steinhausen, Hans-Christoph; Walitza, Susanne; Brandeis, Daniel; Drechsler, Renate

    2014-01-01

    Two types of biofeedback (BF), tomographic electroencephalogram (EEG) neurofeedback (NF) and electromyographic biofeedback (EMG-BF), both with phasic and tonic protocols, were compared for treatment effects and specificity in attention-deficit/hyperactivity disorder (ADHD). Thirteen children with ADHD trained their brain activity in the anterior cingulate cortex (ACC), and twelve trained activity of arm muscles involved in fine motor skills. In each training session, resting state 24-channel EEG and training performances were recorded. Both groups showed similar behavioral improvements and artifact reduction in selected conditions, with no significant advantages despite medium effect sizes on primary outcomes for NF. Only the EMG-BF group, however, showed clear improvement in training regulation performance, and specific motor coordination effects. The NF group tended to present individual normalization of trained frequency bands in the ACC during rest across training. The results provide evidence for some specific effects in our small sample, albeit only to a small extent.

  2. A Study on Melody Tempo with EEG

    Institute of Scientific and Technical Information of China (English)

    Yuan Yuan; Yong-Xiu Lai; Dan Wu; De-Zhong Yao

    2009-01-01

    In this paper,tempo perception is investi-gated by recording spontaneous electroencephalograph (EEG).Ten normal male non-musician college students are selected according to questionnaire results after listening absorbedly to four different tempos of an excerpt from a Mozart sonata.EEGs data are recorded when the subjects are listening to the music.The EEG spectral power (SP) is analyzed for alpha band.The varying trend of power spectrum during exposure to music excerpts of different tempos is studied and shows the consistence with the previous tempo-specific hypo-thesis:a tempo-transformed performance will sound less natural than an original performance does.The results presented in this paper suggest that tempo is an important factor that could influence the alpha rhythm.

  3. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants.

    Science.gov (United States)

    Sabeti, Malihe; Katebi, Serajeddin; Boostani, Reza

    2009-11-01

    In this paper, electroencephalogram (EEG) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups. For each case, 20 channels of EEG are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension are extracted from EEG signals. Leave-one (participant)-out cross-validation is used for reliable estimate of the separability of the two groups. The training set is used for training the two classifiers, namely, linear discriminant analysis (LDA) and adaptive boosting (Adaboost). Each classifier is assessed using the test dataset. A classification accuracy of 86% and 90% is obtained by LDA and Adaboost respectively. For further improvement, genetic programming is employed to select the best features and remove the redundant ones. Applying the two classifiers to the reduced feature set, a classification accuracy of 89% and 91% is obtained by LDA and Adaboost respectively. The proposed technique is compared and contrasted with a recently reported method and it is demonstrated that a considerably enhanced performance is achieved. This study shows that EEG signals can be a useful tool for discrimination of the schizophrenic and control participants. It is suggested that this analysis can be a complementary tool to help psychiatrists diagnosing schizophrenic patients.

  4. Treatment Effects on Neonatal EEG.

    Science.gov (United States)

    Obeid, Rawad; Tsuchida, Tammy N

    2016-10-01

    Conventional EEG and amplitude-integrated electroencephalography are used in neonates to assess prognosis and significant changes in brain activity. Neuroactive medications and hypothermia can influence brain activity and therefore alter EEG interpretation. There are limited studies on the effect of these therapies on neonatal EEG background activity. Medication effects on the EEG or amplitude-integrated electroencephalography include increased interburst interval duration, voltage suppression, and sleep disruption. The effect is transient in term newborns but can be persistent in premature newborns. Although therapeutic hypothermia does not produce significant changes in EEG activity, it does change the time point at which EEG can accurately predict neurodevelopmental outcome. It is important to account for these effects on the EEG to avoid inaccurate interpretation that may affect prognostication.

  5. Evaluation of partial epilepsy in Iran: role of video-EEG, EEG, and MRI with epilepsy protocol

    Directory of Open Access Journals (Sweden)

    Parastoo Faraji

    2011-05-01

    Full Text Available Background: we evaluated the diagnostic value of Electroencephalography (EEG, video-EEG monitoring (VEM and Magnetic resonance imaging (MRI of the brain with epilepsy protocol in patients with complex partial epilepsy.Methods: Forty-two consecutive patients underwent complete neurological examination, EEG, and MRI with a modified epilepsy protocol. A subset of these patients (n=29 also underwent VEM. Data were presented using descriptive statistics and were analyzed using Chi square and McNemar tests.Results: Twenty-four women and eighteen men entered the study. The mean (±SD age for patients, was 25.2(±10.1 and mean (±SD age at onset was 10.9(±8.1. All patients had abnormal ictal or interictal EEG. Fifteen patients had normal MRI. Temporal lobe involvement was the most common involvement in both EEG (27 patients and MRI (14 patients. Interictal EEG was abnormal in 81% of patients which showed epileptiform discharges in about half of the cases. In half of patients who had lateralized finding on MRI, site of the lesion was congruent between MRI and interictal EEG. Thirty-six patients had symptoms suggesting a specific lobe, of which interictal EEG was able to show the concordant lobe in 22 (61% patients. McNemar test showed superiority of EEG over MRI in correct diagnosis of the involved lobe based on the clinical manifestations (P<0.01.Conclusion: In our setting, both ictal and interictal EEG perform better than MRI in evaluating complex partial epilepsy. In addition, combination of these tools may increase the yield of showing abnormality to near 100% in patients with complex partial epilepsy

  6. [Qualitative and quantitative EEG-findings in schizophrenia (author's transl)].

    Science.gov (United States)

    Itil, T M

    1978-03-01

    The results of the qualitative but particularly the quantitative EEG-studies indicate that 1. The EEG of adult schizophrenics is characterized by an appearance of excessive fast activity along with some slow waves and the lack of alpha-activity. 2. Excessive fast activity and lack of alpha-waves have also been found in the EEGs of psychotic children and most interestingly in children whose parents (particularly the mother) are schizophrenic (high risk children). 3. Based on the studies during sleep and investigations with neuroleptics, it was established that the origin of the excess fast activity in schizophrenia cannot be the muscle potential. Particularly the excess fast activity in high risk children for schizophrenia goes against the muscle potential hypothesis. 4. The quantitative EEG changes seen in schizophrenia show similarity to those seen after hallucinogenic compounds particularly after anticholinergic hallucinogenics. 5. All neuroleptics (major tranquilizers) produce quantitative EEG alterations which are almost diametrically opposite to those seen in schizoprenia.

  7. 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.

  8. Resting state EEG oscillatory power differences in ADHD college students and their peers

    Directory of Open Access Journals (Sweden)

    Woltering Steven

    2012-12-01

    Full Text Available Abstract Background Among the most robust neural abnormalities differentiating individuals with Attention-Deficit/Hyperactivity Disorder (ADHD from typically developing controls are elevated levels of slow oscillatory activity (e.g., theta and reduced fast oscillatory activity (e.g., alpha and beta during resting-state electroencephalography (EEG. However, studies of resting state EEG in adults with ADHD are scarce and yield inconsistent findings. Methods EEG profiles, recorded during a resting-state with eyes-open and eyes-closed conditions, were compared for college students with ADHD (n = 18 and a nonclinical comparison group (n = 17. Results The ADHD group showed decreased power for fast frequencies, especially alpha. This group also showed increased power in the slow frequency bands, however, these effects were strongest using relative power computations. Furthermore, the theta/beta ratio measure was reliably higher for the ADHD group. All effects were more pronounced for the eyes-closed compared to the eyes-open condition. Measures of intra-individual variability suggested that brains of the ADHD group were less variable than those of controls. Conclusions The findings of this pilot study reveal that college students with ADHD show a distinct neural pattern during resting state, suggesting that oscillatory power, especially alpha, is a useful index for reflecting differences in neural communication of ADHD in early adulthood.

  9. EEG entropy measures in anesthesia

    Directory of Open Access Journals (Sweden)

    Zhenhu eLiang

    2015-02-01

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

  10. Interactive effects of age and gender on EEG power and coherence during a short-term memory task in middle-aged adults.

    Science.gov (United States)

    Kober, Silvia Erika; Reichert, Johanna Louise; Neuper, Christa; Wood, Guilherme

    2016-04-01

    The effects of age and gender on electroencephalographic (EEG) activity during a short-term memory task were assessed in a group of 40 healthy participants aged 22-63 years. Multi-channel EEG was recorded in 20 younger (mean = 24.65-year-old, 10 male) and 20 middle-aged participants (mean = 46.40-year-old, 10 male) during performance of a Sternberg task. EEG power and coherence measures were analyzed in different frequency bands. Significant interactions emerged between age and gender in memory performance and concomitant EEG parameters, suggesting that the aging process differentially influences men and women. Middle-aged women showed a lower short-term memory performance compared to young women, which was accompanied by decreasing delta and theta power and increasing brain connectivity with age in women. In contrast, men showed no age-related decline in short-term memory performance and no changes in EEG parameters. These results provide first evidence of age-related alterations in EEG activity underlying memory processes, which were already evident in the middle years of life in women but not in men.

  11. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene

    Science.gov (United States)

    Rodriguez, Rene; Lopera, Francisco; Alvarez, Alfredo; Fernandez, Yuriem; Galan, Lidice; Quiroz, Yakeel; Bobes, Maria Antonieta

    2014-01-01

    To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D2) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D2, the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D2 using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function. PMID:24551475

  12. Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene.

    Science.gov (United States)

    Rodriguez, Rene; Lopera, Francisco; Alvarez, Alfredo; Fernandez, Yuriem; Galan, Lidice; Quiroz, Yakeel; Bobes, Maria Antonieta

    2014-01-01

    To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

  13. Relationship between EEG power and rhythm synchronization in health and cognitive pathology.

    Science.gov (United States)

    Strelets, V B; Garakh, Zh V; Novototskii-Vlasov, V Yu; Magomedov, R A

    2006-07-01

    We report here studies of comparative measures of spectral density and cortical interactions in EEG rhythms in health and schizophrenia. In healthy subjects, all rhythms were symmetrical and synchronous. In "acute" schizophrenia, unlike the situation in health, there was asymmetry (predominantly right-sided) in the distribution of the spectral power of EEG rhythms. In chronic patients, asymmetry was less marked, though the power of most EEG rhythms was significantly lower than in the other two study groups. "Acute" patients showed a lack of interhemisphere interactions for all rhythms apart from the alpha rhythm, while the number of cortical interactions in chronic patients was rather lower than that in the "acute" patients, though there were significantly fewer than in healthy subjects. In addition, the gamma range showed only one interhemisphere association in the posterior areas. These neurophysiological characteristics may underlie a number of the impairments of mental activity in patients with schizophrenia. These data may also indicate that the linkage between power characteristics and synchronization of EEG rhythms is a necessary condition for normal perceptive and cognitive activity and the organization of behavior.

  14. Dissociative symptoms and interregional EEG cross-correlations in paranoid schizophrenia.

    Science.gov (United States)

    Bob, Petr; Susta, Marek; Glaslova, Katerina; Boutros, Nash N

    2010-05-15

    Recent findings indicate that binding and synchronization of distributed activities are crucial for the mechanism of consciousness, and there is increased evidence that disruptions in feature binding produce disintegration of consciousness in schizophrenia. These data suggest that the disrupted binding and disintegration of consciousness could be related to dissociation, which is historically linked to Bleuler's concept of splitting in schizophrenia. In the present study we aimed to investigate relations among electroencephalogram (EEG) activities of cortical sites and used psychometric measures of positive and negative schizophrenia symptoms (Positive and Negative Syndrome Scale) and the Dissociative Experiences Scale (DES) in 58 patients with paranoid schizophrenia. The results show statistically significant Spearman correlations of the DES with cross-correlation function in nine (of 16) EEG pairs. Positive symptoms display significant Spearman correlation with mean of cross-correlation function in only one EEG pair (F4-C4). Results of the Mann-Whitney test between patients with higher (DES > or = 30) and lower dissociation show statistically significant differences between the groups for cross-correlations in nine EEG pairs. The results of this study provide the first supportive evidence for a negative relationship between cross-correlation indices and symptoms of dissociation in schizophrenia.

  15. Discriminant Multitaper Component Analysis of EEG

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Sajda, Paul

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

  16. Analysis of EEG features of neuronal surface antibody associated encephalitis

    Directory of Open Access Journals (Sweden)

    Lu-hua WEI

    2016-09-01

    Full Text Available Objective To summarize the clinical manifestations, EEG and head MRI features of neuronal surface antibody associated encephalitis, and to investigate the role of EEG in determining the relapse or fluctuation of this disease, characteristics of EEG corresponding to head MRI, and EEG features in different clinical stages. Methods A total of 23 patients with neuronal surface antibody associated encephalitis were divided into ascent, climax, descent and recovery stage according to their clinical course. The relation between EEG background activity, distribution of slow wave, epileptiform discharge, extreme delta brush (EDB and relapse or fluctuation of the disease was analyzed. The relation between EEG features and head MRI abnormalities, and also EEG features in different stages were analyzed. Results There were 19 anti-N-methyl-D-aspartate (NMDA receptor encephalitis patients, 3 anti-leucine-rich glioma-inactivated 1 (LGI1 antibody associated encephalitis and one anti-γ-aminobutyric acid B receptor (GABABR antibody associated encephalitis. The frequencies of clinical presentations were psychological or cognitive dysfunction, epileptic seizure, conscious disturbance, speech dysfunction and movement disorder in descending order. Within 30.50 d from onset, 6 patients demonstrated slow wave background, of whom 2 relapsed or fluctuated; 5 patients had α rhythm background and none of them relapsed or fluctuated. In patients with anti-NMDA receptor encephalitis, the difference in first hospital stay (Z = -0.785, P = 0.433 and relapse or fluctuation (Fisher's exact probability: P = 0.155 between EDB group and non-EDB group was not significant. There was no apparent correlation between EEG background activities and head MRI abnormalities in different stages. In ascent and climax stage, EEG background activities were predominantly slow wave, and the distribution of slow wave was relatively broader. EEG background changed to α rhythm from descent stage

  17. Diagnostic Accuracy of microEEG: A Miniature, Wireless EEG Device

    OpenAIRE

    Grant, Arthur C.; Samah G Abdel-Baki; Omurtag, Ahmet; Sinert, Richard; Chari, Geetha; Malhotra, Schweta; Weedon, Jeremy; Andre A Fenton; Zehtabchi, Shahriar

    2014-01-01

    Measuring the diagnostic accuracy (DA) of an EEG device is unconventional and complicated by imperfect interrater reliability. We sought to compare the DA of a miniature, wireless, battery-powered EEG device (“microEEG”) to a reference EEG machine in emergency department (ED) patients with altered mental status (AMS). 225 ED patients with AMS underwent 3 EEGs. EEG1 (Nicolet Monitor, “reference”) and EEG2 (microEEG) were recorded simultaneously with EEG cup electrodes using a signal splitter. ...

  18. 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.

  19. Circadian cycle-dependent EEG biomarkers of pathogenicity in adult mice following prenatal exposure to in utero inflammation.

    Science.gov (United States)

    Adler, D A; Ammanuel, S; Lei, J; Dada, T; Borbiev, T; Johnston, M V; Kadam, S D; Burd, I

    2014-09-05

    Intrauterine infection or inflammation in preterm neonates is a known risk for adverse neurological outcomes, including cognitive, motor and behavioral disabilities. Our previous data suggest that there is acute fetal brain inflammation in a mouse model of intrauterine exposure to lipopolysaccharides (LPS). We hypothesized that the in utero inflammation induced by LPS produces long-term electroencephalogram (EEG) biomarkers of neurodegeneration in the exposed mice that could be determined by using continuous quantitative video/EEG/electromyogram (EMG) analyses. A single LPS injection at E17 was performed in pregnant CD1 dams. Control dams were injected with same volumes of saline (LPS n=10, Control n=8). At postnatal age of P90-100, 24-h synchronous video/EEG/EMG recordings were done using a tethered recording system and implanted subdural electrodes. Behavioral state scoring was performed blind to treatment group, on each 10s EEG epoch using synchronous video, EMG and EEG trace signatures to generate individual hypnograms. Automated EEG power spectrums were analyzed for delta and theta-beta power ratios during wake vs. sleep cycles. Both control and LPS hypnograms showed an ultradian wake/sleep cycling. Since rodents are nocturnal animals, control mice showed the expected diurnal variation with significantly longer time spent in wake states during the dark cycle phase. In contrast, the LPS-treated mice lost this circadian rhythm. Sleep microstructure also showed significant alteration in the LPS mice specifically during the dark cycle, caused by significantly longer average non-rapid eye movement (NREM) cycle durations. No significance was found between treatment groups for the delta power data; however, significant activity-dependent changes in theta-beta power ratios seen in controls were absent in the LPS-exposed mice. In conclusion, exposure to in utero inflammation in CD1 mice resulted in significantly altered sleep architecture as adults that were circadian

  20. 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.

  1. Task-related functional connectivity in autism spectrum conditions: an EEG study using wavelet transform coherence

    Directory of Open Access Journals (Sweden)

    Catarino Ana

    2013-01-01

    Full Text Available Abstract Background Autism Spectrum Conditions (ASC are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC. Methods We analyzed electroencephalography (EEG data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset. Results Results demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8, such differences were only seen for one electrode pair in the ASC group (T7-T8. No significant differences in EEG power spectra were observed between groups. Conclusions Interhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain. Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals. Overall, the results of this research support the value of WTC

  2. Predictive value of the amplitude integrated EEG in infants with hypoxic ischaemic encephalopathy: data from a randomised trial of therapeutic hypothermia.

    Science.gov (United States)

    Azzopardi, Denis

    2014-01-01

    The amplitude integrated EEG (aEEG) is reputed to be one of the best predictors of neurological outcome following hypoxic ischaemic encephalopathy in term newborns and was used to select infants into trials of neuroprotection with hypothermia, but its predictive value and the effect of moderate hypothermia on the aEEG have not previously been examined in a randomised study. The positive predictive value (PPV) of the aEEG recorded within 6 h of birth for death or disability at 18 months of age was determined in 314 infants born after 35 weeks gestation who were randomised to receive standard care with or without cooling for 72 h. The aEEG was classified according to voltage and by pattern. The PPV of a severely abnormal aEEG assessed by the voltage and pattern methods was 0.63 and 0.59 respectively in non-cooled infants and 0.55 and 0.51 in cooled infants (p>0.05). Although the differences in PPV between cooled and non-cooled groups were not significant, they are consistent with observational studies showing a lower PPV in infants treated with hypothermia, probably due to a neuroprotective effect of cooling.

  3. Seizure (Ictal—EEG Characteristics in Subgroups of Depressive Disorder in Patients Receiving Electroconvulsive Therapy (ECT—A Preliminary Study and Multivariate Approach

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    Björn Wahlund

    2009-01-01

    Full Text Available Objectives. Examine frequency distributions of ictal EEG after ECT stimulation in diagnostic subgroups of depression. Methods. EEG registration was consecutively monitored in 33 patients after ECT stimulation. Patients were diagnosed according to DSM IV and subdivided into: (1 major depressive disorder with psychotic features (n=7, (2 unipolar depression (n=20, and (3 bipolar depression (n=6. Results. Results indicate that the diagnostically subgroups differ in their ictal EEG frequency spectrumml: (1 psychotic depression has a high occurrence of delta and theta waves, (2 unipolar depression has high occurrence of delta, theta and gamma waves, and (3 bipolar depression has a high occurrence of gamma waves. A linear discriminant function separated the three clinical groups with an accuracy of 94%. Conclusion. Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment.

  4. Helicobacter pylori genotyping from American indigenous groups shows novel Amerindian vacA and cagA alleles and Asian, African and European admixture.

    Directory of Open Access Journals (Sweden)

    Margarita Camorlinga-Ponce

    Full Text Available It is valuable to extend genotyping studies of Helicobacter pylori to strains from indigenous communities across the world to better define adaption, evolution, and associated diseases. We aimed to genetically characterize both human individuals and their infecting H. pylori from indigenous communities of Mexico, and to compare them with those from other human groups. We studied individuals from three indigenous groups, Tarahumaras from the North, Huichols from the West and Nahuas from the center of Mexico. Volunteers were sampled at their community site, DNA was isolated from white blood cells and mtDNA, Y-chromosome, and STR alleles were studied. H. pylori was cultured from gastric juice, and DNA extracted for genotyping of virulence and housekeeping genes. We found Amerindian mtDNA haplogroups (A, B, C, and D, Y-chromosome DYS19T, and Amerindian STRs alleles frequent in the three groups, confirming Amerindian ancestry in these Mexican groups. Concerning H.pylori cagA phylogenetic analyses, although most isolates were of the Western type, a new Amerindian cluster neither Western nor Asian, was formed by some indigenous Mexican, Colombian, Peruvian and Venezuelan isolates. Similarly, vacA phylogenetic analyses showed the existence of a novel Amerindian type in isolates from Alaska, Mexico and Colombia. With hspA strains from Mexico and other American groups clustered within the three major groups, Asian, African or European. Genotyping of housekeeping genes confirmed that Mexican strains formed a novel Asian-related Amerindian group together with strains from remote Amazon Aborigines. This study shows that Mexican indigenous people with Amerindian markers are colonized with H. pylori showing admixture of Asian, European and African strains in genes known to interact with the gastric mucosa. We present evidence of novel Amerindian cagA and vacA alleles in indigenous groups of North and South America.

  5. Helicobacter pylori Genotyping from American Indigenous Groups Shows Novel Amerindian vacA and cagA Alleles and Asian, African and European Admixture

    Science.gov (United States)

    Camorlinga-Ponce, Margarita; Perez-Perez, Guillermo; Gonzalez-Valencia, Gerardo; Mendoza, Irma; Peñaloza-Espinosa, Rosenda; Ramos, Irma; Kersulyte, Dangeruta; Reyes-Leon, Adriana; Romo, Carolina; Granados, Julio; Muñoz, Leopoldo; Berg, Douglas E.; Torres, Javier

    2011-01-01

    It is valuable to extend genotyping studies of Helicobacter pylori to strains from indigenous communities across the world to better define adaption, evolution, and associated diseases. We aimed to genetically characterize both human individuals and their infecting H. pylori from indigenous communities of Mexico, and to compare them with those from other human groups. We studied individuals from three indigenous groups, Tarahumaras from the North, Huichols from the West and Nahuas from the center of Mexico. Volunteers were sampled at their community site, DNA was isolated from white blood cells and mtDNA, Y-chromosome, and STR alleles were studied. H. pylori was cultured from gastric juice, and DNA extracted for genotyping of virulence and housekeeping genes. We found Amerindian mtDNA haplogroups (A, B, C, and D), Y-chromosome DYS19T, and Amerindian STRs alleles frequent in the three groups, confirming Amerindian ancestry in these Mexican groups. Concerning H.pylori cagA phylogenetic analyses, although most isolates were of the Western type, a new Amerindian cluster neither Western nor Asian, was formed by some indigenous Mexican, Colombian, Peruvian and Venezuelan isolates. Similarly, vacA phylogenetic analyses showed the existence of a novel Amerindian type in isolates from Alaska, Mexico and Colombia. With hspA strains from Mexico and other American groups clustered within the three major groups, Asian, African or European. Genotyping of housekeeping genes confirmed that Mexican strains formed a novel Asian-related Amerindian group together with strains from remote Amazon Aborigines. This study shows that Mexican indigenous people with Amerindian markers are colonized with H. pylori showing admixture of Asian, European and African strains in genes known to interact with the gastric mucosa. We present evidence of novel Amerindian cagA and vacA alleles in indigenous groups of North and South America. PMID:22073291

  6. EEG source imaging during two Qigong meditations.

    Science.gov (United States)

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

    2012-08-01

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

  7. Trait anxiety impact on posterior activation asymmetries at rest and during evoked negative emotions: EEG investigation.

    Science.gov (United States)

    Aftanas, Ljubomir I; Pavlov, Sergey V

    2005-01-01

    The main objective of the present investigation was to examine how high trait anxiety would influence cortical EEG asymmetries under non-emotional conditions and while experiencing negative emotions. The 62-channel EEG was recorded in control (n=21) and high anxiety (HA, n=18) non-patient individuals. Results showed that in HA subjects, the lowest level of arousal (eyes closed) was associated with stronger right-sided parieto-temporal theta-1 (4-6 Hz) and beta-1 (12-18 Hz) activity, whereas increased non-emotional arousal (eyes open, viewing neutral movie clip) was marked by persisting favored right hemisphere beta-1 activity. In turn, viewing aversive movie clip by the HA group led to significant lateralized decrease of the right parieto-temporal beta-1 power, which was initially higher in the emotionally neutral conditions. The EEG data suggests that asymmetrical parieto-temporal theta-1 and beta-1 EEG activity might be better interpreted in terms of Gray's BAS and BIS theory.

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

    Science.gov (United States)

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

    2013-03-01

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

  9. Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression

    CERN Document Server

    Zotev, Vadim; Misaki, Masaya; Phillips, Raquel; Young, Kymberly D; Feldner, Matthew T; Bodurka, Jerzy

    2014-01-01

    Background: Real-time fMRI neurofeedback (rtfMRI-nf) is a promising approach for studies and treatment of major depressive disorder (MDD). EEG performed simultaneously with rtfMRI-nf procedure allows independent evaluation of rtfMRI-nf effects. Frontal EEG asymmetry in the alpha band is a widely used measure of emotion and motivation that shows profound changes in depression. However, it has never been related to simultaneously acquired fMRI data. Methods: We performed the first study combining rtfMRI-nf with simultaneous (passive) EEG recordings. MDD patients in the experimental group (n=13) learned to upregulate BOLD activity of the left amygdala using rtfMRI-nf during a positive emotion induction task. MDD patients in the control group (n=11) were provided with sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper-alpha band and BOLD activity across the brain were examined. Results: Participants in the experimental group showed positive average changes in frontal EEG asymmetry during the ...

  10. Somatosensory-evoked spikes on electroencephalography (EEG): longitudinal clinical and EEG aspects in 313 children.

    Science.gov (United States)

    Fonseca, Lineu Corrêa; Tedrus, Gloria M A S

    2012-01-01

    Somatosensory-evoked spikes (ESp) are high-voltage potentials registered on the EEG, which accompany each of the percussions on the feet or hands. The objective of this research was to study the longitudinal clinical and EEG aspects of children with ESp. A total of 313 children, 53.7% male, showing ESp on the EEG and with an average initial age of 6.82 (range from 2 to 14 years) were followed for a mean period of 35.7 months. In the initial evaluation, 118 (37.7%) had a history of nonfebrile epileptic seizures (ES). Epileptiform activity (EA) was observed on the EEG in 61% and showed a significantly greater occurrence in children with ES than in those without (P = .000). Of the 118 showing seizures from the start, 53 (44.9%) continued to have seizures; of the 195 without seizures at the start, only 13 (6.67%) developed them. Thus, only 66 (21.1%) children showed ES during the follow-up. ESp disappeared in 237 (75.7%) cases and EA in 221 (70.6%). In the children with ES, it was found that the presence of EA on the first EEG did not indicate continuation of the ES throughout the remaining period, while the 13 children who presented their first ES in a later period showed a greater occurrence of EA on the initial EEG than those who did not develop ES (P = .001). Evidence of brain injury was observed in 43 (13.7%) children and was associated with a greater continuity of the ES during the study (P = .018). ESp, EA, and ES tend to disappear, suggesting an age-dependent phenomenon. The finding of ESp, particularly in the absence of any evidence of brain injury, indicates a low association with ES and benign outcome.

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

    Directory of Open Access Journals (Sweden)

    Joshua H Balsters

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

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

    Science.gov (United States)

    Balsters, Joshua H; O'Connell, Redmond G; Martin, Mary P; Galli, Alessandra; Cassidy, Sarah M; Kilcullen, Sophia M; Delmonte, Sonja; Brennan, Sabina; Meaney, Jim F; Fagan, Andrew J; Bokde, Arun L W; Upton, Neil; Lai, Robert; Laruelle, Marc; Lawlor, Brian; Robertson, Ian H

    2011-01-01

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

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

    Science.gov (United States)

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

    2014-07-01

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

  14. 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...

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

    Science.gov (United States)

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

    2006-07-01

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

  16. A channel differential EZW coding scheme for EEG data compression.

    Science.gov (United States)

    Dehkordi, Vahid R; Daou, Hoda; Labeau, Fabrice

    2011-11-01

    In this paper, a method is proposed to compress multichannel electroencephalographic (EEG) signals in a scalable fashion. Correlation between EEG channels is exploited through clustering using a k-means method. Representative channels for each of the clusters are encoded individually while other channels are encoded differentially, i.e., with respect to their respective cluster representatives. The compression is performed using the embedded zero-tree wavelet encoding adapted to 1-D signals. Simulations show that the scalable features of the scheme lead to a flexible quality/rate tradeoff, without requiring detailed EEG signal modeling.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2012-11-14

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

  18. 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.

  19. Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence

    Science.gov (United States)

    Qazi, Emad-ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

    2017-01-01

    Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0–1.875 Hz (delta low) and 1.875–3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system. PMID:28163676

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

    Directory of Open Access Journals (Sweden)

    Kaare B. Mikkelsen

    2017-06-01

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

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

    Science.gov (United States)

    Cichy, Radoslaw Martin; Pantazis, Dimitrios

    2017-07-14

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

  3. Chebyshev and Modified Wavelet Algorithm Based Sleep Arousals Detection Using EEG Sensor Database

    Directory of Open Access Journals (Sweden)

    Mahalaxmi U. S. B. K.

    2017-04-01

    Full Text Available Electroencephalographic (EEG arousals are generally observed in EEG recordings as an awakening response of the human brain. Sleep apnea is a major sleep disorder. The patients, with Severe Sleep Apnea (SAS suffers from frequent interruptions in their sleep which brings about EEG arousals. In this paper, a new method for Segmentation and Filtering process of EEG sensor database signals for finding sleep arousals using Chebyshev and Modified Wavelet Algorithm is proposed. The Segmentation Algorithm appears as various features extracted from EEG Data’s and PSG Recordings. The Chebyshev Equiripple Filter is used in Filtering algorithm and then MSVM [M-Support Vector Machine] was utilized as Classification Tool. Algorithms are performed and different features are extracted and the ROC characteristics are performed. The extracted features are Delta, Gama, Beta, Alpha, Sigma of the EEG signal, EEG Signal Mean, EEG Signal Standard Deviation, EEG Signal Peak Signal to Noise Ratio [PSNR], and EEG Signal Normalization. MSVM tool showing EEG signals results.

  4. Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms

    Science.gov (United States)

    Babiloni, Claudio; Triggiani, Antonio I.; Lizio, Roberta; Cordone, Susanna; Tattoli, Giacomo; Bevilacqua, Vitoantonio; Soricelli, Andrea; Ferri, Raffaele; Nobili, Flavio; Gesualdo, Loreto; Millán-Calenti, José C.; Buján, Ana; Tortelli, Rosanna; Cardinali, Valentina; Barulli, Maria Rosaria; Giannini, Antonio; Spagnolo, Pantaleo; Armenise, Silvia; Buenza, Grazia; Scianatico, Gaetano; Logroscino, Giancarlo; Frisoni, Giovanni B.; del Percio, Claudio

    2016-01-01

    Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG) rhythms in groups of Alzheimer's disease (AD) compared to healthy elderly (Nold) subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA) estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), alpha 2 (10.5–13 Hz), beta 1 (13–20 Hz), beta 2 (20–30 Hz), and gamma (30–40 Hz) were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC) higher than 0.7 as a threshold for a moderate classification rate (i.e., 70%). Results showed that the following EEG markers overcame this threshold: (i) central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii) central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii) frontal theta/alpha 1 current density; (iv) occipital delta/alpha 1 inter-hemispherical connectivity; (v) occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi) parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%). These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%. PMID:26941594

  5. 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.

  6. Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison

    Science.gov (United States)

    Bleichner, Martin G.; Mirkovic, Bojana; Debener, Stefan

    2016-12-01

    Objective. This study presents a direct comparison of a classical EEG cap setup with a new around-the-ear electrode array (cEEGrid) to gain a better understanding of the potential of ear-centered EEG. Approach. Concurrent EEG was recorded from a classical scalp EEG cap and two cEEGrids that were placed around the left and the right ear. Twenty participants performed a spatial auditory attention task in which three sound streams were presented simultaneously. The sound streams were three seconds long and differed in the direction of origin (front, left, right) and the number of beats (3, 4, 5 respectively), as well as the timbre and pitch. The participants had to attend to either the left or the right sound stream. Main results. We found clear attention modulated ERP effects reflecting the attended sound stream for both electrode setups, which agreed in morphology and effect size. A single-trial template matching classification showed that the direction of attention could be decoded significantly above chance (50%) for at least 16 out of 20 participants for both systems. The comparably high classification results of the single trial analysis underline the quality of the signal recorded with the cEEGrids. Significance. These findings are further evidence for the feasibility of around the-ear EEG recordings and demonstrate that well described ERPs can be measured. We conclude that concealed behind-the-ear EEG recordings can be an alternative to classical cap EEG acquisition for auditory attention monitoring.

  7. Towards out-of-the-lab EEG in uncontrolled environments: Feasibility study of dry EEG recordings during exercise bike riding.

    Science.gov (United States)

    Kohli, Siddharth; Casson, Alexander J

    2015-08-01

    Conventional EEG (electroencephalography) has relied on wet electrodes which require conductive gel to help the electrodes make contact with the scalp. In recent years many dry electrode EEG systems have become available that do not require this gel. As a result they are quicker and easier to set up, with the potential to record the the EEG in situations and environments where it has not previously been possible. This paper investigates the practicality of using dry EEG in new non-conventional recording situations. In particular it uses a dry EEG recording system to monitor the EEG while a subject is riding an exercise bike. The results show that good-quality EEG, free from high-amplitude motion artefacts, can be collected in this challenging motion rich environment. In the frequency domain a peak of activity is seen over the motor cortex (C4) at 23 Hz starting five minutes after the start of the exercise task, giving initial insights into the on-going operation of the brain during exercise.

  8. 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.

  9. Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers.

    Science.gov (United States)

    Fink, Andreas; Graif, Barbara; Neubauer, Aljoscha C

    2009-07-01

    Neuroscientific research on creativity has revealed valuable insights into possible brain correlates underlying this complex mental ability domain. However, most of the studies investigated brain activity during the performance of comparatively simple (verbal) type of tasks and the majority of studies focused on samples of the normal population. In this study we investigate EEG activity in professional dancers (n=15) who have attained a high level of expertise in this domain. This group was compared with a group of novices (n=17) who have only basic experience in dancing and completed no comprehensive training in this field. The EEG was recorded during performance of two different dancing imagery tasks which differed with respect to creative demands. In the first task participants were instructed to mentally perform a dance which should be as unique and original as possible (improvisation dance). In the waltz task they were asked to imagine dancing the waltz, a standard dance which involves a sequence of monotonous steps (lower creative demands). In addition, brain activity was also measured during performance of the Alternative Uses test. We observed evidence that during the generation of alternative uses professional dancers show stronger alpha synchronization in posterior parietal brain regions than novice dancers. During improvisation dance, professional dancers exhibited more right-hemispheric alpha synchronization than the group of novices did, while during imagining dancing the waltz no significant group differences emerged. The findings complement and extend existing findings on the relationship between EEG alpha activity and creative thinking.

  10. “I Want to be There When He Graduates:” Foster Parents Show Higher Levels of Commitment than Group Care Providers

    Science.gov (United States)

    Lo, Albert; Roben, Caroline K.P.; Maier, Collin; Fabian, Kim; Shauffer, Carole; Dozier, Mary

    2015-01-01

    Group care is a frequent placement for adolescents placed in out of home care when their birth parents’ care is deemed unsafe. In the present study, we assessed whether foster parents show greater commitment to children than group care providers. Given that group care represents a number of living arrangements, we considered both shift care (where staff work shifts and do not live with the children) and cottage care (where staff live for extended periods of time with the children in a group living context). Commitment was assessed using the This Is My Child Interview (adapted for adolescents). Thirty-one foster parents, 18 shift workers, and 28 cottage care providers were interviewed. As predicted, foster parents showed higher levels of commitment than both shift care workers and cottage care providers, and the associations held when children’s externalizing behaviors and the number of children the caregivers had cared for were controlled. The results suggest that foster care promotes greater commitment among caregivers than other out of home placements, and add to other findings that favor foster care as the out of home placement of choice for adolescents. PMID:25937687

  11. Emotion recognition method using entropy analysis of EEG signals

    Directory of Open Access Journals (Sweden)

    Seyyed Abed Hosseini

    2011-08-01

    Full Text Available This paper proposes an emotion recognition system using EEG signals, therefore a new approach to emotion state analysis by approximate (ApEn and wavelet entropy (WE is described. We have used EEG signals recorded during emotion in five channels (FP1, FP2, T3, T4 and Pz, under pictures induction environment (calm-neutral and negative excited for participants. After a brief introduction to the concept, the ApEn and WE were extracted from two different EEG time series. The result showed that, the classification accuracy in two emotion states was 73.25% using the support vector machine (SVM classifier. The simulations showed that the classification accuracy is good and the proposed methods are effective. During an emotion, the EEG is less complex compared to the normal, indicating reduction in active neuronal process in the brain.

  12. THE USE OF SLEEP EEG IN EPILEPTIC DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    刘秀琴; 周祥琴; 吴立文; 王平; 孙鹤阳

    1998-01-01

    Sleep and waklng EEG of 522 patients with epilepsy and various disease with attack nature were studled, EEG showed paroxysmal activities(PA) in 2]7 cases. PA appeared only during sleep in 96 cases, posirive rate of EEG diagnosis increased from 23.2 percent in waking recordings to 41.6 percent, Fifty of 97 benign childhood epilepsy with centrotemporal spike(5l.6%) had focal PA only during sleep. Two of 6 cases with Lennox-Gastaut syndrome showed tonic seizure and/or generalized paroxysmal fast activities,Seizure types of 15 patients were deigned hy interictal PA and ictal EEG during sleep. There was no corresponding relationship between seizure time(waking or sleep) and PA sensitivity to state of vigilance.

  13. 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…

  14. The Dynamics of EEG Entropy

    CERN Document Server

    Ignaccolo, M; Jernajczyk, W; Grigolini, P; West, B J

    2009-01-01

    EEG time series are analyzed using the diffusion entropy method. The resulting EEG entropy manifests short-time scaling, asymptotic saturation and an attenuated alpha-rhythm modulation. These properties are faithfully modeled by a phenomenological Langevin equation interpreted within a neural network context.

  15. EEG patterns in persons exposed to ionizing radiation as a result of the Chernobyl accident: part 1: conventional EEG analysis.

    Science.gov (United States)

    Loganovsky, K N; Yuryev, K L

    2001-01-01

    Prospective conventional EEG study was carried out 3-5 and 10-13 years after the Chernobyl accident (1986) in patients who had acute radiation sickness and in emergency workers in 1986 ("liquidators"). Control groups comprised healthy volunteers; veterans of the Afghanistan war with posttraumatic stress disorder; veterans with mild traumatic brain injury; and patients with dyscirculatory encephalopathy. In 3-5 years after irradiation, there were irritated EEG changes with paroxysmal activity shifted to the left frontotemporal region (cortical-limbic overactivation) that were transformed 10-13 years after irradiation toward a low-voltage EEG pattern with excess of fast (beta) and slow (delta) activity together with depression of alpha and theta activity (organic brain damage with inhibition of the cortical-limbic system). Quantitative EEG is likely to be very informative for investigation of dose-effect relationships.

  16. STUDY OF INTERICTAL E.E.G IN EPILEPSY

    Directory of Open Access Journals (Sweden)

    Usha Rani

    2013-04-01

    such basis unreliable. The study shows limitations of interictal EEG indicating that ictal records should be done for better information.

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

    NARCIS (Netherlands)

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

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

  18. 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.

  19. Songs induced mood recognition system using EEG signals.

    Science.gov (United States)

    Janvale, G B; Gawali, B W; Deore, Rakesh S; Mehrotra, Suresh C; Deshmukh, Sachin N; Marwale, Arun V

    2010-04-01

    Brain computer interfacing is a system that acquires and analyzes neural signals to create a communication channel directly between the brain and the computer. The EEG records the electrical fields generated by the nerve cells. With the help of Fourier Transformation the EEG signals are classified into four different frequency bands. The main purpose of the present paper is to report results related to classification of EEG signals of different people subjected to different conditions. The experiment has been done on 10 subjects having activities related to hearing music chosen from categories of patriotic, happy, romantic and sad songs along with relaxation activity. 19 electrodes have been used under (10-20) International Standard. The δ, θ α and β components of EEG signals to these activities have been determined. Different statistical methods including linear discriminate analysis have been tested for classification. Result of the Linear Discriminant Analysis (LDA) made four groups of all modes (Relaxation, Happy, Sad, Patriotic and Romantic Song) labeled group1, Group2, Group3 and Group4 of all ten electrodes for Delta, Theta, alpha and Beta frequencies. The study may be used for the development of activities induced mood recognition (AIMR) system from the EEG signal.

  20. Record of two species of Culicoides (Diptera, Ceratopogonidae) new for Madagascar and molecular study showing the paraphylies of the subgenus Oecacta and the Schultzei group.

    Science.gov (United States)

    Augot, D; Randrianambinintsoa, F J; Gasser, A; Depaquit, J

    2013-08-01

    Culicoides are vectors of diseases of Veterinary Medicine importance (bluetongue, African horse sickness, Schmallenberg virus) all over the world. In the present study, we report two species new for Madagascar: C. nevilli and C. enderleini. They belong to the Schultzei group which is sometimes classified in the subgenus Oecacta and sometimes in the subgenus Remmia, depending on authors. Consequently, we carried out a molecular cladistics of these groups based on cytochrome C oxidase subunit I mtDNA sequences. We processed the Malagasy specimens and some C. furens (the Oecacta type-species) caught in Florida and we analyzed their sequences and those available in Genbank: C. schultzei, C. oxystoma, C. festivipennis, C. brunnicans, C. kibunensis, C. truncorum and C. vexans. C. (Avaritia) imicola have been selected as an outgroup. The maximum parsimony analysis showed the paraphylies of the Schultzei group (=Remmia) and of the subgenus Oecacta if the first group is excluded from the latter. Our results underline the doubtful current classification and need to be validated by other molecular markers in the future.

  1. 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.

  2. Dry EEG Electrodes

    Directory of Open Access Journals (Sweden)

    M. A. Lopez-Gordo

    2014-07-01

    Full Text Available Electroencephalography (EEG emerged in the second decade of the 20th century as a technique for recording the neurophysiological response. Since then, there has been little variation in the physical principles that sustain the signal acquisition probes, otherwise called electrodes. Currently, new advances in technology have brought new unexpected fields of applications apart from the clinical, for which new aspects such as usability and gel-free operation are first order priorities. Thanks to new advances in materials and integrated electronic systems technologies, a new generation of dry electrodes has been developed to fulfill the need. In this manuscript, we review current approaches to develop dry EEG electrodes for clinical and other applications, including information about measurement methods and evaluation reports. We conclude that, although a broad and non-homogeneous diversity of approaches has been evaluated without a consensus in procedures and methodology, their performances are not far from those obtained with wet electrodes, which are considered the gold standard, thus enabling the former to be a useful tool in a variety of novel applications.

  3. 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.

  4. EEG Based Inference of Spatio-Temporal Brain Dynamics

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese

    . The main topic of this thesis is the localization of the EEG generators. This entails solving both a forward and an inverse problem. The inverse problem maps the EEG signal recorded on the scalp to its origin in the brain. It is a highly ill-posed problem which we tackle by employing a sparsity promoting...... ’spike and slab’ like method augmented with physiologically relevant source priors. The incorporated temporal and spatial priors exploit coherence between neighboring time samples and between neighboring source locations, respectively. We show that these augmentations effectively increase the source...... through the EEG forward model and assumes that the activity of the fMRI component overlaps spatially with the origin of the coupled EEG component....

  5. Analysis of Seizure EEG in Kindled Epileptic Rats

    Directory of Open Access Journals (Sweden)

    A. K. Sen

    2007-01-01

    Full Text Available Using wavelet analysis we have detected the presence of chirps in seizure EEG signals recorded from kindled epileptic rats. Seizures were induced by electrical stimulation of the amygdala and the EEG signals recorded from the amygdala were analyzed using a continuous wavelet transform. A time–frequency representation of the wavelet power spectrum revealed that during seizure the EEG signal is characterized by a chirp-like waveform whose frequency changes with time from the onset of seizure to its completion. Similar chirp-like time–frequency profiles have been observed in newborn and adult patients undergoing epileptic seizures. The global wavelet spectrum depicting the variation of power with frequency showed two dominant frequencies with the largest amounts of power during seizure. Our results indicate that a kindling paradigm in rats can be used as an animal model of human temporal lobe epilepsy to detect seizures by identifying chirp-like time–frequency variations in the EEG signal.

  6. Electroencephalographic (EEG) control of three-dimensional movement

    Science.gov (United States)

    McFarland, Dennis J.; Sarnacki, William A.; Wolpaw, Jonathan R.

    2010-06-01

    Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.

  7. Numerical simulation of EEG forward problem in centrosphere head model

    Directory of Open Access Journals (Sweden)

    HE Juan

    2013-02-01

    Full Text Available At present,EEG has become an important technical means in investigation of the brain function and clinical diagnosis.On the inverse problem of EEG,a lot of calculation of EEG forward problem is essential.In this paper,on the one hand,we develop a computing formula based on weighted residuals BEM; in center spherical head model,we compute the scalp potentials for different dipole position and orientation.On the other hand,we conduct simulation to EEG forward problem,and compare the numerical and analytical solutions of scalp potential.The results show that the weighted residual method has advantages of high computing efficiency and accuracy compared with FEM,DM.So it is widely used in computational mechanics.

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

    Directory of Open Access Journals (Sweden)

    Duffy Frank H

    2012-06-01

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

  9. Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke

    Science.gov (United States)

    Liu, Shuang; Guo, Jie; Meng, Jiayuan; Wang, Zhijun; Yao, Yang; Yang, Jiajia; Qi, Hongzhi; Ming, Dong

    2016-01-01

    Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution. PMID:27403202

  10. Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy.

    Science.gov (United States)

    Mohammadpoory, Zeynab; Nasrolahzadeh, Mahda; Haddadnia, Javad

    2017-08-01

    Epileptic seizure detection has been a complex task for both researchers and specialist in that the assessment of epilepsy is difficult because, electroencephalogram (EEG) signals are chaotic and non-stationary. This paper proposes a new method based on weighted visibility graph entropy (WVGE) to identify seizure from EEG signals. Single channel EEG signals are mapped onto the WVGs and WVGEs are calculated from these WVGs. Then some features are extracted of WVGEs and given to classifiers to investigate the performance of these features to classify the brain signals into three groups of normal (healthy), seizure free (interictal) and during a seizure (ictal) groups. Four popular classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree (DT) and, Naïve Bayes (NB) are used in this work. Experimental results show that the proposed method can classify normal, ictal and interictal groups with a high accuracy of 97%. This high accuracy index, which is obtained using just three features, is higher than those obtained by several previous works in which more nonlinear features were employed. Also, our method is fast and easy and may be helpful in different applications of automatic seizure detection such as online epileptic seizure detection. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

  11. EEG and dementia indicators in AIDS patients' Rorschach test

    Directory of Open Access Journals (Sweden)

    G. Fernandes do Prado

    1994-09-01

    Full Text Available We studied the EEG and Rorschach test (RT of nineteen AIDS patients and eight normal people in the same age group. Eight patients presented slow alpha rhythms (8 to 9 Hz; three, not-slow alpha rhythms (>9 to 13Hz; and eight, beta rhythms in background activity. Paroxystic activity, characterized by diffuse theta or delta waves, was present in eleven patients. We observed Oberholzer syndrome (organic dementia diagnosed by RT in ten patients and Piotrowski syndrome (organic dementia diagnosed by RT in eleven patients; six presented both. When considering only the group of AIDS patients, we did not observe a significant relation among slow alpha rhythm, not-slow alpha rhythm and the presence of paroxystic activity with the above-mentioned syndromes. AIDS patients with slow alpha rhythms showed a significantly greater number of Piotrowski syndrome dementia indicators when compared to normal individuals or those with slow alpha rhythms. We did not observe the same with Oberholzer syndrome.

  12. Headache in the paediatrics patients, clinical-EEG Correlation.

    Directory of Open Access Journals (Sweden)

    Marcelino Lizano Rabelo

    2011-12-01

    Full Text Available A descriptive study was made, with the objective to describe to a clinical group of variables epidemiologists and of laboratory of 108 patients to whom it was made to them and EEG and whose fundamental symptom was the headache; in the period January to December of 2009. The data was taken from the registry of patients of the neurophysiology department of Paediatric Hospital. The variables of the study were: age, sex, type of headache, results of the EEG, and characteristics of pathological EEG activity. The results were expressed in graphical and analyzed tables and of percentage form. The patients of 14 to 16 years predominated (40,7%, female patient (53,7%, clinically the observed recurrent acute migraine in 60 cases was the one that prevailed, as well as normality in the EEG (81,5%, the pathological cases we observed focal paroxysms in 15 patients (75% and focal alterations in 80% of the pathological EEG. Conclusions: In our environment the migraine in the paediatric patient is a frequent pathology that motivates the accomplishment of diverse studies among them the EEG, being this normal one in most of the cases and the non-specific alterations, the recurrent acute migraine and female patients prevailed.

  13. Mouse EEG spike detection based on the adapted continuous wavelet transform

    Science.gov (United States)

    Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

    2016-04-01

    Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

  14. Transient ischemic attacks: electrophysiological (conventional and topographic EEG) and radiological (CCT) evaluation.

    Science.gov (United States)

    Madkour, O; Elwan, O; Hamdy, H; Elwan, H; Abbas, A; Taher, M; Abdel-Kader, A

    1993-10-01

    The value of electrophysiological tests: conventional electroencephalography (EEG), topographic EEG analysis as well as computerized tomography (CT) in the diagnosis and evaluation of 25 patients with manifestations of transient ischemic attacks (TIA) in the domain of the carotid system was assessed. Normal CT was the rule in TIA patients except in 8% of the cases, where nonspecific changes of brain atrophy were described. Conventional EEG, topographic EEG and spectral analysis could detect abnormalities in 48%, 80% and 64% of TIA cases respectively. None of the abnormal EEG records could be missed by topographic EEG analysis. 32% of the records were diagnosed as abnormal by topographic EEG, while conventional EEG failed to detect abnormalities. Spectral analysis of the EEG results revealed a significant decrease regarding mean high limit alpha percent power, and a significant increase regarding mean low and high limit theta percent power, as well as a significant increase of the mean high limit of the slow activities (delta + theta)/fast activities (alpha + beta) percent power ratio in the TIA group as compared to the normal control group.

  15. First multigene analysis of Archamoebae (Amoebozoa: Conosa) robustly reveals its phylogeny and shows that Entamoebidae represents a deep lineage of the group.

    Science.gov (United States)

    Pánek, Tomáš; Zadrobílková, Eliška; Walker, Giselle; Brown, Matthew W; Gentekaki, Eleni; Hroudová, Miluše; Kang, Seungho; Roger, Andrew J; Tice, Alexander K; Vlček, Čestmír; Čepička, Ivan

    2016-05-01

    Archamoebae is an understudied group of anaerobic free-living or endobiotic protists that constitutes the major anaerobic lineage of the supergroup Amoebozoa. Hitherto, the phylogeny of Archamoebae was based solely on SSU rRNA and actin genes, which did not resolve relationships among the main lineages of the group. Because of this uncertainty, several different scenarios had been proposed for the phylogeny of the Archamoebae. In this study, we present the first multigene phylogenetic analysis that includes members of Pelomyxidae, and Rhizomastixidae. The analysis clearly shows that Mastigamoebidae, Pelomyxidae and Rhizomastixidae form a clade of mostly free-living, amoeboid flagellates, here called Pelobiontida. The predominantly endobiotic and aflagellated Entamoebidae represents a separate, deep-branching lineage, Entamoebida. Therefore, two unique evolutionary events, horizontal transfer of the nitrogen fixation system from bacteria and transfer of the sulfate activation pathway to mitochondrial derivatives, predate the radiation of recent lineages of Archamoebae. The endobiotic lifestyle has arisen at least three times independently during the evolution of the group. We also present new ultrastructural data that clarifies the primary divergence among the family Mastigamoebidae which had previously been inferred from phylogenetic analyses based on SSU rDNA.

  16. Using Brain Connectivity Measure of EEG Synchrostates for Discriminating Typical and Autism Spectrum Disorder

    CERN Document Server

    Jamal, Wasifa; Maharatna, Koushik; Kuyucu, Doga; Sicca, Federico; Billeci, Lucia; Apicella, Fabio; Muratori, Filippo

    2016-01-01

    In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data.

  17. Tobacco Smoking and the Resting Maternal Brain: A Preliminary Study of Frontal EEG

    Science.gov (United States)

    Wilbanks, Haley E.; Von Mohr, Mariana; Potenza, Marc N.; Mayes, Linda C.; Rutherford, Helena J.V.

    2016-01-01

    Tobacco smoking has been attributed to a wide range of detrimental health consequences for both women and their children. In addition to its known physical health effects, smoking may also impact maternal neural responses and subsequent caregiving behavior. To begin investigating this issue, we employed electroencephalography (EEG) to examine resting neural oscillations of tobacco-smoking mothers (n = 35) and non-smoking mothers (n = 35). We examined seven EEG frequency bands recorded from frontal electrode sites (delta, theta, alpha, alpha1, alpha2, beta, and gamma). While no between-group differences were present in high-frequency bands (alpha2, beta, gamma), smokers showed greater spectral power in low-frequency bands (delta, theta, alpha, alpha1) compared to non-smokers. This increased power in low-frequency bands of tobacco-smoking mothers is consistent with a less aroused state and may be one mechanism through which smoking might affect the maternal brain and caregiving behavior. PMID:27354838

  18. The interrelated effect of sleep and learning in dogs (Canis familiaris); an EEG and behavioural study

    Science.gov (United States)

    Kis, Anna; Szakadát, Sára; Gácsi, Márta; Kovács, Enikő; Simor, Péter; Török, Csenge; Gombos, Ferenc; Bódizs, Róbert; Topál, József

    2017-01-01

    The active role of sleep in memory consolidation is still debated, and due to a large between-species variation, the investigation of a wide range of different animal species (besides humans and laboratory rodents) is necessary. The present study applied a fully non-invasive methodology to study sleep and memory in domestic dogs, a species proven to be a good model of human awake behaviours. Polysomnography recordings performed following a command learning task provide evidence that learning has an effect on dogs’ sleep EEG spectrum. Furthermore, spectral features of the EEG were related to post-sleep performance improvement. Testing an additional group of dogs in the command learning task revealed that sleep or awake activity during the retention interval has both short- and long-term effects. This is the first evidence to show that dogs’ human-analogue social learning skills might be related to sleep-dependent memory consolidation. PMID:28165489

  19. Tobacco Smoking and the Resting Maternal Brain: A Preliminary Study of Frontal EEG.

    Science.gov (United States)

    Wilbanks, Haley E; Von Mohr, Mariana; Potenza, Marc N; Mayes, Linda C; Rutherford, Helena J V

    2016-06-01

    Tobacco smoking has been attributed to a wide range of detrimental health consequences for both women and their children. In addition to its known physical health effects, smoking may also impact maternal neural responses and subsequent caregiving behavior. To begin investigating this issue, we employed electroencephalography (EEG) to examine resting neural oscillations of tobacco-smoking mothers (n = 35) and non-smoking mothers (n = 35). We examined seven EEG frequency bands recorded from frontal electrode sites (delta, theta, alpha, alpha1, alpha2, beta, and gamma). While no between-group differences were present in high-frequency bands (alpha2, beta, gamma), smokers showed greater spectral power in low-frequency bands (delta, theta, alpha, alpha1) compared to non-smokers. This increased power in low-frequency bands of tobacco-smoking mothers is consistent with a less aroused state and may be one mechanism through which smoking might affect the maternal brain and caregiving behavior.

  20. TMS-evoked changes in brain-state dynamics quantified by using EEG data.

    Science.gov (United States)

    Mutanen, Tuomas; Nieminen, Jaakko O; Ilmoniemi, Risto J

    2013-01-01

    To improve our understanding of the combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) method in general, it is important to study how the dynamics of the TMS-modulated brain activity differs from the dynamics of spontaneous activity. In this paper, we introduce two quantitative measures based on EEG data, called mean state shift (MSS) and state variance (SV), for evaluating the TMS-evoked changes in the brain-state dynamics. MSS quantifies the immediate TMS-elicited change in the brain state, whereas SV shows whether the rate at which the brain state changes is modulated by TMS. We report a statistically significant increase for a period of 100-200 ms after the TMS pulse in both MSS and SV at the group level. This indicates that the TMS-modulated brain state differs from the spontaneous one. Moreover, the TMS-modulated activity is more vigorous than the natural activity.

  1. Qualitative and quantitative EEG in psychotic children.

    Science.gov (United States)

    Itil, T M; Simeon, J; Coffin, C

    1976-05-01

    The EEGs of hospitalized psychotic boys were analyzed quantitatively by means of visual evaluation, analog frequency analysis, and digital computer period analysis and were compared with those of age- and sex-matched normals. Visual evaluation of the records demonstrated that psychotic children have significantly more beta activity as well as fewer alpha bursts than normal controls. EEG analog frequency analysis showed that psychotic children have a greater percentage of total voltage in the 3-5 cps and 13-33 cps bands, while they show less voltage in the 6-12 cps bands as compared with normal controls. Digital computer period analysis demonstrated more slow, less alpha, and more fast activity, as well as a greater average frequency and frequency deviation in both the primary wave and first derivative measurements in psychotic children than normals, while normals showed a trend towards higher amplitude and amplitude variability. The similarity of the EEG differences between psychotic and normal children to those differences observed between adult chronic schizophrenics and normals, as well as to those between children of "high risk" for becoming schizophrenic and controls, suggests that the above described findings are characteristic for the pathophysiology of schizophrenia.

  2. A distinct group of CpG islands shows differential DNA methylation between replicas of the same cell line in vitro.

    Science.gov (United States)

    Cocozza, Sergio; Scala, Giovanni; Miele, Gennaro; Castaldo, Imma; Monticelli, Antonella

    2013-10-10

    CpG dinucleotide-rich genomic DNA regions, known as CpG islands (CGIs), can be methylated at their cytosine residues as an epigenetic mark that is stably inherited during cell mitosis. Differentially methylated regions (DMRs) are genomic regions showing different degrees of DNA methylation in multiple samples. In this study, we focused our attention on CGIs showing different DNA methylation between two culture replicas of the same cell line. We used methylation data of 35 cell lines from the Encyclopedia of DNA Elements (ENCODE) consortium to identify CpG islands that were differentially methylated between replicas of the same cell line and denoted them Inter Replicas Differentially Methylated CpG islands (IRDM-CGIs). We identified a group of IRDM-CGIs that was consistently shared by different cell lines, and denoted it common IRDM-CGIs. X chromosome CGIs were overrepresented among common IRDM-CGIs. Autosomal IRDM-CGIs were preferentially located in gene bodies and intergenic regions had a lower G + C content, a smaller mean length, and a reduced CpG percentage. Functional analysis of the genes associated with autosomal IRDM-CGIs showed that many of them are involved in DNA binding and development. Our results show that several specific functional and structural features characterize common IRDM-CGIs. They may represent a specific subset of CGIs that are more prone to being differentially methylated for their intrinsic characteristics.

  3. EEG after sleep deprivation is a sensitive tool in the first diagnosis of idiopathic generalized but not focal epilepsy.

    Science.gov (United States)

    Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa

    2016-01-01

    Electroencephalography (EEG) is an essential tool in the diagnosis of epilepsy. EEG after sleep deprivation might increase the likelihood of finding specific epileptiform abnormalities. However conflicting data exist concerning the sensitivity and specificity of this method. We aimed to evaluate the role of EEG after sleep deprivation in the first diagnosis of epilepsy. We analyzed retrospectively the medical histories of patients who underwent at least one unspecific standard EEG and a subsequent EEG after sleep deprivation during the time period from 2001 to 2014 at the University Hospital Zurich because of suspected epilepsy. Out of 237 patients who fulfilled all inclusion criteria, 69 were finally diagnosed with epilepsy. Seventeen of them showed interictal epileptiform patterns in EEGs after sleep deprivation, giving this method an overall sensitivity of 25%. Sensitivity of EEG after sleep deprivation was superior in patients with primary generalized epilepsies compared to patients with focal epilepsies (64% vs. 17%, p=0.0011). Overall EEG after sleep deprivation was not more sensitive than a subsequent repeated standard EEG in a subgroup of 55 patients (22% vs. 9%; p=0.065). After an unspecific standard EEG, EEG after sleep deprivation is a useful tool to increase diagnostic sensitivity in patients with idiopathic generalized epilepsy but not in those with focal epilepsy. This study provides further evidence about the usefulness of EEG after sleep deprivation as an additional diagnostic tool in epilepsy. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  4. Spectral Analysis of EEG in Familial Alzheimer’s Disease with E280A Presenilin-1 Mutation Gene

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    Rene Rodriguez

    2014-01-01

    Full Text Available To evaluate the hypothesis that quantitative EEG (qEEG analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects, an asymptomatic carrier (ACr group (21 subjects, with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D2 was calculated between groups. To evaluate the diagnostic efficiency of this statistic D2, the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D2 using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89 and between AD probable and ACr groups (area ROC = 0.91. This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

  5. Consumer-grade EEG devices: are they usable for control tasks?

    OpenAIRE

    2016-01-01

    We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high var...

  6. 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

  7. Small-world Characteristics of EEG Patterns in Post-Anoxic Encephalopathy

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    Martijn eBeudel

    2014-06-01

    Full Text Available Post-Anoxic Encephalopathy (PAE has a heterogenous outcome which is difficult to predict. At present, it is possible to predict poor outcome using somatosensory evoked potentials (SSEP in only a minority of the patients at an early stage. In addition, it remains difficult to predict good outcome at an early stage. Network architecture, as can be quantified with continuous electroencephalography (cEEG, may serve as a candidate measure for predicting neurological outcome. Here we explore whether cEEG monitoring can be used to detect the integrity of neural network architecture in patients with PAE after cardiac arrest. From 56 patients with PAE treated with mild therapeutic hypothermia (MTH, 19-channel cEEG data was recorded starting as soon as possible after cardiac arrest. Adjacency matrices of shared frequencies between 1 and 25 Hz of the EEG channels were obtained using Fourier transformations. Number of network nodes and connections, clustering coefficient (C, average path length (L and small-world index (SWI were derived. Outcome was quantified by the best Cerebral Performance Category (CPC-score within 6 months. Compared to non-survivors, survivors showed significantly more nodes and connections. L was significantly higher and C and SWI were significantly lower in the survivor group than in the non-survivor group. The number of nodes, connections and the L negatively correlated with the CPC-score. C and SWI correlated positively with the CPC-score. The combination of number of nodes, connections, C and L showed the most significant difference and correlation between survivors and non-survivors and CPC-score. Our data might implicate that non-survivors have insufficient distribution and differentiation of neural activity for regaining normal brain function. These network differences, already present during hypothermia, might be further developed as early prognostic markers. The predictive values are however still inferior to current practice

  8. High-resolution EEG (HR-EEG) and magnetoencephalography (MEG).

    Science.gov (United States)

    Gavaret, M; Maillard, L; Jung, J

    2015-03-01

    High-resolution EEG (HR-EEG) and magnetoencephalography (MEG) allow the recording of spontaneous or evoked electromagnetic brain activity with excellent temporal resolution. Data must be recorded with high temporal resolution (sampling rate) and high spatial resolution (number of channels). Data analyses are based on several steps with selection of electromagnetic signals, elaboration of a head model and use of algorithms in order to solve the inverse problem. Due to considerable technical advances in spatial resolution, these tools now represent real methods of ElectroMagnetic Source Imaging. HR-EEG and MEG constitute non-invasive and complementary examinations, characterized by distinct sensitivities according to the location and orientation of intracerebral generators. In the presurgical assessment of drug-resistant partial epilepsies, HR-EEG and MEG can characterize and localize interictal activities and thus the irritative zone. HR-EEG and MEG often yield significant additional data that are complementary to other presurgical investigations and particularly relevant in MRI-negative cases. Currently, the determination of the epileptogenic zone and functional brain mapping remain rather less well-validated indications. In France, in 2014, HR-EEG is now part of standard clinical investigation of epilepsy, while MEG remains a research technique.

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

    Science.gov (United States)

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

    2016-06-13

    Electroencephalography (EEG) is a flexible and accessible tool with excellent temporal resolution but with a spatial resolution hampered by volume conduction. Reconstruction of the cortical sources of measured EEG activity partly alleviates this problem and effectively turns EEG into a brain imaging device. The quality of the source reconstruction depends on the forward model which details head geometry and conductivities of different head compartments. These person-specific factors are complex to determine, requiring detailed knowledge of the subject's anatomy and physiology. In this proof-of-concept study, we show that, even when anatomical knowledge is unavailable, a suitable forward model can be estimated directly from the EEG. We propose a data-driven approach that provides a low-dimensional parametrization of head geometry and compartment conductivities, built using a corpus of forward models. Combined with only a recorded EEG signal, we are able to estimate both the brain sources and a person-specific forward model by optimizing this parametrization. We thus not only solve an inverse problem, but also optimize over its specification. Our work demonstrates that personalized EEG brain imaging is possible, even when the head geometry and conductivities are unknown.

  10. 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.

  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. 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 =…

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

    Science.gov (United States)

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

    2011-01-01

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

  14. 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 =…

  15. 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.

  16. No evidence for mirror system dysfunction in schizophrenia from a multimodal TMS/EEG study.

    Science.gov (United States)

    Andrews, Sophie C; Enticott, Peter G; Hoy, Kate E; Thomson, Richard H; Fitzgerald, Paul B

    2015-08-30

    Dysfunctional mirror neuron systems have been proposed to contribute to the social cognitive deficits observed in schizophrenia. A few studies have explored mirror systems in schizophrenia using various techniques such as TMS (levels of motor resonance) or EEG (levels of mu suppression), with mixed results. This study aimed to use a novel multimodal approach (i.e. concurrent TMS and EEG) to further investigate mirror systems and social cognition in schizophrenia. Nineteen individuals with schizophrenia or schizoaffective disorder and 19 healthy controls participated. Single-pulse TMS was applied to M1 during the observation of hand movements designed to elicit mirror system activity. Single EEG electrodes (C3, CZ, C4) recorded brain activity. Participants also completed facial affect recognition and theory of mind tasks. The schizophrenia group showed significant deficits in facial affect recognition and higher level theory of mind compared to healthy controls. A significant positive relationship was revealed between mu suppression and motor resonance for the overall sample, indicating concurrent validity of these measures. Levels of mu suppression and motor resonance were not significantly different between groups. These findings indicate that in stable outpatients with schizophrenia, mirror system functioning is intact, and therefore their social cognitive difficulties may be caused by alternative pathophysiology. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  17. Correlation of Visuospatial Ability and EEG Slowing in Patients with Parkinson's Disease

    Science.gov (United States)

    Meyer, Antonia; Chaturvedi, Menorca; Hatz, Florian; Gschwandtner, Ute

    2017-01-01

    Background. Visuospatial dysfunction is among the first cognitive symptoms in Parkinson's disease (PD) and is often predictive for PD-dementia. Furthermore, cognitive status in PD-patients correlates with quantitative EEG. This cross-sectional study aimed to investigate the correlation between EEG slowing and visuospatial ability in nondemented PD-patients. Methods. Fifty-seven nondemented PD-patients (17 females/40 males) were evaluated with a comprehensive neuropsychological test battery and a high-resolution 256-channel EEG was recorded. A median split was performed for each cognitive test dividing the patients sample into either a normal or lower performance group. The electrodes were split into five areas: frontal, central, temporal, parietal, and occipital. A linear mixed effects model (LME) was used for correlational analyses and to control for confounding factors. Results. Subsequently, for the lower performance, LME analysis showed a significant positive correlation between ROCF score and parietal alpha/theta ratio (b = .59, p = .012) and occipital alpha/theta ratio (b = 0.50, p = .030). No correlations were found in the group of patients with normal visuospatial abilities. Conclusion. We conclude that a reduction of the parietal alpha/theta ratio is related to visuospatial impairments in PD-patients. These findings indicate that visuospatial impairment in PD-patients could be influenced by parietal dysfunction. PMID:28348918

  18. [Correlation between EEG and neuroimaging].

    Science.gov (United States)

    Tobimatsu, Shozo

    2012-01-01

    The present state of knowledge of physiological mechanisms underlying nonepileptiform EEG abnormalities is reviewed to clarify the correlation between EEG and neuroimaging. Focal and widespread slow waves, background abnormalities, and bursts of rhythmic slow activity are discussed. EEG phenomena were correlated with lesion size, location, type (white matter vs. gray matter, high density vs. low density), and mass effect. Clinical and experimental accumulated over the past five decades suggest that polymorphic slow activity is generated in cerebral cortex by layers of pyramidal cells and is probably due to partial deafferentation from subcortical areas. Unilateral background activity changes are probably thalamic dysfunction, and bilateral paroxysmal slow activity is due to abnormal thalamocortical circuits combined with cortical pathology. Paroxysmal discharges indicate the presence of epilepsy with possible brain lesion(s). The EEG is a functional test and provides us complementary information to neuroimaging studies.

  19. Independent EEG sources are dipolar

    National Research Council Canada - National Science Library

    Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

    2012-01-01

    Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG...

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

    Directory of Open Access Journals (Sweden)

    Bin-bin SUN

    2015-03-01

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

  1. Epileptic EEG: a comprehensive study of nonlinear behavior.

    Science.gov (United States)

    Daneshyari, Moayed; Kamkar, L Lily; Daneshyari, Matin

    2010-01-01

    In this study, the nonlinear properties of the electroencephalograph (EEG) signals are investigated by comparing two sets of EEG, one set for epileptic and another set for healthy brain activities. Adopting measures of nonlinear theory such as Lyapunov exponent, correlation dimension, Hurst exponent, fractal dimension, and Kolmogorov entropy, the chaotic behavior of these two sets is quantitatively computed. The statistics for the two groups of all measures demonstrate the differences between the normal healthy group and epileptic one. The statistical results along with phase-space diagram verify that brain under epileptic seizures possess limited trajectory in the state space than in healthy normal state, consequently behaves less chaotically compared to normal condition.

  2. Comparative Analysis of EEG Signals Based on Complexity Measure

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The aim of this study is to identify the functions and states of the brains according to the values of the complexity measure of the EEG signals. The EEG signals of 30 normal samples and 30 patient samples are collected. Based on the preprocessing for the raw data, a computational program for complexity measure is compiled and the complexity measures of all samples are calculated. The mean value and standard error of complexity measure of control group is as 0.33 and 0.10, and the normal group is as 0.53 an...

  3. EEG oscillatory states: universality, uniqueness and specificity across healthy-normal, altered and pathological brain conditions.

    Directory of Open Access Journals (Sweden)

    Alexander A Fingelkurts

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

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

    Science.gov (United States)

    Fingelkurts, Alexander A.; Fingelkurts, Andrew A.

    2014-01-01

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

  5. [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.

  6. Mice repeatedly exposed to Group-A β-Haemolytic Streptococcus show perseverative behaviors, impaired sensorimotor gating, and immune activation in rostral diencephalon.

    Science.gov (United States)

    Macrì, Simone; Ceci, Chiara; Onori, Martina Proietti; Invernizzi, Roberto William; Bartolini, Erika; Altabella, Luisa; Canese, Rossella; Imperi, Monica; Orefici, Graziella; Creti, Roberta; Margarit, Immaculada; Magliozzi, Roberta; Laviola, Giovanni

    2015-08-25

    Repeated exposure to Group-A β-Haemolytic Streptococcus (GAS) may constitute a vulnerability factor in the onset and course of pediatric motor disturbances. GAS infections/colonization can stimulate the production of antibodies, which may cross the blood brain barrier, target selected brain areas (e.g. basal ganglia), and exacerbate motor alterations. Here, we exposed developing SJL male mice to four injections with a GAS homogenate and evaluated the following domains: motor coordination; general locomotion; repetitive behaviors; perseverative responses; and sensorimotor gating (pre-pulse inhibition, PPI). To demonstrate that behavioral changes were associated with immune-mediated brain alterations, we analyzed, in selected brain areas, the presence of infiltrates and microglial activation (immunohistochemistry), monoamines (HPLC), and brain metabolites (in vivo Magnetic Resonance Spectroscopy). GAS-exposed mice showed increased repetitive and perseverative behaviors, impaired PPI, and reduced concentrations of serotonin in prefrontal cortex, a brain area linked to the behavioral domains investigated, wherein they also showed remarkable elevations in lactate. Active inflammatory processes were substantiated by the observation of infiltrates and microglial activation in the white matter of the anterior diencephalon. These data support the hypothesis that repeated GAS exposure may elicit inflammatory responses in brain areas involved in motor control and perseverative behavior, and result in phenotypic abnormalities.

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  9. Analysis of Age Dependent Effects of Heat Stress on EEG Frequency Components in Rats

    Institute of Scientific and Technical Information of China (English)

    RAKESH KUMAR SINHA

    2009-01-01

    Objective To demonstrate changes in different frequencies of cerebral electrical activity or electroencephalogram (EEG) following exposure to high environmental heat in three different age groups of freely moving rats. Methods Rats were divided into three groups (i) acute heat stress - subjected to a single exposure for four hours at 38 ℃; (ii) chronic heat stress -exposed for 21 days daily for one hour at 38 ℃, and (iii) handling control groups. The digital polygraphic sleep-EEG recordings were performed just after the heat exposure from acute stressed rats and on 22nd day from chronic stressed rats by simultaneous recording of cortical EEG EOG (electrooculogram), and EMG (electromyogram). Further, power spectrum analyses were performed to analyze the effects of heat stress. Results The frequency analysis of EEG signals following exposure to high environmental heat revealed that in all three age groups of rats, changes in higher frequency components (β2) were significant in all sleep-wake states following both acute and chronic heat stress conditions. After exposure to acute heat, significant changes in EEG frequencies with respect to their control groups were observed, which were reversed partly or fully in four hours of EEG recording. On the other hand, due to repetitive chronic exposure to hot environment, adaptive and long-term changes in EEG frequency patterns were observed. Conclusion The present study has exhibited that the cortical EEG is sensitive to environmental heat and alterations in EEG frequencies in different sleep-wake states due to heat stress can be differentiated efficiently by EEG power spectrum analysis.

  10. The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study.

    Science.gov (United States)

    Neuner, Irene; Arrubla, Jorge; Werner, Cornelius J; Hitz, Konrad; Boers, Frank; Kawohl, Wolfram; Shah, N Jon

    2014-01-01

    Electroencephalography (EEG) frequencies have been linked to specific functions as an "electrophysiological signature" of a function. A combination of oscillatory rhythms has also been described for specific functions, with or without predominance of one specific frequency-band. In a simultaneous fMRI-EEG study at 3 T we studied the relationship between the default mode network (DMN) and the power of EEG frequency bands. As a methodological approach, we applied Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) and dual regression analysis for fMRI resting state data. EEG power for the alpha, beta, delta and theta-bands were extracted from the structures forming the DMN in a region-of-interest approach by applying Low Resolution Electromagnetic Tomography (LORETA). A strong link between the spontaneous BOLD response of the left parahippocampal gyrus and the delta-band extracted from the anterior cingulate cortex was found. A positive correlation between the beta-1 frequency power extracted from the posterior cingulate cortex (PCC) and the spontaneous BOLD response of the right supplementary motor cortex was also established. The beta-2 frequency power extracted from the PCC and the precuneus showed a positive correlation with the BOLD response of the right frontal cortex. Our results support the notion of beta-band activity governing the "status quo" in cognitive and motor setup. The highly significant correlation found between the delta power within the DMN and the parahippocampal gyrus is in line with the association of delta frequencies with memory processes. We assumed "ongoing activity" during "resting state" in bringing events from the past to the mind, in which the parahippocampal gyrus is a relevant structure. Our data demonstrate that spontaneous BOLD fluctuations within the DMN are associated with different EEG-bands and strengthen the conclusion that this network is characterized by a specific

  11. The default mode network and EEG regional spectral power: a simultaneous fMRI-EEG study.

    Directory of Open Access Journals (Sweden)

    Irene Neuner

    Full Text Available Electroencephalography (EEG frequencies have been linked to specific functions as an "electrophysiological signature" of a function. A combination of oscillatory rhythms has also been described for specific functions, with or without predominance of one specific frequency-band. In a simultaneous fMRI-EEG study at 3 T we studied the relationship between the default mode network (DMN and the power of EEG frequency bands. As a methodological approach, we applied Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC and dual regression analysis for fMRI resting state data. EEG power for the alpha, beta, delta and theta-bands were extracted from the structures forming the DMN in a region-of-interest approach by applying Low Resolution Electromagnetic Tomography (LORETA. A strong link between the spontaneous BOLD response of the left parahippocampal gyrus and the delta-band extracted from the anterior cingulate cortex was found. A positive correlation between the beta-1 frequency power extracted from the posterior cingulate cortex (PCC and the spontaneous BOLD response of the right supplementary motor cortex was also established. The beta-2 frequency power extracted from the PCC and the precuneus showed a positive correlation with the BOLD response of the right frontal cortex. Our results support the notion of beta-band activity governing the "status quo" in cognitive and motor setup. The highly significant correlation found between the delta power within the DMN and the parahippocampal gyrus is in line with the association of delta frequencies with memory processes. We assumed "ongoing activity" during "resting state" in bringing events from the past to the mind, in which the parahippocampal gyrus is a relevant structure. Our data demonstrate that spontaneous BOLD fluctuations within the DMN are associated with different EEG-bands and strengthen the conclusion that this network is characterized by a specific

  12. Knowing when not to swing: EEG evidence that enhanced perception-action coupling underlies baseball batter expertise.

    Science.gov (United States)

    Muraskin, Jordan; Sherwin, Jason; Sajda, Paul

    2015-12-01

    Given a decision that requires less than half a second for evaluating the characteristics of the incoming pitch and generating a motor response, hitting a baseball potentially requires unique perception-action coupling to achieve high performance. We designed a rapid perceptual decision-making experiment modeled as a Go/No-Go task yet tailored to reflect a real scenario confronted by a baseball hitter. For groups of experts (Division I baseball players) and novices (non-players), we recorded electroencephalography (EEG) while they performed the task. We analyzed evoked EEG single-trial variability, contingent negative variation (CNV), and pre-stimulus alpha power with respect to the expert vs. novice groups. We found strong evidence for differences in inhibitory processes between the two groups, specifically differential activity in supplementary motor areas (SMA), indicative of enhanced inhibitory control in the expert (baseball player) group. We also found selective activity in the fusiform gyrus (FG) and orbital gyrus in the expert group, suggesting an enhanced perception-action coupling in baseball players that differentiates them from matched controls. In sum, our results show that EEG correlates of decision formation can be used to identify neural markers of high-performance athletes. Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Show Time

    Institute of Scientific and Technical Information of China (English)

    2004-01-01

    <正> Story: Show Time!The whole class presents the story"Under the Sea".Everyone is so excited and happy.Both Leo and Kathy show their parentsthe characters of the play."Who’s he?"asks Kathy’s mom."He’s the prince."Kathy replies."Who’s she?"asks Leo’s dad."She’s the queen."Leo replieswith a smile.

  14. Snobbish Show

    Institute of Scientific and Technical Information of China (English)

    YIN PUMIN

    2010-01-01

    @@ The State Administration of Radio,Film and Television (SARFT),China's media watchdog,issued a new set of mles on June 9 that strictly regulate TV match-making shows,which have been sweeping the country's primetime programming. "Improper social and love values such as money worship should not be presented in these shows.Humiliation,verbal attacks and sex-implied vulgar content are not allowed" the new roles said.

  15. 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

  16. Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis.

    Directory of Open Access Journals (Sweden)

    Parameswaran Mahadeva Iyer

    Full Text Available Amyotrophic Lateral Sclerosis (ALS is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS.18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity.Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005. Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02. Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05.There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS.

  17. Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis

    Science.gov (United States)

    Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C.; Hardiman, Orla

    2015-01-01

    Background Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. Methods 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Results Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). Discussion There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS. PMID:26091258

  18. Multimodal EEG-MRI in the differential diagnosis of Alzheimer's disease and dementia with Lewy bodies

    Science.gov (United States)

    Colloby, Sean J.; Cromarty, Ruth A.; Peraza, Luis R.; Johnsen, Kristinn; Jóhannesson, Gísli; Bonanni, Laura; Onofrj, Marco; Barber, Robert; O'Brien, John T.; Taylor, John-Paul

    2016-01-01

    Differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) remains challenging; currently the best discriminator is striatal dopaminergic imaging. However this modality fails to identify 15–20% of DLB cases and thus other biomarkers may be useful. It is recognised electroencephalography (EEG) slowing and relative medial temporal lobe preservation are supportive features of DLB, although individually they lack diagnostic accuracy. Therefore, we investigated whether combined EEG and MRI indices could assist in the differential diagnosis of AD and DLB. Seventy two participants (21 Controls, 30 AD, 21 DLB) underwent resting EEG and 3 T MR imaging. Six EEG classifiers previously generated using support vector machine algorithms were applied to the present dataset. MRI index was derived from medial temporal atrophy (MTA) ratings. Logistic regression analysis identified EEG predictors of AD and DLB. A combined EEG-MRI model was then generated to examine whether there was an improvement in classification compared to individual modalities. For EEG, two classifiers predicted AD and DLB (model: χ2 = 22.1, df = 2, p EEG-MRI model showed greater prediction in AD and DLB (model: χ2 = 31.1, df = 3, p EEG and MRI, and may represent an alternative to dopaminergic imaging. PMID:27060340

  19. 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.

  20. Optimizing microsurgical skills with EEG neurofeedback

    Directory of Open Access Journals (Sweden)

    Benjamin Larry

    2009-07-01

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

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

    Science.gov (United States)

    Somers, Ben; Bertrand, Alexander

    2016-12-01

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

  2. Separation instead of filling up. Why does the EEG know only one rated power; Aufsplitten statt Auffuellen. Warum das EEG nur eine Bemessungsleistung kennt

    Energy Technology Data Exchange (ETDEWEB)

    Vollprecht, Jens [Becker Buettner Held, Berlin (Germany); Kahl, Hartmut [Stiftung Umweltenergierecht, Wuerzburg (Germany). Forschungsbereich Internationales Umweltenergierecht

    2013-02-15

    The new EEG 2012 distinguishes between rated power with respect to paragraph 3 No. 2a EEG and installed power with respect to paragraph No. 6 EEG 2012. The authors of the contribution under consideration report on the significance of the performance of a facility and use the calculation of the KWK bonus according paragraph 66 Sect. 1 No. 3 sentence 3 EEG 2009 in order to show that there only exist the two legally defined terms of performance. The selected example only is valid for plants which where brought on line before 1st January, 2009. However, the underlying fundamental considerations also have an enhanced relevance for the application of the EEG 2012.

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

    Directory of Open Access Journals (Sweden)

    Claudio eBabiloni

    2016-02-01

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

  4. Classification of spontaneous EEG signals in migraine

    Science.gov (United States)

    Bellotti, R.; De Carlo, F.; de Tommaso, M.; Lucente, M.

    2007-08-01

    We set up a classification system able to detect patients affected by migraine without aura, through the analysis of their spontaneous EEG patterns. First, the signals are characterized by means of wavelet-based features, than a supervised neural network is used to classify the multichannel data. For the feature extraction, scale-dependent and scale-independent methods are considered with a variety of wavelet functions. Both the approaches provide very high and almost comparable classification performances. A complete separation of the two groups is obtained when the data are plotted in the plane spanned by two suitable neural outputs.

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

    Science.gov (United States)

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

    2014-10-15

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

  6. EEG epoch selection: lack of alpha rhythm improves discrimination of Alzheimer's disease.

    Science.gov (United States)

    Fraga, Francisco J; Oliveira, Eliezyer F; Kanda, Paulo A M

    2016-08-01

    In this work we propose a detailed EEG epoch selection method and compare epochs with rare and abundant alpha rhythm (AR) of patients with Alzheimer's disease (AD) and normal controls. Epochs were classified as Dominant Alpha Scenario (DAS) and Rare Alpha Scenario (RAS) according to the AR percentage (energy within the 8-13 Hz bandwidth) in O1, O2 and Oz electrodes. Participants were divided into four groups: 17 DAS controls (N1), 15 DAS mild-AD patients (AD1), 12 RAS controls (N2) and 15 RAS mild-AD patients (AD2). We found out that scenario factor (DAS vs. RAS, two-way ANOVA) is significant over a great amount of electrode-bandwidth situations. Furthermore, one-way ANOVA showed significant differences between RAS AD and RAS controls in much more situations as compared to DAS. This is the first study using AD awake EEG reporting the decisive influence of alpha rhythm on epoch selection, where our results revealed that, contrary to what was initially expected, EEG epochs with poor alpha (RAS) discriminate mild AD much better than those presenting richer alpha content (DAS).

  7. Non-linear analysis of EEG and HRV signals during sleep.

    Science.gov (United States)

    Martin, Alejandro; Guerrero-Mora, Guillermina; Dorantes-Méndez, Guadalupe; Alba, Alfonso; Méndez, Martin O; Chouvarda, Ioanna

    2015-01-01

    The sleep phenomenon is a complex process that involves fluctuations of autonomic functions such as the blood pressure, temperature and brain function. These fluctuations change their properties through the different sleep stages with specific relations among the different systems. In order to understand the relation between the cardiovascular and central nervous system at the different sleep stages, we applied different non-linear methods to the energy of electroencephalographic signal (EEG) and the heart rate fluctuations. The EEG was divided in the Delta, Theta, Alpha and Beta frequency bands and the mean energy of these bands was computed at each heart rate interval. Thus, the non-linear relation was evaluated between the energy of the EEG bands and the heart rate fluctuations using Cross-Correlation, Cross-Sample Entropy and Recurrence Quantification Analysis in segments of 5 minutes grouped by sleep stage. The results showed that a relation exists between the changes of the energy in the Delta band and the Heart rate fluctuations.

  8. 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.

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

    Science.gov (United States)

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

  10. DNA barcoding resolves species complexes in Stigmella salicis and S. aurella species groups and shows additional cryptic speciation in S. salicis (Lepidoptera: Nepticulidae)

    NARCIS (Netherlands)

    Nieukerken, van E.J.; Mutanen, M.; Doorenweerd, C.

    2012-01-01

    We sequenced the mitochondrial barcoding marker COI and nuclear marker EF1-alpha for most Nordic and other European species of the Stigmella salicis and S. aurella species groups. In the S. salicis group both markers confirm the synonymy of S. lappovimella with S. zelleriella. Specimens previously i

  11. INCREASE OF THETA FREQUENCY IS ASSOCIATED WITH REDUCTION IN REGIONAL CEREBRAL BLOOD FLOW ONLY IN SUBJECTS WITH MILD COGNITIVE IMPAIRMENT WITH HIGHER UPPER ALPHA/LOW ALPHA EEG FREQUENCY POWER RATIO

    Directory of Open Access Journals (Sweden)

    Davide v Moretti

    2013-12-01

    Full Text Available Background: several biomarkers have been proposed for detecting Alzheimer's disease (AD in its earliest stages, that is, in the predementia stage. In an attempt to find noninvasive biomarkers, researchers have investigated the feasibility of neuroimaging tools, such as MRI, SPECT as well as neurophysiological measurements using EEG. Moreover, the increase of EEG alpha3/alpha2 frequency power ratio has been associated with AD-converters subjects with mild cognitive impairment (MCI.Objective: to study the association of alpha3/alpha2 frequency power ratio with regional cerebral blood flow (rCBF changes in subjects with MCI .Methods: 27 adult subjects with MCI underwent EEG recording and perfusion single-photon emission computed tomography (SPECT evaluation. The alpha3/alpha2 frequency power ratio was computed for each subject. Two groups were obtained according to the median values of alpha3/alpha2, at a cut-off of 1.17. Correlation between brain perfusion and EEG markers were detected.Results: subjects with higher alpha3/alpha2 frequency power ratio showed a constant trend to a lower perfusion than low alpha3/alpha2 group. The two groups were significantly different as about the hippocampal volume and correlation with the theta frequency activity.Conclusion: there is a complex interplay between cerebral blood flow, theta frequency activity and hippocampal volume in MCI patients with prodromal Alzheimer's disease, characterized by higher EEG alpha3 /alpha2 frequency power ratio.

  12. EROBATIC SHOW

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    Visitors look at plane models of the Commercial Aircraft Corp. of China, developer of the count,s first homegrown large passenger jet C919, during the Singapore Airshow on February 16. The biennial event is the largest airshow in Asia and one of the most important aviation and defense shows worldwide. A number of Chinese companies took part in the event during which Okay Airways, the first privately owned aidine in China, signed a deal to acquire 12 Boeing 737 jets.

  13. Continuous EEG in Critically Ill Children

    Directory of Open Access Journals (Sweden)

    Jonathan E Kurz

    2015-03-01

    Full Text Available Investigators from the Critical Care Continuous EEG Task Force of the American Clinical Neurophysiology Society reported a consensus statement on indications for the use of critical care continuous electroencephalographic monitoring (ccEEG in adults and children.

  14. EEG guidelines in the diagnosis of brain death.

    Science.gov (United States)

    Szurhaj, W; Lamblin, M-D; Kaminska, A; Sediri, H

    2015-03-01

    In France, for the determination and diagnostic validation of brain death the law requires either two EEG recordings separated by a 4-hour observation period, both showing electrocerebral inactivity; or cerebral angiography examination. Since EEG is available in most hospitals and clinics, it is often used in this indication, at the patient's bedside, especially in the context of organ donation. However, very precise methodology must be followed. The last French guidelines date back to 1989, before the development of digital EEG recording. We present the new guidelines from the Société de Neurophysiologie Clinique de Langue Française. Electrocerebral inactivity may be confirmed when a 30-minute good quality EEG recording shows complete electrocerebral silence, defined as no cerebral activity greater than 2 uV, having first ruled out the possible influence of sedative drugs, metabolic disorders or hypothermia. In the presence of sedative drugs, CT brain angiography will be the gold standard test for this diagnosis. In the newborn, the utmost caution is indicated since electrocerebral inactivity can be observed in the absence of cerebral death. In the infant, the criterion for the observation period to be respected between both EEG recordings needs to be more clearly refined.

  15. Saethre-Chotzen syndrome: a clinical, EEG and neuroradiological study.

    Science.gov (United States)

    Elia, M; Musumeci, S A; Ferri, R; Greco, D; Romano, C; Del Gracco, S; Stefanini, M C

    1996-11-01

    Saethre-Chotzen syndrome is a form of acrocephalosyndactyly with autosomal dominant inheritance, characterized by craniosynostosis, facial asymmetry, palpebral ptosis, deviated nasal septum, partial cutaneous syndactyly, and various skeletal abnormalities. We studied in detail the neurological, EEG, and neuroradiological features of a group of 11 (6 male, 5 female) patients with Saethre-Chotzen syndrome. Four subjects were affected by seizures; they had paroxysmal EEG abnormalities, and gross neuroimaging revealed destructive brain lesions or malformations. Our findings suggest that CNS involvement in Saethre-Chotzen syndrome might be more severe than previously reported and support the wider use of neurophysiological and neuroimaging techniques in the study of children with this syndrome.

  16. Combining TMS and EEG offers new prospects in cognitive neuroscience.

    Science.gov (United States)

    Miniussi, Carlo; Thut, Gregor

    2010-01-01

    The combination of brain stimulation by transcranial magnetic stimulation (TMS) with simultaneous electroencephalographic (EEG) imaging has become feasible due to recent technical developments. The TMS-EEG integration provides real-time information on cortical reactivity and connectivity through the analysis of TMS-evoked potentials (TEPs), and how functional activity links to behavior through the study of TMS-induced modulations thereof. It reveals how these effects vary as a function of neuronal state, differing between individuals and patient groups but also changing rapidly over time during task performance. This review discusses the wide range of possible TMS-EEG applications and what new information may be gained using this technique on the dynamics of brain functions, hierarchical organization, and cortical connectivity, as well as on TMS action per se. An advance in the understanding of these issues is timely and promises to have a substantial impact on many areas of clinical and basic neuroscience.

  17. Using of the interictal EEGs for epilepsy diagnosing

    Science.gov (United States)

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

    2015-12-01

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

  18. Brain perfusion SPECT and EEG findings in Rett syndrome

    Energy Technology Data Exchange (ETDEWEB)

    Lappalainen, R. [Children`s Castle Hospital, Dept. of Child Neurology, Helsinki (Finland); Liewendahl, K.; Nikkinen, P. [Univ. Central Hospital, Division of Nuclear Medicine, Laboratory Dept., Helsinki (Finland); Sainio, K.; Riikonen, R.S. [Univ. Central Hospital, Child Neurology, Helsinki (Finland)

    1997-01-01

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

  19. Non-Ceruloplasmin Copper Distinguishes A Distinct Subtype of Alzheimer's Disease: A Study of EEG-Derived Brain Activity.

    Science.gov (United States)

    Tecchio, Franca; Vecchio, Fabrizio; Ventriglia, Mariacarla; Porcaro, Camillo; Miraglia, Francesca; Siotto, Mariacristina; Rossini, Paolo M; Rongioletti, Mauro; Squitti, Rosanna

    2016-01-01

    Meta-analyses show that percentages of non-Cp-Cu-copper that is not bound to ceruloplasmin (also known as 'free' copper)-in serum are higher in Alzheimer's disease (AD) patients. Genetic heterogeneity in AD patients stratified on the basis of non-Cp-Cu cut-off sustains the existence of a copper AD metabolic subtype. Non-Cp-Cu abnormalities correlated with alterations of electroencephalographic rhythms (EEG). We aimed to determine whether an EEG-derived brain cortical rhythm's heterogeneity between two AD groups stratified on the basis of a copper marker. We assessed levels of copper, ceruloplasmin, Non-Cp-Cu, and the APOE4 genotype in 67 AD patients and compared resting EEG-derived eLORETA cortical rhythms between AD groups stratified in terms of 'Normal' and 'High' non-Cp-Cu. The High non-Cp-Cu group experienced a lower power in all bands (0.2-48 Hz) in the parietal cortices (p=0.019) and a more limited alpha band (8-13 Hz) power in the sensory lobes (temporal, occipital, and parietal p>0.05 consistently) than the Normal non-Cp-Cu AD group. When corrected for MMSE, the non-Cp-Cu levels correlated with a reduction of high-frequency brain activity (from high alpha to gamma, 10.5-48 Hz). This neurophysiological heterogeneity in EEG-derived brain cortical rhythms between the two AD groups sustains a copper AD metabolic subtype; Non-Cp-Cu is a marker of this copper AD.

  20. 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.

  1. Evaluating the Performance of BSBL Methodology for EEG Source Localization On a Realistic Head Model

    CERN Document Server

    Saha, Sajib; Nesterets, Ya I; Tahtali, M; de Hoog, Frank; Gureyev, T E

    2015-01-01

    Source localization in EEG represents a high dimensional inverse problem, which is severely ill-posed by nature. Fortunately, sparsity constraints have come into rescue as it helps solving the ill-posed problems when the signal is sparse. When the signal has a structure such as block structure, consideration of block sparsity produces better results. Knowing sparse Bayesian learning is an important member in the family of sparse recovery, and a superior choice when the projection matrix is highly coherent (which is typical the case for EEG), in this work we evaluate the performance of block sparse Bayesian learning (BSBL) method for EEG source localization. It is already accepted by the EEG community that a group of dipoles rather than a single dipole are activated during brain activities; thus, block structure is a reasonable choice for EEG. In this work we use two definitions of blocks: Brodmann areas and automated anatomical labelling (AAL), and analyze the reconstruction performance of BSBL methodology fo...

  2. EEG and MEG: relevance to neuroscience

    NARCIS (Netherlands)

    Lopes da Silva, F.

    2013-01-01

    To understand dynamic cognitive processes, the high time resolution of EEG/MEG is invaluable. EEG/MEG signals can play an important role in providing measures of functional and effective connectivity in the brain. After a brief description of the foundations and basic methodological aspects of EEG/M

  3. Recognition of wake-sleep stage 1 multichannel eeg patterns using spectral entropy features for drowsiness detection.

    Science.gov (United States)

    Sriraam, N; Padma Shri, T K; Maheshwari, Uma

    2016-09-01

    Electroencephalographic (EEG) activity recorded during the entire sleep cycle reflects various complex processes associated with brain and exhibits a high degree of irregularity through various stages of sleep. The identification of transition from wakefulness to stage1 sleep is a challenging area of research for the biomedical community. In this paper, spectral entropy (SE) is used as a complexity measure to quantify irregularities in awake and stage1 sleep of 8-channel sleep EEG data from the polysomnographic recordings of ten healthy subjects. The SE measures of awake and stage1 sleep EEG data are estimated for each second and applied to a multilayer perceptron feed forward neural network (MLP-FF). The network is trained using back propagation algorithm for recognizing these two patterns. Initially, the MLP network is trained and tested for randomly chosen subject-wise combined datasets I and II and then for the combined large dataset III. In all cases, 60 % of the entire dataset is used for training while 20 % is used for testing and 20 % for validation. Results indicate that the MLP neural network learns with maximum testing accuracy of 95.9 % for dataset II. In the case of combined large dataset, the network performs with a maximum accuracy of 99.2 % with 100 hidden neurons. Results show that in channels O1, O2, F3 and F4 (A1, A2 as reference), the mean of the spectral entropy value is higher in awake state than in stage1 sleep indicating that the EEG becomes more regular and rhythmic as the subject attains stage1 sleep from wakefulness. However, in C3 and C4 the mean values of SE values are not very much discriminative of both groups. This may prove to be a very effective indicator for scoring the first two stages of sleep EEG and may be used to detect the transition from wakefulness to stage1 sleep.

  4. Supine posture inhibits cortical activity: Evidence from Delta and Alpha EEG bands.

    Science.gov (United States)

    Spironelli, Chiara; Busenello, Jessica; Angrilli, Alessandro

    2016-08-01

    Past studies have shown consistent evidence that body position significantly affects brain activity, revealing that both head-down and horizontal bed-rest are associated with cortical inhibition and altered perceptual and cognitive processing. The present study investigates the effects of body position on spontaneous, open-eyes, resting-state EEG cortical activity in 32 young women randomly assigned to one of two conditions, seated position (SP) or horizontal bed rest (BR). A between-group repeated-measure experimental design was used, EEG recordings were made from 38 scalp locations, and low-frequency (delta and alpha) amplitudes of the two groups were compared in four different conditions: when both groups (a) were seated (T0), (b) assumed two different body positions (seated vs. supine conditions, immediate [T1] and 120min later [T2]), and (c) were seated again (T3). Overall, the results showed no a priori between-group differences (T0) before experimental manipulation. As expected, delta amplitude, an index of cortical inhibition in awake resting participants, was significantly increased in group BR, revealing both rapid (T1) and mid-term (T2) inhibitory effects of supine or horizontal positions. Instead, the alpha band was highly sensitive to postural transitions, perhaps due to baroreceptor intervention and, unlike the delta band, underwent habituation and decreased after a 2-h bed rest. These results indicate clear-cut differences at rest between the seated and supine positions, thus supporting the view that the role of body position in the differences found between brain metabolic methods (fMRI and PET) in which participants lie horizontally, and EEG-MEG-TMS techniques with participants in a seated position, has been largely underestimated so far.

  5. 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.

  6. The characteristics of EEG power spectra changes after ACL rupture

    Science.gov (United States)

    Miao, Xin; Huang, Hongshi; Hu, Xiaoqing; Li, Dai; Yu, Yuanyuan; Ao, Yingfang

    2017-01-01

    Background Reestablishing knee stability is the core of the treatment of ACL (Anterior Cruciate Ligament) injury. Some patients still have a feeling of instability of the knee after ACL injury treatment. This unstable feeling may be caused by central nervous system changes after ACL rupture. Methods To identify the central changes after ACL rupture, EEG spectra were recorded to compare ACL patients and healthy controls when they were walking, jogging, and landing. Results There was a significant increase in delta, theta, alpha and beta band power during walking, jogging and landing in ACL patients. We also found an asymmetry phenomenon of EEG only in the ACL patients, mainly in the frontal area and central-parietal area. The asymmetry of beta band power extended to the frontal and the central area during jogging and landing task. Conclusions There were significant differences in EEG power spectra between the ACL patients and healthy people. ACL patients showed high EEG band power activities and an asymmetry phenomenon. EEG power changes were affected by movements, the asymmetry extended when performing more complicated movements. PMID:28182627

  7. The use of standardized infinity reference in EEG coherency studies.

    Science.gov (United States)

    Marzetti, L; Nolte, G; Perrucci, M G; Romani, G L; Del Gratta, C

    2007-05-15

    The study of large scale interactions in the brain from EEG signals is a promising method for the identification of functional networks. However, the validity of a large scale parameter is limited by two factors: the use of a non-neutral reference and the artifactual self-interactions between the measured EEG signals introduced by volume conduction. In this paper, we propose an approach to study large scale EEG coherency in which these factors are eliminated. Artifactual self-interaction by volume conduction is eliminated by using the imaginary part of the complex coherency as a measure of interaction and the Reference Electrode Standardization Technique (REST) is used for the approximate standardization of the reference of scalp EEG recordings to a point at infinity that, being far from all possible neural sources, acts like a neutral virtual reference. The application of our approach to simulated and real EEG data shows that the detection of interaction, as opposed to artifacts due to reference and volume conduction, is a goal that can be achieved from the study of a large scale parameter.

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

    Science.gov (United States)

    Ahani, Asieh; Wahbeh, Helane; Nezamfar, Hooman; Miller, Meghan; Erdogmus, Deniz; Oken, Barry

    2014-05-14

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

  9. EEG recordings as a source for the detection of IRBD

    DEFF Research Database (Denmark)

    Bisgaard, Sissel; Duun-Christensen, Bolette; Kempfner, Lykke

    2015-01-01

    The purpose of this pilot study was to develop a supportive algorithm for the detection of idiopathic Rapid Eye-Movement (REM) sleep Behaviour Disorder (iRBD) from EEG recordings. iRBD is defined as REM sleep without atonia with no current sign of neurodegenerative disease, and is one of the earl......The purpose of this pilot study was to develop a supportive algorithm for the detection of idiopathic Rapid Eye-Movement (REM) sleep Behaviour Disorder (iRBD) from EEG recordings. iRBD is defined as REM sleep without atonia with no current sign of neurodegenerative disease, and is one...... contents of the EEG and a semi-automatic signal reduction method was introduced. The reduced feature set was used for a subject-based classification. With a subject specific re-scaling of the feature set and the use of an outlier detection classifier the algorithm reached an accuracy of 0.78. The result...... shows that EEG recordings contain valid information for a supportive algorithm for the detection of iRBD. Further investigation could lead to promising application of EEG recordings as a supportive source for the detection of iRBD....

  10. EEG source analysis of data from paralysed subjects

    Science.gov (United States)

    Carabali, Carmen A.; Willoughby, John O.; Fitzgibbon, Sean P.; Grummett, Tyler; Lewis, Trent; DeLosAngeles, Dylan; Pope, Kenneth J.

    2015-12-01

    One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

  11. Artefatos biológicos no EEG quantitativo Biologic artifacts in quantitative EEG

    Directory of Open Access Journals (Sweden)

    Renato Anghinah

    2006-06-01

    Full Text Available Estudamos, em 10 indivíduos adultos normais, o comportamento de cinco artefatos biológicos do eletrencefalograma (EEG: piscamento palpebral, fechamento forçado dos olhos, fechamento forçado da mandíbula, movimentos de língua e varredura horizontal dos olhos - tanto por análise visual como espectral - tanto com objetivo de verificar como esses artefatos são visualizados quando apresentados em mapas de potência da amplitude espectral. Observamos que os potenciais do espectro respeitavam a mesma disposição topográfica que os encontrados à análise visual do traçado. A análise visual do EEG é superior à quantitativa, para o reconhecimento de artefatos, porque preserva a visualização morfológica dos grafoelementos que deve ser feita obrigatoriamente no domínio do tempo, pois a sua correta identificação se perde no domínio da frequência. Devido a grande dificuldade de excluirmos totalmente os artefatos durante o registro do EEG e, por conseguinte, serem incluídos na análise quantitativa, é fundamental conhecermos como estes potenciais serão representados nos mapas quantitativos, para podermos identifica-los, evitando confundí-los com atividades patológicas do EEG.We studied the influence of five biologic artifacts sources on quantitative EEG (blinking, forced eyes closure, forced jaw closure, tongue movements and pursuit eyes movements through both visual and spectral analysis, with the purpose of verifying how do these artifacts can be seen in a cartographic way. We found that the spectrum’s potencials showed the same topographic display that was found through visual analysis. Visual analysis was superior than the quantitative evaluation to recognise the artifacts, as the former preserved the morphological display of the paroxisms. However it is important know how do the potencials are represented in quantitative maps, so that they can be identified as artifacts and not as pathologic EEG activity.

  12. Reduced mind wandering in experienced meditators and associated EEG correlates.

    Science.gov (United States)

    Brandmeyer, Tracy; Delorme, Arnaud

    2016-11-04

    One outstanding question in the contemplative science literature relates to the direct impact of meditation experience on the monitoring of internal states and its respective correspondence with neural activity. In particular, to what extent does meditation influence the awareness, duration and frequency of the tendency of the mind to wander. To assess the relation between mind wandering and meditation, we tested 2 groups of meditators, one with a moderate level of experience (non-expert) and those who are well advanced in their practice (expert). We designed a novel paradigm using self-reports of internal mental states based on an experiential sampling probe paradigm presented during ~1 h of seated concentration meditation to gain insight into the dynamic measures of electroencephalography (EEG) during absorption in meditation as compared to reported mind wandering episodes. Our results show that expert meditation practitioners report a greater depth and frequency of sustained meditation, whereas non-expert practitioners report a greater depth and frequency of mind wandering episodes. This is one of the first direct behavioral indices of meditation expertise and its associated impact on the reduced frequency of mind wandering, with corresponding EEG activations showing increased frontal midline theta and somatosensory alpha rhythms during meditation as compared to mind wandering in expert practitioners. Frontal midline theta and somatosensory alpha rhythms are often observed during executive functioning, cognitive control and the active monitoring of sensory information. Our study thus provides additional new evidence to support the hypothesis that the maintenance of both internal and external orientations of attention may be maintained by similar neural mechanisms and that these mechanisms may be modulated by meditation training.

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

    DEFF Research Database (Denmark)

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

    2006-01-01

    While phylogenetic and cluster analyses are often used to define clonal groups within bacterial species, the identification of clonal groups that are associated with specific ecological niches or host species remains a challenge. We used Listeria monocytogenes, which causes invasive disease...... in humans and different animal species and which can be isolated from a number of environments including food, as a model organism to develop and implement a two-step statistical approach to the identification of phylogenetic clades that are significantly associated with different source populations...

  14. Clustering technique-based least square support vector machine for EEG signal classification.

    Science.gov (United States)

    Siuly; Li, Yan; Wen, Peng Paul

    2011-12-01

    This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals.

  15. Altered Resting-State EEG Source Functional Connectivity In Schizophrenia: The Effect Of Illness Duration

    Directory of Open Access Journals (Sweden)

    Giorgio eDi Lorenzo

    2015-05-01

    Full Text Available Despite the increasing body of evidence supporting the hypothesis of schizophrenia as a disconnection syndrome, studies of resting-state EEG Source Functional Connectivity (EEG-SFC in people affected by schizophrenia are sparse. The aim of the present study was to investigate resting-state EEG-SFC in 77 stable, medicated patients with schizophrenia (SCZ compared to 78 healthy volunteers (HV. In order to study the effect of illness duration, SCZ were divided in those with a short duration of disease (SDD; n = 25 and those with a long duration of disease (LDD; n = 52. Resting-state EEG recordings in eyes closed condition were analyzed and lagged phase synchronization (LPS indices were calculated for each ROI pair in the source-space EEG data. In delta and theta bands, SCZ had greater EEG-SFC than HV; a higher theta band connectivity in frontal regions was observed in LDD compared with SDD. In the alpha band, SCZ showed lower frontal EEG-SFC compared with HV whereas no differences were found between LDD and SDD. In the beta1 band, SCZ had greater EEG-SFC compared with HVs and in the beta2 band, LDD presented lower frontal and parieto-temporal EEG-SFC compared with HV. In the gamma band, SDD had greater connectivity values compared with LDD and HV. This study suggests that resting state brain network connectivity is abnormally organized in schizophrenia, with different patterns for the different EEG frequency components and that EEG can be a powerful tool to further elucidate the complexity of such disordered connectivity.

  16. 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.

  17. Filter transient response to EEG waveforms.

    Science.gov (United States)

    Shirakawa, S; Smith, J R; Azumi, K

    1987-01-01

    The response of two types of linear filters to sinusoidal bursts was calculated to demonstrate how filters can distort EEG waveforms. Results show that the wider the filter bandwidth the less is the distortion, and for a given bandwidth, the higher the filter order the greater the distortion. The response of a linear phase filter was also calculated to demonstrate that this type of filter can also cause waveform distortion, although it is normally less than that caused by Butterworth, Tchebychev and elliptic filters.

  18. Sleep Inducing for EEG Recording in Children: A Comparison between Oral Midazolam and Chloral Hydrate

    Directory of Open Access Journals (Sweden)

    Mahmoud Reza ASHRAFI

    2013-02-01

    Full Text Available How to Cite This Article: AshrafiMR, Azizi Malamiri R, Zamani GR, Mohammadi M, Hosseini F. Sleep Inducing for EEG Recording in Children: A Comparison between Oral Midazolam and Chloral Hydrate. Iran J Child Neurol. 2013 Winter;7(1:15-19.ObjectiveElectroencephalography (EEG recording is a long duration procedure that needs patient’s cooperation for device setup and performing the procedure. Many children lose their cooperation during this procedure. Therefore, sedation and sleep are frequently induced using a few agents as pre procedure medication in children before EEG recording. We aimed to compare the sedative effects of oral midazolam versus chloral hydrate before the procedure along with their impacts on EEG recording in children.Materials & MethodsA randomized trial was carried out to compare the sedative effects of oral midazolam versus chloral hydrate and their impacts on EEG recording in children. A total of 198 children (100 in the midazolam group and 98 in the chloral hydrate group were enrolled in the study and randomly allocated to receive either oral moidazolam or chloral hydrate.ResultsOral midazolam had superiority neither in sleep onset latency nor in sleep duration when compared to chloral hydrate. Moreover, the yield of epileptiform discharges in the chloral hydrate group was more than the midazolam group.ConclusionThe results of this study showed that both chloral hydrate 5% (one ml/kg and oral midazolam (0.5 mg/kg could be administered as a pre medication agent for EEG recording in children. However, oral midazolam at this dose had no advantage compared with chloral hydrate.ReferencesAshrafi MR, Mohammadi M, Tafarroji J, Shabanian R, Salamati P, Zamani GR. Melatonin versus chloral hydrate for recording sleep EEG. Eur J Paediatr Neurol 2010;14(3:235-8.Slifer KJ, Avis KT, Frutchey RA. Behavioral intervention to increase compliance with electroencephalographic procedures in children with developmental disabilities. Epilepsy

  19. EEG source imaging assists decoding in a face recognition task

    DEFF Research Database (Denmark)

    Andersen, Rasmus S.; Eliasen, Anders U.; Pedersen, Nicolai

    2017-01-01

    EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting that EEG source reconstruction can assist decoding. EEG source imaging leads to high-dimensional representations...... of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source...... imaging does not lead to an improved decoding. We design a distributed pipeline in which the classifier has access to brain wide features which in turn does lead to a 15% reduction in the error rate using source space features. Hence, our work presents supporting evidence for the hypothesis that source...

  20. The Best Time for EEG Recording in Febrile Seizure

    Directory of Open Access Journals (Sweden)

    Parvaneh KARIMZADEH

    2014-01-01

    Full Text Available How to Cite This Article: Karimzadeh P, Rezayi A, Togha M, Ahmadabadi F, Derakhshanfar H, Azargashb E, Khodaei F. The Best Time for EEG Recording in Febrile Seizure. Iran J Child Neurol. 2014 Winter; 8(1:20-25.ObjectiveSome studies suggest that detection of epileptic discharge is unusual during the first postictal week of febrile seizure and others believe that EEGs carried out on the day of the seizure are abnormal in as many as 88% of the patients. In thisstudy, we intend to compare early and late EEG abnormalities in febrile seizure.Materials & Methods EEG was recorded during daytime sleep, 24-48 hours (early EEG and 2 weeks (late EEG after the seizure in 36 children with febrile seizure (FS, aged between 3 months and 6 years. EEGs that showed generalized or focal spikes, sharp, spike wave complex, and slowing were considered as abnormal EEG.Abnormalities of the first EEG were compared with those of second EEG.ResultsThe most common abnormal epileptiform discharges recorded in the early EEG were slow waves (27.6% and sharp waves in late EEG (36%. Distribution of abnormalities in early and late EEG showed no significant statistical difference.ConclusionThe early and late EEG recording had the same results in patient with febrile seizure. Reference:Hauser WA, Kurland LT. The epidemiology of epilepsy in Rochester, Minnesota, 1935 through 1967. Epilepsia 1975;16(1:1-66.Freeman JM. Febrile seizures: a consensus of their significance, evaluation, and treatment. Pediatrics 1980;66(6:1009.Waruiru C, Appleton R. Febrile seizures: an update. Arch Dis Child 2004;89(8:751-6.ILAE. Guidelines for epidemiologic studies on epilepsy, International League against Epilepsy. Epilepsia 1993;34(4:592-6.Annegers JF, Hauser WA, Shirts SB, Kurland LT. Factors prognostic of unprovoked seizures after febrile convulsions. N Engl J Med 1987;316(9:493-8.Berg AT, Shinnar S, Darefsky AS, Holford TR, Shapiro ED, Salomon ME, et al. Predictors of recurrent febrile

  1. Mice repeatedly exposed to Group-A β-Haemolytic Streptococcus show perseverative behaviors, impaired sensorimotor gating, and immune activation in rostral diencephalon

    OpenAIRE

    Simone Macrì; Chiara Ceci; Martina Proietti Onori; Roberto William Invernizzi; Erika Bartolini; Luisa Altabella; Rossella Canese; Monica Imperi; Graziella Orefici; Roberta Creti; Immaculada Margarit; Roberta Magliozzi; Giovanni Laviola

    2015-01-01

    Repeated exposure to Group-A β-Haemolytic Streptococcus (GAS) may constitute a vulnerability factor in the onset and course of pediatric motor disturbances. GAS infections/colonization can stimulate the production of antibodies, which may cross the blood brain barrier, target selected brain areas (e.g. basal ganglia), and exacerbate motor alterations. Here, we exposed developing SJL male mice to four injections with a GAS homogenate and evaluated the following domains: motor coordination; gen...

  2. The karyotypes of five species of the Scinax perpusillus group (Amphibia, Anura, Hylidae) of southeastern Brazil show high levels of chromosomal stabilization in this taxon.

    Science.gov (United States)

    Peixoto, Marco Antônio Amorim; Lacerda, João Victor Andrade; Coelho-Augusto, Carolina; Feio, Renato Neves; Dergam, Jorge Abdala

    2015-12-01

    Based on morphological, bioacoustics, and morphological traits, the genus Scinax has been subdivided into two major clades: S. catharinae and S. ruber. The first clade includes S. catharinae and S. perpusillus groups, whereas the second clade includes S. rostratus and S. uruguayus groups. Chromosome morphology, NOR and C-banding patterns of variation support these clades. This study aims the cytogenetic characterization of five species currently included in the S. perpusillus group: Scinax sp. (gr. perpusillus), S. arduous, S. belloni, S. cosenzai, and S. v-signatus, including standard cytogenetic techniques and repetitive DNA FISH probes. All species had 2n = 24 chromosomes. Nucleolar organizing regions occurred in chromosome pair 6 in all species, but differed in their locations among some species, suggesting a putative synaponomastic character for the clade. In S. belloni, the first chromosome pair was a metacentric, contrasting with the submetacentric first pair reported in all other species of the genus. Scinax sp. (gr. perpusillus) and S. v-signatus had similar karyotypic formulae, suggesting they are related species. Scinax cosenzai had a divergent C-banding pattern. Repetitive DNA probes hybridized more frequently in chromosomal subtelomeric regions in all species indicating recent cladogenesis in these species. Karyotypic evidence indicates unreported high levels of stabilization within S. perpusillus and in S. catharinae clade, resulting in a wealth of characters potentially informative for higher phylogenetic analyses.

  3. 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.

  4. [Lithium: EEG, performance and mood under subchronic therapy (author's transl)].

    Science.gov (United States)

    Herrmann, W M; Hamann, S; Fichte, K; Müller-Oerlinghausen, B

    1978-01-01

    In a controlled double-blind trial 2X12 healthy male volunteers were tested during treatment with lithium sulphate (24--36 mval/d) or placebo. Volunteers were tested after one and two weeks' drug intake using the following tests: Quantitative pharmaco EEG, psychological performance and mood scales. In comparison to pacebo, lithium shows increased mistake duration of the pursuit rotor (MLS), decreased flicker fusion threshold and an impairment of mood. The EEG effects (decrease 7--9 cps, increase 20--50 cps) require further clarification.

  5. Methylphenidate Efficacy: Immediate versus Extended Release at Short Term in Mexican Children with ADHD Assessed by Conners Scale and EEG

    Science.gov (United States)

    Alatorre-Miguel, Efren; Zambrano-Sánchez, Elizabeth; Reyes-Legorreta, Celia

    2015-01-01

    Attention deficit hyperactivity disorder (ADHD) affects 5-6% of school aged children worldwide. Pharmacological therapy is considered the first-line treatment and methylphenidate (MPH) is considered the first-choice medication. There are two formulations: immediate release (IR) MPH and long-acting (or extended release) formulation (MPH-ER). In this work, we measure the efficacy of treatment for both presentations in one month with Conners' scales and electroencephalography (EEG). Results. for IR group, in parents and teachers Conners test, all items showed significant differences, towards improvement, except for teachers in perfectionism and emotional instability. For ER group in parent's Conners test, the items in which there were no significant differences are psychosomatic and emotional instability. For teachers, there were no significant differences in: hyperactivity and perfectionism. Comparing the Conners questionnaires (parents versus teachers) we find significant differences before and after treatment in hyperactivity, perfectionism, psychosomatics, DSM-IV hyperactive-impulsive, and DSM-IV total. In the EEG the Wilcoxon test showed a significant difference (P < 0.0001). As we can see, both presentations are suitable for managing the ADHD and have the same effect on the symptomatology and in the EEG. PMID:25838946

  6. Methylphenidate Efficacy: Immediate versus Extended Release at Short Term in Mexican Children with ADHD Assessed by Conners Scale and EEG

    Directory of Open Access Journals (Sweden)

    Alfredo Durand-Rivera

    2015-01-01

    Full Text Available Attention deficit hyperactivity disorder (ADHD affects 5-6% of school aged children worldwide. Pharmacological therapy is considered the first-line treatment and methylphenidate (MPH is considered the first-choice medication. There are two formulations: immediate release (IR MPH and long-acting (or extended release formulation (MPH-ER. In this work, we measure the efficacy of treatment for both presentations in one month with Conners’ scales and electroencephalography (EEG. Results. for IR group, in parents and teachers Conners test, all items showed significant differences, towards improvement, except for teachers in perfectionism and emotional instability. For ER group in parent’s Conners test, the items in which there were no significant differences are psychosomatic and emotional instability. For teachers, there were no significant differences in: hyperactivity and perfectionism. Comparing the Conners questionnaires (parents versus teachers we find significant differences before and after treatment in hyperactivity, perfectionism, psychosomatics, DSM-IV hyperactive-impulsive, and DSM-IV total. In the EEG the Wilcoxon test showed a significant difference (P<0.0001. As we can see, both presentations are suitable for managing the ADHD and have the same effect on the symptomatology and in the EEG.

  7. Preliminary EEG study of protective effects of Tebonin in transient global cerebral ischemia in rats

    DEFF Research Database (Denmark)

    Zagrean, L; Vatasescu, R; Munteanu, A M

    2000-01-01

    and metabolism. The objective of this study was to investigate the effects of preventive treatment with Ginkgo biloba extract (EGb 761--Tebonin) in cerebral global ischemia and reperfusion in rats using computerized EEG analysis. Ginkgo biloba extract, known to be, in vitro, a free radicals scavanger and a PAF.......0015). Computerized spectral analysis of EEG has shown that the percentage of slow waves at 10 minutes after reperfusion was 117% higher in control group than in Ginkgo biloba group (p

  8. 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.

  9. Phase Spectral Analysis of EEG Signals

    Institute of Scientific and Technical Information of China (English)

    YOURong-yi; CHENZhong

    2004-01-01

    A new method of phase spectral analysis of EEG is proposed for the comparative analysis of phase spectra between normal EEG and epileptic EEG signals based on the wavelet decomposition technique. By using multiscale wavelet decomposition, the original EEGs are mapped to an orthogonal wavelet space, such that the variations of phase can be observed at multiscale. It is found that the phase (and phase difference) spectra of normal EEGs are distinct from that of epileptic EEGs. That is the variations of phase (and phase difference) of normal EEGs have a distinct periodic pattern with the electrical activity proceeds in the brain, but do not the epileptic EEGs. For epileptic EEGs, only at those transient points, the phase variations are obvious. In order to verify these results with the observational data, the phase variations of EEGs in principal component space are observed and found that, the features of phase spectra is in correspondence with that the wavelet space. These results make it possible to view the behavior of EEG rhythms as a dynamic spectrum.

  10. EEG Findings in Burnout Patients

    NARCIS (Netherlands)

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

    2010-01-01

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

  11. EEG Findings in Burnout Patients

    NARCIS (Netherlands)

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

    2010-01-01

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

  12. Analysis of Grey Matter in Thalamus and Basal Ganglia Based on EEG α3/α2 Frequency Ratio Reveals Specific Changes in Subjects with Mild Cognitive Impairment

    Directory of Open Access Journals (Sweden)

    Davide V Moretti

    2012-11-01

    Full Text Available GM (grey matter changes of thalamus and basal ganglia have been demonstrated to be involved in AD (Alzheimer's disease. Moreover, the increase of a specific EEG (electroencephalogram marker, α3/α2, have been associated with AD-converters subjects with MCI (mild cognitive impairment. To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to detect biomarkers (morpho-physiological early predictive of AD and non-AD dementia. Seventy-four adult subjects with MCI underwent EEG recording and high-resolution 3D MRI (three-dimensional magnetic resonance imaging. The α3/α2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of α3/α2 ratio. GM density differences between groups were investigated using a VBM (voxel-based morphometry technique. Subjects with higher α3/α2 ratios when compared with subjects with lower and middle α3/α2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally and of the pulvinar nuclei in the thalamus; The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.

  13. Cerebral PET glucose hypometabolism in subjects with mild cognitive impairment and higher EEG high-alpha/low-alpha frequency power ratio.

    Science.gov (United States)

    Moretti, Davide Vito; Pievani, Michela; Pini, Lorenzo; Guerra, Ugo Paolo; Paghera, Barbara; Frisoni, Giovanni Battista

    2017-10-01

    In Alzheimer's disease (AD) research, both 2-deoxy-2-((18)F)fluoro-D-glucose (FDG) positron emission tomography (PET) and electroencephalography (EEG) are reliable investigational modalities. The aim of this study was to investigate the associations between EEG High-alpha/Low-alpha (H-alpha/L-alpha) power ratio and cortical glucose metabolism. A total of 23 subjects with mild cognitive impairment (MCI) underwent FDG-PET and EEG examinations. H-alpha/L-alpha power ratio was computed for each subject and 2 groups were obtained based on the increase of the power ratio. The subjects with higher H-alpha/L-alpha power ratio showed a decrease in glucose metabolism in the hub brain areas previously identified as typically affected by AD pathology. In subjects with higher H-alpha/L-alpha ratio and lower metabolism, a "double alpha peak" was identified in the EEG spectrum and a U-shaped correlation between glucose metabolism and increase of H-alpha/L-alpha power ratio has been found. Moreover, in this group, a conversion rate of 62.5% at 24 months was detected, significantly different from the chance percentage expected. The neurophysiological meaning of the interplay between alpha oscillations and glucose metabolism and the possible interest of the H-alpha/L-alpha power ratio as a clinical biomarker in AD have been discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. The Effect of EEG Biofeedback Therapy on Motor Abilities of Children with Attention Deficit Hyperactivity Disorder

    Directory of Open Access Journals (Sweden)

    Elena Žiaková

    2015-12-01

    Full Text Available Background. Currently, EEG biofeedback (Neurofeedback is used in the rehabilitation of children with brain damage with the symptoms of attention deficit disorder, hyperactivity and impulsivity. After treatment improvements were observed not only in the control of attention and impulsivity but also in voluntary and involuntary movements. The aim of the prospective clinical study was to measure the impact of EEG biofeedback on motor abilities of children with ADHD (Attention Deficit Hyperactivity Disorder and compare the effectiveness of EEG biofeedback with classical rehabilitation. It was assumed that in children with ADHD in combination with central motor disorders EEG biofeedback therapy will strengthen not only the control of impulsivity and attention but also motor skills. Material. The observed group consisted of 60 (N = 60 children with mild central motor disorders with ADHD. They were randomly assigned to either the EEG biofeedback group (N = 30, mean age 8.9 years or the classical rehabilitation group (N = 30, mean age 8.5 years. Methods. Both groups received thirty 30-45 minute sessions of training, at a frequency of 2-3 times a week. Pre-post assessment included testing of motor skills with PANESS test (Physical and Neurological Examination for Subtle Signs for both groups and the EEG biofeedback group were assessed also for changes in impulse and attention control using CPT (Continuous Performance Test test AX version and changes observed by parents using TLC Subjective Assessment (The Learning Curve, 2004. Results. Achieved overall score of EEG biofeedback group was lower after therapy (Mdn = 24.00 than before therapy (Mdn = 55.00, T = 0.00, p <0.01, Z = -4.78, r = -0.62. Values of significance (Asymp.Sig. 2-tailed = 0.000 and effect size (effect size r = -0.62 indicate a statistical and factual significant positive effect of EEG biofeedback to improve overall motor skills (lower score is better. Conclusion. EEG biofeedback therapy

  15. EEG disorder in patients with complex febrile convulsion and underlying risk factors

    Directory of Open Access Journals (Sweden)

    Mitra Hemmati

    2014-08-01

    Full Text Available Background: Febrile seizures are the most common convulsion disorder in childhood. The possible risk of developing epilepsy in febrile seizures is about 2-10%. EEG is helpful to diagnose epilepsy; however, there are controversies about the abnormal EEG and associated risk factors .The aim of this study was to determine EEG abnormality and effective risk factors in patients with complex febrile seizures. Methods: This study was conducted on the patients with complex febrile seizures in 2009-2010.EEG was performed on all children 6 to 10 days after seizure and reported by a neurologist. Demographic data and risk factors, including age, sex, family history of epilepsy and febrile convulsions, presentation of seizure, postictal neurological disorder were documented by a checklist and their association with EEG was analyzed. Results: 111 patients with complex febrile seizure, 70 girls and 41 boys, with the mean age of 3.4±20 months were studied. EEG was abnormal in 37.8% of patients, 9% were epileptic form abnormality and 28.8% were nonspecific abnormal. There was a statistically significant association between EEG abnormality in patients with focal seizures, family history of febrile seizures and postictal neurologic disorder (p<0.05. Conclusion: The results of this study showed abnormality of EEG in complex febrile convulsions in 37.8% of patients, which was significantly higher in patients with postictal neurological disorder, focal seizures and family history of febrile seizure.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-10-24

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

  17. 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.

  18. Conductive polymer foam surface improves the performance of a capacitive EEG electrode.

    Science.gov (United States)

    Baek, Hyun Jae; Lee, Hong Ji; Lim, Yong Gyu; Park, Kwang Suk

    2012-12-01

    In this paper, a new conductive polymer foam-surfaced electrode was proposed for use as a capacitive EEG electrode for nonintrusive EEG measurements in out-of-hospital environments. The current capacitive electrode has a rigid surface that produces an undefined contact area due to its stiffness, which renders it unable to conform to head curvature and locally isolates hairs between the electrode surface and scalp skin, making EEG measurement through hair difficult. In order to overcome this issue, a conductive polymer foam was applied to the capacitive electrode surface to provide a cushioning effect. This enabled EEG measurement through hair without any conductive contact with bare scalp skin. Experimental results showed that the new electrode provided lower electrode-skin impedance and higher voltage gains, signal-to-noise ratios, signal-to-error ratios, and correlation coefficients between EEGs measured by capacitive and conventional resistive methods compared to a conventional capacitive electrode. In addition, the new electrode could measure EEG signals, while the conventional capacitive electrode could not. We expect that the new electrode presented here can be easily installed in a hat or helmet to create a nonintrusive wearable EEG apparatus that does not make users look strange for real-world EEG applications.

  19. Automated Classification and Removal of EEG Artifacts with SVM and Wavelet-ICA.

    Science.gov (United States)

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

    2017-07-04

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

  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. 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.

  2. Ultra-dense EEG sampling results in two-fold increase of functional brain information.

    Science.gov (United States)

    Petrov, Yury; Nador, Jeffrey; Hughes, Christopher; Tran, Stanley; Yavuzcetin, Ozgur; Sridhar, Srinivas

    2014-04-15

    Contemporary high-density electroencephalographic systems (hd-EEG) comprising up to 256 electrodes have inter-electrode separations of 2-4 cm. Because electric currents of the brain are believed to strongly diffuse before reaching the scalp surface, higher-density electrode coverage is often deemed unnecessary. We used an ultra-dense electroencephalography (ud-EEG) sensor array to reveal strong potential variation at 1cm scale and discovered that it reflects functional brain activity. A new classification paradigm demonstrates that ud-EEG provides twice the signal to noise ratio for brain-response classification compared with contemporary hd-EEG. These results suggest a paradigm shift from current thinking by showing that higher spatial resolution sampling of EEG is required and leads to increased functional brain information that is useful for diverse neurological applications.

  3. A unified treatment of the reference estimation problem in depth EEG recordings.

    Science.gov (United States)

    Madhu, Nilesh; Ranta, Radu; Maillard, Louis; Koessler, Laurent

    2012-10-01

    The starting point of this paper is the analysis of the reference problem in intra-cerebral electroencephalographic (iEEG) recordings. It is well accepted that both surface and depth EEG signals are always recorded with respect to some unknown time-varying signal called reference. This article discusses different methods for determining and reducing the influence of the reference signal for the iEEG signals. In particular, we derive optimal approaches for the estimation of the reference signal in iEEG recording setups and demonstrate their relation to the well-known minimum power/variance distortionless response approaches derived for general array and antenna signal processing applications. We show that the proposed approaches achieve optimal performance in terms of estimation error and that they outperform other reference identification methods proposed in the literature. The developed algorithms are illustrated on simulated examples and on real iEEG signals.

  4. 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.

  5. Comparisons of coat protein gene sequences show that East African isolates of Sweet potato feathery mottle virus form a genetically distinct group.

    Science.gov (United States)

    Kreuze, J F; Karyeija, R F; Gibson, R W; Valkonen, J P

    2000-01-01

    Sweet potato feathery mottle virus (SPFMV, genus Potyvirus) infects sweet potatoes (Ipomoea batatas) worldwide, but no sequence data on isolates from Africa are available. Coat protein (CP) gene sequences from eight East African isolates from Madagascar and different districts of Uganda (the second biggest sweet potato producer in the world) and two West African isolates from Nigeria and Niger were determined. They were compared by phylogenetic analysis with the previously reported sequences of ten SPFMV isolates from other continents. The East African SPFMV isolates formed a distinct cluster, whereas the other isolates were not clustered according to geographic origin. These data indicate that East African isolates of SPFMV form a genetically unique group.

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

    Directory of Open Access Journals (Sweden)

    Yvonne Höller

    2017-09-01

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

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

    Science.gov (United States)

    Höller, Yvonne; Bathke, Arne C.; Uhl, Andreas; Strobl, Nicolas; Lang, Adelheid; Bergmann, Jürgen; Nardone, Raffaele; Rossini, Fabio; Zauner, Harald; Kirschner, Margarita; Jahanbekam, Amirhossein; Trinka, Eugen; Staffen, Wolfgang

    2017-01-01

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

  8. Interhemispheric synchrony in the neonatal EEG revisited: activation synchrony index as a promising classifier.

    Science.gov (United States)

    Koolen, Ninah; Dereymaeker, Anneleen; Räsänen, Okko; Jansen, Katrien; Vervisch, Jan; Matic, Vladimir; De Vos, Maarten; Van Huffel, Sabine; Naulaers, Gunnar; Vanhatalo, Sampsa

    2014-01-01

    A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS), which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI), was recently shown to perform well as compared to visual assessments. The present study was set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality. We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n = 14) or abnormal (n = 17) using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-min epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define them for the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies. Using the average of four consecutive 2.5-min epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6) which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 to 5 min leads to increased variability in ASI-based classification. Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG classifiers.

  9. Interhemispheric synchrony in the neonatal EEG revisited: Activation Synchrony Index as a promising classifier

    Directory of Open Access Journals (Sweden)

    Ninah eKoolen

    2014-12-01

    Full Text Available A key feature of normal neonatal EEG at term age is interhemispheric synchrony (IHS, which refers to the temporal co-incidence of bursting across hemispheres during trace alternant EEG activity. The assessment of IHS in both clinical and scientific work relies on visual, qualitative EEG assessment without clearly quantifiable definitions. A quantitative measure, activation synchrony index (ASI, was recently shown to perform well as compared to visual assessments. The present study set out to test whether IHS is stable enough for clinical use, and whether it could be an objective feature of EEG normality.We analyzed 31 neonatal EEG recordings that had been clinically classified as normal (n=14 or abnormal (n=17 using holistic, conventional visual criteria including amplitude, focal differences, qualitative synchrony, and focal abnormalities. We selected 20-minute epochs of discontinuous background pattern. ASI values were computed separately for different channel pair combinations and window lengths to define the optimal ASI intraindividual stability. Finally, ROC curves were computed to find trade-offs related to compromised data lengths, a common challenge in neonatal EEG studies.Using the average of four consecutive 2.5-minute epochs in the centro-occipital bipolar derivations gave ASI estimates that very accurately distinguished babies clinically classified as normal vs. abnormal. It was even possible to draw a cut-off limit (ASI~3.6 which correctly classified the EEGs in 97% of all cases. Finally, we showed that compromising the length of EEG segments from 20 minutes to 5 minutes leads to increased variability in ASI-based classification.Our findings support the prior literature that IHS is an important feature of normal neonatal brain function. We show that ASI may provide diagnostic value even at individual level, which strongly supports its use in prospective clinical studies on neonatal EEG as well as in the feature set of upcoming EEG

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

    Directory of Open Access Journals (Sweden)

    Jih-Huah Wu

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    E. Parvinnia

    2014-01-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  13. Cognitive training modifies frequency EEG bands and neuropsychological measures in Rett syndrome.

    Science.gov (United States)

    Fabio, Rosa Angela; Billeci, Lucia; Crifaci, Giulia; Troise, Emilia; Tortorella, Gaetano; Pioggia, Giovanni

    2016-01-01

    Rett syndrome (RS) is a childhood neurodevelopmental disorder characterized by a primary disturbance in neuronal development. Neurological abnormalities in RS are reflected in several behavioral and cognitive impairments such as stereotypies, loss of speech and hand skills, gait apraxia, irregular breathing with hyperventilation while awake, and frequent seizures. Cognitive training can enhance both neuropsychological and neurophysiological parameters. The aim of this study was to investigate whether behaviors and brain activity were modified by training in RS. The modifications were assessed in two phases: (a) after a short-term training (STT) session, i.e., after 30 min of training and (b) after long-term training (LTT), i.e., after 5 days of training. Thirty-four girls with RS were divided into two groups: a training group (21 girls) who underwent the LTT and a control group (13 girls) that did not undergo LTT. The gaze and quantitative EEG (QEEG) data were recorded during the administration of the tasks. A gold-standard eye-tracker and a wearable EEG equipment were used. Results suggest that the participants in the STT task showed a habituation effect, decreased beta activity and increased right asymmetry. The participants in the LTT task looked faster and longer at the target, and show increased beta activity and decreased theta activity, while a leftward asymmetry was re-established. The overall result of this study indicates a positive effect of long-term cognitive training on brain and behavioral parameters in subject with RS.

  14. Sparse Source EEG Imaging with the Variational Garrote

    DEFF Research Database (Denmark)

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

    2013-01-01

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

  15. Sleep EEG spectral analysis in a diurnal rodent : Eutamias sibiricus

    NARCIS (Netherlands)

    DIJK, DJ; DAAN, S

    1989-01-01

    1. Sleep was studied in the diurnal rodent Eutamias sibiricus, chronically implanted with EEG and EMG electrodes. Analysis of the distribution of wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep over the 24 h period (LD 12:12) showed that total sleep time was 27.5%

  16. Single-trial EEG RSVP classification using convolutional neural networks

    Science.gov (United States)

    Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

    2016-05-01

    Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

  17. Differences in resting EEG related to ability.

    Science.gov (United States)

    Jausovec, N; Jausovec, K

    2000-01-01

    The aim of the present study was to investigate the relationship between different EEG measures (mean power, mean frequency, approximated entropy and coherence), and ability (creativity and intelligence). For that purpose the EEG of 115 student-teachers (Intelligence: M= 115.17; SD = 12.78; IQ(min)= 82; IQ(max)= 136; Creativity - standardized scores: M = 55.97; SD = 10.67; C(min)= 38; C(max)= 84) was recorded while they were resting with eyes open and closed. The study showed only weak correlations between measures based on the level of activity in different areas (mean power, mean frequency and approximated entropy) and creativity. The correlations with IQ scores were even less pronounced. On the other hand, coherence measures showed a much more intense relationship both with creativity as well as with intelligence. In the eyes-open state these differences were mainly distributed over the right hemisphere. The results are discussed in the light of different theories relating brain functioning and ability.

  18. [Models and computation methods of EEG forward problem].

    Science.gov (United States)

    Zhang, Yinghcun; Zou, Ling; Zhu, Shanan

    2004-04-01

    The research of EEG is of grat significance and clinical importance in studying the cognitive function and neural activity of the brain. There are two key problems in the field of EEG, EEG forward problem and EEG inverse problem. EEG forward problem which aims to get the distribution of the scalp potential due to the known current distribution in the brain is the basis of the EEG inverse problem. Generally, EEG inverse problem depends on the accuracy and efficiency of the computational method of EEG forward problem. This paper gives a review of the head model and corresponding computational method about EEG forward problem studied in recent years.

  19. [Gender differences in EEG coherence changes during figural creative thinking: the efficacy coupling].

    Science.gov (United States)

    Vol'f, N V; Tarasova, I V; Razumnikova, O M

    2009-01-01

    The study was aimed to explore the features of interaction between cortical areas during figural creative task performance in high- and low-creative men and women. We divided the participants into two groups with high and low creativity by the median of originality score. EEG was recorded at rest and during task performance (Torrance Tests of Creative Thinking "Incomplete figures"). The EEG coherence was computed in six frequency bands from theta1 to beta2. We analyzed the total values of coherence for each of 16 sites, calculated separately for intrahemispheric and interhemispheric connections. In the theta2, alphal, and alpha2 bands, coherence values decreased in task performance as compared to baseline in subjects with lower originality scores, whereas in subjects with higher scores, they increased in the theta2 and alpha1 bands. The decrease in the alpha2 band in the higher-creativity group was significantly lower in comparison with the decrease in the lower-score group. In the alpha2 band, the interaction of gender, creativity, laterality, and electrode position factors was also found during analysis of task-induced coherence changes. Further examination of the interaction showed the similarity of EEG coherence patterns in men and women with opposite creative abilities and higher values of task-induced coherence changes in the anterior regions of the left hemisphere and posterior regions of the right hemisphere in high-creative in comparison with low-creative men. The findings are discussed in terms of different cognitive strategies used by men and women that may have the same results in creative problem solving.

  20. 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

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

    Directory of Open Access Journals (Sweden)

    Miralena I. Tomescu

    2015-09-01

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

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Liang Muh-Lii

    2010-02-01

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

  4. [Changes of EEG power spectrum in response to the emotional auditory stimuli in patients in acute and recovery stages of TBI (traumatic brain injury)].

    Science.gov (United States)

    2013-01-01

    We investigated variability of responses to emotionally important auditory stimulation in different groups of TBI (Traumatic Brain Injury) in acute state or recovery. The patients sampling consisted of three different groups: patients in coma or vegetative state, patients with Severe and Moderate TBI in recovery period. Subjects were stimulated with auditory stimuli containing important physiological sounds (coughing, vomiting), emotional sounds (laughing, crying), nature sounds (bird song, barking), unpleasant household sounds (nails scratching the glass), natural sounds (sea, rain, fire) and neutral sounds (white noise). The background encephalographic activity was registered during at least 7 minutes. EEG was recorded while using portable device "Entsefalan". Significant differences of power of the rhythmic activity registered during the presentation of different types of stimuli were analyzed using Mathlab and Statistica 6.0. Results showed that EEG-response to the emotional stimuli differed depending on consciousness level, stimuli type, severity of TBI. Most valuable changes in EEG spectrum power for a patient with TBI were found for unpleasant auditory stimulation. Responsiveness to the pleasant stimulation could be registered in later stages of coming out of coma than to unpleasant stimulation. Alpha-activity is reducing in patients with TBI: the alpha rhythm depression is most evident in the control group, less in group after moderate TBI, and even less in group after severe TBI. Patients in coma or vegetative state didn't show any response in rhythmic power in the frequency of alpha rhythm.

  5. Putative EEG measures of social anxiety: Comparing frontal alpha asymmetry and delta-beta cross-frequency correlation.

    Science.gov (United States)

    Harrewijn, A; Van der Molen, M J W; Westenberg, P M

    2016-12-01

    The goal of the present study was to examine whether frontal alpha asymmetry and delta-beta cross-frequency correlation during resting state, anticipation, and recovery are electroencephalographic (EEG) measures of social anxiety. For the first time, we jointly examined frontal alpha asymmetry and delta-beta correlation during resting state and during a social performance task in high (HSA) versus low (LSA) socially anxious females. Participants performed a social performance task in which they first watched and evaluated a video of a peer, and then prepared their own speech. They believed that their speech would be videotaped and evaluated by a peer. We found that HSA participants showed significant negative delta-beta correlation as compared to LSA participants during both anticipation of and recovery from the stressful social situation. This negative delta-beta correlation might reflect increased activity in subcortical brain regions and decreased activity in cortical brain regions. As we hypothesized, no group differences in delta-beta correlation were found during the resting state. This could indicate that a certain level of stress is needed to find EEG measures of social anxiety. As for frontal alpha asymmetry, we did not find any group differences. The present frontal alpha asymmetry results are discussed in relation to the evident inconsistencies in the frontal alpha asymmetry literature. Together, our results suggest that delta-beta correlation is a putative EEG measure of social anxiety.

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

    Science.gov (United States)

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

    2014-10-01

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

  7. Creativity and cortical activation during creative, intellectual and EEG feedback tasks.

    Science.gov (United States)

    Martindale, C; Hines, D

    1975-09-01

    Thirty-two male subjects were divided into four groups based on their performance on the remote associates test and alternate uses test, two measures of creativity. Right EEG alpha presence was monitored under basal conditions, while subjects took tests of creativity and intelligence, and while they attempted to enhance and suppress the amount of alpha in a feedback situation. High scorers on the alternate uses test operated at a high percentage of basal alpha during all tests while high scorers on the remote associates test showed differential amounts of alpha presence across tests, with the highest percentage of basal alpha during tests of creativity and the lowest percentage during an intellectual test. Both high creative groups tended to show increases in amount of alpha across trials when trying to suppress alpha as well as when trying to enhance it, but did not differ in overall control from the low creative groups.

  8. 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.

  9. Elements of EEG signal processing.

    Science.gov (United States)

    Rampil, I J

    1987-01-01

    It is difficult to find well controlled clinical studies comparing the utility of the numerous EEG measures which have been described. A brief summary of the literature reveals a number of promising case reports, a few large series of patients, and fewer well-designed, well-controlled studies. The very abundance of algorithms makes even technical comparisons of the ability of each to transduce and reflect clinical changes useful (27, 29). Most studies are positive; but thus far (as with so many procedures in medicine), there is no definitive study demonstrating an unequivocal benefit to patients who are monitored with automated, online EEG analysis. That such a study may never be done should not detract from the possible benefits these techniques may bring to clinical practice, most particularly preparing the way for effective brain monitoring in situations in which it was impractical before.

  10. 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.

  11. Use of EEG to Diagnose ADHD

    Science.gov (United States)

    Lenartowicz, Agatha; Loo, Sandra K.

    2015-01-01

    Electroencephalography (EEG) has, historically, played a focal role in the assessment of neural function in children with attention deficit hyperactivity disorder (ADHD). We review here the most recent developments in the utility of EEG in the diagnosis of ADHD, with emphasis on the most commonly used and emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical heterogeneity of ADHD and the complexity of information available from the EEG signals, we suggest that considerable benefits are to be gained from multivariate analyses and a focus towards understanding of the neural generators of EEG. We conclude that while EEG cannot currently be used as a diagnostic tool, vast developments in analytical and technological tools in its domain anticipate future progress in its utility in the clinical setting. PMID:25234074

  12. 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.

  13. Fractal Dimension in Eeg Signals during Muscle Fatigue

    Science.gov (United States)

    Huang, Haibin; Yao, Bin; Yue, Guang; Brown, Robert; Jing, Liu

    2003-10-01

    Fractal dimension (FD) has been successfully used to characterize signals in the format of time series. In this study, we calculated FD of EEG signals recorded during human muscle fatigue as a measure of changes in the EEG signal complexity along fatigue. Subjects performed 200 intermittent handgrip contractions at 100contraction level. Each contraction lasted 2 s, followed by a 5-s rest. EEG data were recorded from the scalp along with handgrip force and muscle EMG signals. The FD computation was based on measurements of the length (Lk) of the signal at 6 different temporal resolutions (k = 1, 2, ¡­, 6). FD was determined from the relationship between Lk and k using the least square fit. The results showed that: (1) EEG fractal dimension associated with the motor performance was significantly higher than that during the rest period; (2) changes in the fractal dimension along the process of fatigue showed a significant correlation with the decline in force and EMG signals.

  14. Quantitative EEG findings in different stages of Alzheimer's disease.

    Science.gov (United States)

    Kwak, Yong Tae

    2006-10-01

    Although quantitative EEG (q-EEG) has been used in Alzheimer's disease (AD), q-EEG changes in AD are complex because of the progressive nature of this disease. The topographical spectral power and occipital peak frequency (OPF) were compared among elderly controls, patients with mild cognitive impairment (MCI), and patients with four stages of AD. In AD patients, except those with a Clinical Dementia Rating Scale (CDR) score of 0.5, OPF was lower than that of elderly controls. Compared with elderly controls, the left anterior alpha spectral power was reduced in CDR 0.5; both posterior theta spectral powers were increased and all alpha spectral powers were reduced in CDR 1; all alpha and beta spectral powers were reduced and theta spectral power was increased in CDR 2; and all alpha and beta spectral powers were reduced and all delta and theta spectral powers were increased in CDR 3. Patients with MCI exhibited a reduction in both centrotemporal, posterior delta and left anterior, centrotemporal theta fields. The Mini-Mental State Examination (MMSE) score was related to left OPF, right posterior delta and left anterior theta spectral power, in that order. This study suggests that q-EEG in MCI shows nonoverlapping features between controls and AD patients, and AD patients show dynamic changes as the disease progresses. Finally, the left OPF is the parameter most significantly correlated with MMSE score.

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

    Directory of Open Access Journals (Sweden)

    Weisz Nathan

    2010-03-01

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

  16. ICA-based muscle artefact correction of EEG data: what is muscle and what is brain? Comment on McMenamin et al.

    Science.gov (United States)

    Olbrich, Sebastian; Jödicke, Johannes; Sander, Christian; Himmerich, Hubertus; Hegerl, Ulrich

    2011-01-01

    Independent component analysis (ICA)-based muscle artefact correction has become a popular tool within electroencephalographic (EEG) research. As a comment on the article by McMenamin et al. (2010), we want to address three issues concerning the claimed lack of sensitivity and specificity of this method. The under- or overestimation of myogenic and neurogenic signals after ICA-based muscle artefact correction reported by McMenamin et al. might be explainable in part by a) insufficient temporal independence of myogenic and neurogenic components when exploring more than one condition, b) wrong classification of myogenic or neurogenic components by human raters and c) differences of neuronal mass activity during tensed or relaxed-muscle conditions. Our own data show only significant differences regarding intracortical alpha band EEG-source estimates for contrasts between clean EEG data and artificially contaminated EEG data at group-analysis level but not between clean data and data after ICA-based correction. ICA-based artefact correction already provides a powerful tool for muscle artefact rejection. More research is needed for determining reliable criteria to delineate myogenic from neurogenic components.

  17. Independent EEG sources are dipolar.

    Directory of Open Access Journals (Sweden)

    Arnaud Delorme

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

  18. A high-speed brain-computer interface (BCI) using dry EEG electrodes

    Science.gov (United States)

    Spüler, Martin

    2017-01-01

    Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication. PMID:28225794

  19. Juvenile myoclonic epilepsy: clinical and EEG features

    DEFF Research Database (Denmark)

    Pedersen, S B; Petersen, K A

    1998-01-01

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

  20. Comparison of EEG propagation speeds under emotional stimuli on smartphone between the different anxiety states

    OpenAIRE

    Asakawa, Tetsuya; Muramatsu, Ayumi; Hayashi, Takuto; Urata, Tatsuya; Taya, Masato; Mizuno-Matsumoto, Yuko

    2014-01-01

    The current study evaluated the effect of different anxiety states on information processing as measured by an electroencephalography (EEG) using emotional stimuli on a smartphone. Twenty-three healthy subjects were assessed for their anxiety states using The State Trait Anxiety Inventory (STAI) and divided into two groups: low anxiety (I, II) or high anxiety (III and IV, V). An EEG was performed while the participant was presented with emotionally laden audiovisual stimuli (resting, pleasant...

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-11-30

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

  2. System Level spatial-frequency EEG changes coincident with a 90-day cognitive-behavioral therapy program for couples in relationship distress.

    Science.gov (United States)

    DuRousseau, Donald R; Beeton, Theresa A

    2015-09-01

    Evaluating relationship intervention programs traditionally involves the use of self-report surveys or observational studies to assess changes in behavior. Instead, to investigate intervention-related changes in behavior, our study evaluates spatial-frequency electroencephalography (EEG) patterns from the brains of couples participating in an Imago Relationship workshop and 12 weeks of group counseling sessions lasting approximately 90 days. This explorative study recorded 32-channel EEGs from nine committed distressed couples prior to, during and immediately following the Imago Relationship Therapy program. A repeated measures t-Test approach was applied to investigate if significant group level brain pattern changes could be identified in key resting state networks in the brains of the participants that could be correlated with changes in relationship outcome. The study results show that significant reductions in EEG power in the alpha2, beta3 and gamma bands were evident in the averaged brain activity in the pre-frontal, frontal and temporal-parietal cortices that are anatomically associated with the frontal executive, default mode and salience networks of the human brain. Our current understanding of system level neural connectivity and network dynamics strongly indicates that each of these systems is integrally required in learning and implementing a complex communication process taught in the Imago intervention. Thus, a high degree of hemispheric lateralization is consistent with our understanding of language function and mood regulation in the brain and is consistent with recent research into the use of resting frontal EEG asymmetry as an indicator of behavioral changes in distressed couples undergoing a program for relationship improvement. Although preliminary, these results further indicate that the EEG is an inexpensive and easily quantifiable measure, and possibly predictor, of behavioral changes in response to a cognitive behavioral intervention.

  3. Shifted coupling of EEG driving frequencies and fMRI resting state networks in schizophrenia spectrum disorders.

    Directory of Open Access Journals (Sweden)

    Nadja Razavi

    Full Text Available INTRODUCTION: The cerebral resting state in schizophrenia is altered, as has been demonstrated separately by electroencephalography (EEG and functional magnetic resonance imaging (fMRI resting state networks (RSNs. Previous simultaneous EEG/fMRI findings in healthy controls suggest that a consistent spatiotemporal coupling between neural oscillations (EEG frequency correlates and RSN activity is necessary to organize cognitive processes optimally. We hypothesized that this coupling is disorganized in schizophrenia and related psychotic disorders, in particular regarding higher cognitive RSNs such as the default-mode (DMN and left-working-memory network (LWMN. METHODS: Resting state was investigated in eleven patients with a schizophrenia spectrum disorder (n = 11 and matched healthy controls (n = 11 using simultaneous EEG/fMRI. The temporal association of each RSN to topographic spectral changes in the EEG was assessed by creating Covariance Maps. Group differences within, and group similarities across frequencies were estimated for the Covariance Maps. RESULTS: The coupling of EEG frequency bands to the DMN and the LWMN respectively, displayed significant similarities that were shifted towards lower EEG frequencies in patients compared to healthy controls. CONCLUSIONS: By combining EEG and fMRI, each measuring different properties of the same pathophysiology, an aberrant relationship between EEG frequencies and altered RSNs was observed in patients. RSNs of patients were related to lower EEG frequencies, indicating functional alterations of the spatiotemporal coupling. SIGNIFICANCE: The finding of a deviant and shifted coupling between RSNs and related EEG frequencies in patients with a schizophrenia spectrum disorder is significant, as it might indicate how failures in the processing of internal and external stimuli, as commonly seen during this symptomatology (i.e. thought disorders, hallucinations, arise.

  4. The amendment of the Renewable Energy Law (EEG); Die Novelle des Erneuerbare-Energien-Gesetzes (EEG)

    Energy Technology Data Exchange (ETDEWEB)

    Menze, Julian [Erdgas Muenster GmbH, Muenster (Germany)

    2011-07-01

    The Act for the reformation of the legal framework for the support of the power generation from renewable energy sources mainly consists of an amendment to the Renewable Energy Law (EEG) and becomes effective on 1st January, 2012. The author of the contribution under consideration reports on the most important new features of the EEG and gives an overview of the EEG 2012.

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

    Directory of Open Access Journals (Sweden)

    Lincoln J. Colling

    2017-07-01

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

  6. 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.

  7. Evidence of trace conditioning in comatose patients revealed by the reactivation of EEG responses to alerting sounds.

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    Juan, Elsa; Nguepnjo Nguissi, Nathalie Ata; Tzovara, Athina; Viceic, Dragana; Rusca, Marco; Oddo, Mauro; Rossetti, Andrea O; De Lucia, Marzia

    2016-11-01

    Trace conditioning refers to a learning process occurring after repeated presentation of a neutral conditioned stimulus (CS+) and a salient unconditioned stimulus (UCS) separated by a temporal gap. Recent studies have reported that trace conditioning can occur in humans in reduced levels of consciousness by showing a transfer of the unconditioned autonomic response to the CS+ in healthy sleeping individuals and in vegetative state patients. However, no previous studies have investigated the neural underpinning of trace conditioning in the absence of consciousness in humans. In the present study, we recorded the EEG activity of 29 post-anoxic comatose patients while presenting a trace conditioning paradigm using neutral tones as CS+ and alerting sounds as UCS. Most patients received therapeutic hypothermia and all were deeply unconscious according to standardized clinical scales. After repeated presentation of the CS+ and UCS couple, learning was assessed by measuring the EEG activity during the period where the UCS is omitted after CS+ presentation. Specifically we assessed the 'reactivation' of the neural response to UCS omission by applying a decoding algorithm derived from the statistical model of the EEG activity in response to the UCS presentation. The same procedure was used in a group of 12 awake healthy controls. We found a reactivation of the UCS response in absence of stimulation in eight patients (five under therapeutic hypothermia) and four healthy controls. Additionally, the reactivation effect was temporally specific within trials since it manifested primarily at the specific latency of UCS presentation and significantly less before or after this period. Our results show for the first time that trace conditioning may manifest as a reactivation of the EEG activity related to the UCS and even in the absence of consciousness.

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

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    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.

  9. Best current practice for obtaining high quality EEG data during simultaneous FMRI.

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    Mullinger, Karen J; Castellone, Pierluigi; Bowtell, Richard

    2013-06-03

    Simultaneous EEG-fMRI allows the excellent temporal resolution of EEG to be combined with the high spatial accuracy of fMRI. The data from these two modalities can be combined in a number of ways, but all rely on the acquisition of high quality EEG and fMRI data. EEG data acquired during simultaneous fMRI are affected by several artifacts, including the gradient artefact (due to the changing magnetic field gradients required for fMRI), the pulse artefact (linked to the cardiac cycle) and movement artifacts (resulting from movements in the strong magnetic field of the scanner, and muscle activity). Post-processing methods for successfully correcting the gradient and pulse artifacts require a number of criteria to be satisfied during data acquisition. Minimizing head motion during EEG-fMRI is also imperative for limiting the generation of artifacts. Interactions between the radio frequency (RF) pulses required for MRI and the EEG hardware may occur and can cause heating. This is only a significant risk if safety guidelines are not satisfied. Hardware design and set-up, as well as careful selection of which MR sequences are run with the EEG hardware present must therefore be considered. The above issues highlight the importance of the choice of the experimental protocol employed when performing a simultaneous EEG-fMRI experiment. Based on previous research we describe an optimal experimental set-up. This provides high quality EEG data during simultaneous fMRI when using commercial EEG and fMRI systems, with safety risks to the subject minimized. We demonstrate this set-up in an EEG-fMRI experiment using a simple visual stimulus. However, much more complex stimuli can be used. Here we show the EEG-fMRI set-up using a Brain Products GmbH (Gilching, Germany) MRplus, 32 channel EEG system in conjunction with a Philips Achieva (Best, Netherlands) 3T MR scanner, although many of the techniques are transferable to other systems.

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

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    Lipinski, C.G.

    1982-10-01

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

  11. A comparison of minimum norm and MUSIC for a combined MEG/EEG sensor array

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    Ahrens, H.; Argin, F.; Klinkenbusch, L.

    2012-09-01

    Many different algorithms for imaging neuronal activity with magnetoencephalography (MEG) or electroencephalography (EEG) have been developed so far. We validate the result of other authors that a combined MEG/EEG sensor array provides smaller source localisation errors than a single MEG or single EEG sensor array for the same total number of sensors. We show that Multiple Signal Classification (MUSIC) provides smaller localisation errors than an unweighted minimum norm method for activity located in the cortical sulcus regions. This is important for many medical applications, e.g. the localisation of the origin of epileptic seizures (focal epilepsy) that can be located very deep in the cortical sulcus.

  12. EEG Dominant Frequency Peak Differentiates Between Alzheimer's Disease and Frontotemporal Lobar Degeneration.

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    Goossens, Joery; Laton, Jorne; Van Schependom, Jeroen; Gielen, Jeroen; Struyfs, Hanne; Van Mossevelde, Sara; Van den Bossche, Tobi; Goeman, Johan; De Deyn, Peter Paul; Sieben, Anne; Martin, Jean-Jacques; Van Broeckhoven, Christine; van der Zee, Julie; Engelborghs, Sebastiaan; Nagels, Guy

    2017-01-01

    We investigated the power of EEG as biomarker in differential diagnosis of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). EEG was recorded from 106 patients with AD or FTLD, of which 37 had a definite diagnosis, and 40 controls. Dominant frequency peaks were extracted for all 19 channels, for each subject. The average frequency of the largest dominant frequency peaks (maxpeak) was significantly lower in AD than FTLD patients and controls. Based on ROC analysis, classification could be made with diagnostic accuracy of 78.9%. Our findings show that quantitative analysis of EEG maxpeak frequency is an easy and useful measure for differential dementia diagnosis.

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

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

    2017-02-01

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

  14. Classifying EEG Signals during Stereoscopic Visualization to Estimate Visual Comfort.

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    Frey, Jérémy; Appriou, Aurélien; Lotte, Fabien; Hachet, Martin

    2016-01-01

    With stereoscopic displays a sensation of depth that is too strong could impede visual comfort and may result in fatigue or pain. We used Electroencephalography (EEG) to develop a novel brain-computer interface that monitors users' states in order to reduce visual strain. We present the first system that discriminates comfortable conditions from uncomfortable ones during stereoscopic vision using EEG. In particular, we show that either changes in event-related potentials' (ERPs) amplitudes or changes in EEG oscillations power following stereoscopic objects presentation can be used to estimate visual comfort. Our system reacts within 1 s to depth variations, achieving 63% accuracy on average (up to 76%) and 74% on average when 7 consecutive variations are measured (up to 93%). Performances are stable (≈62.5%) when a simplified signal processing is used to simulate online analyses or when the number of EEG channels is lessened. This study could lead to adaptive systems that automatically suit stereoscopic displays to users and viewing conditions. For example, it could be possible to match the stereoscopic effect with users' state by modifying the overlap of left and right images according to the classifier output.

  15. Epileptic networks studied with EEG-fMRI.

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    Gotman, Jean

    2008-01-01

    It is not easy to determine the location of the cerebral generators and the other brain regions that may be involved at the time of an epileptic spike seen in the scalp EEG. The possibility to combine EEG recording with functional MRI scanning (fMRI) opens the opportunity to uncover the regions of the brain showing changes in metabolism and blood flow in response to epileptic spikes seen in the EEG. These regions are presumably involved in the abnormal neuronal activity at the origin of epileptic discharges. This paper reviews the methodology involved in performing such studies, including the special techniques required for recording the EEG inside the scanner and the statistical issues in analyzing the fMRI signal. We then discuss the results obtained in patients with different types of focal epileptic disorders and in patients with primary generalized epilepsy. The results in general indicate that interictal epileptic discharges may affect brain areas well beyond the presumed region in which they are generated. The noninvasive nature of this method opens new horizons in the investigation of brain regions involved and affected by epileptic discharges.

  16. Multi-scale symbolic transfer entropy analysis of EEG

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    Yao, Wenpo; Wang, Jun

    2017-10-01

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

  17. A stochastic model for EEG microstate sequence analysis.

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    Gärtner, Matthias; Brodbeck, Verena; Laufs, Helmut; Schneider, Gaby

    2015-01-01

    The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences.

  18. Reference layer adaptive filtering (RLAF) for EEG artifact reduction in simultaneous EEG-fMRI

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    Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R.

    2017-04-01

    Objective. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. Approach. To improve EEG data quality substantially, we introduce methods that use a reusable reference layer EEG cap prototype in combination with adaptive filtering. The first method, reference layer adaptive filtering (RLAF), uses adaptive filtering with reference layer artifact data to optimize artifact subtraction from EEG. In the second method, multi band reference layer adaptive filtering (MBRLAF), adaptive filtering is performed on bandwidth limited sub-bands of the EEG and the reference channels. Main results. The results suggests that RLAF outperforms the baseline method, average artifact subtraction, in all settings and also its direct predecessor, reference layer artifact subtraction (RLAS), in lower (mean-square voltage reduction and power reduction of EEG provided that physiological EEG components such as occipital EEG alpha power and visual evoked potentials (VEP) are preserved. We demonstrate that both, RLAF and MBRLAF, improve VEP quality. For that, we calculate the mean-squared-distance of single trial VEP to the mean VEP and estimate single trial VEP classification accuracies. We found that the average mean-squared-distance is lowest and the average classification accuracy is highest after MBLAF. RLAF was second best. Significance. In conclusion, the results suggests that RLAF and MBRLAF are potentially very effective in improving EEG quality of simultaneous EEG-fMRI. Highlights We present a new and reusable reference layer cap prototype for simultaneous EEG-fMRI We introduce new algorithms for reducing EEG artifacts due to simultaneous fMRI The algorithms combine a reference layer and adaptive filtering Several evaluation criteria suggest superior

  19. EEG-based classification of video quality perception using steady state visual evoked potentials (SSVEPs)

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    Acqualagna, Laura; Bosse, Sebastian; Porbadnigk, Anne K.; Curio, Gabriel; Müller, Klaus-Robert; Wiegand, Thomas; Blankertz, Benjamin

    2015-04-01

    Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.

  20. Insights into the mechanisms of absence seizure generation provided by EEG with Functional MRI.

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    Patrick William Carney

    2014-09-01

    Full Text Available Absence seizures are brief epileptic events characterized by loss of awareness with subtle motor features. They may be very frequent, and impact on attention, learning and memory. A number of pathophysiological models have been developed to explain the mechanism of absence seizure generation which rely heavily on observations from animal studies. Studying the structural and functional relationships between large-scale brain networks in humans is only practical with non-invasive whole brain techniques. EEG with functional MRI (EEG-fMRI is one such technique that provides an opportunity to explore the interactions between brain structures involved in AS generation. A number of fMRI techniques including event-related analysis, time course analysis and functional connectivity have identified a common network of structures involved in AS seizures. This network comprises the thalamus, midline and lateral parietal cortex (the default mode network [DMN], caudate nuclei and the reticular structures of the pons. The main component displaying an increase in BOLD signal relative to the resting state, in group studies, is the thalamus while the most consistent cortical change is reduced BOLD signal in the DMN. Time course analysis shows that, rather than some structures being activated or inactivated during AS, there appears to be increase in activity across components of the network preceding or following the electro-clinical onset of the seizure. The earliest change in BOLD signal occurs in the DMN, prior to the onset of epileptiform events. This region also shows altered functional connectivity in patients with absence seizures. Hence it appears that engagement of this network is central to absence seizures. In this review we will explore the insights EEG-fMRI studies into the mechanisms of AS and considers how the DMN is likely to be the major large scale brain network central to both seizure generation and the seizure manifestations.

  1. Focused ultrasound-mediated suppression of chemically-induced acute epileptic EEG activity

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    Chung Yong-An

    2011-03-01

    Full Text Available Abstract Background Epilepsy is a common neurological disorder, which is attributed to uncontrollable abnormal hyper-excitability of neurons. We investigated the feasibility of using low-intensity, pulsed radiation of focused ultrasound (FUS to non-invasively suppress epileptic activity in an animal model (rat, which was induced by the intraperitonial injection of pentylenetetrazol (PTZ. Results After the onset of induced seizures, FUS was transcranially administered to the brain twice for three minutes each while undergoing electroencephalographic (EEG monitoring. An air-backed, spherical segment ultrasound transducer (diameter: 6 cm; radius-of-curvature: 7 cm operating at a fundamental frequency of 690 KHz was used to deliver a train of 0.5 msec-long pulses of sonication at a repetitive rate of 100 Hz to the thalamic areas of the brain. The acoustic intensity (130 mW/cm2 used in the experiment was sufficiently within the range of safety guidelines for the clinical ultrasound imaging. The occurrence of epileptic EEG bursts from epilepsy-induced rats significantly decreased after sonication when it was compared to the pre-sonication epileptic state. The PTZ-induced control group that did not receive any sonication showed a sustained number of epileptic EEG signal bursts. The animals that underwent sonication also showed less severe epileptic behavior, as assessed by the Racine score. Histological analysis confirmed that the sonication did not cause any damage to the brain tissue. Conclusions These results revealed that low-intensity, pulsed FUS sonication suppressed the number of epileptic signal bursts using acute epilepsy model in animal. Due to its non-invasiveness and spatial selectivity, FUS may offer new perspectives for a possible non-invasive treatment of epilepsy.

  2. Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study.

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    Engels, Marjolein M A; Stam, Cornelis J; van der Flier, Wiesje M; Scheltens, Philip; de Waal, Hanneke; van Straaten, Elisabeth C W

    2015-08-20

    EEG studies have shown that patients with Alzheimer's disease (AD) have weaker functional connectivity than controls, especially in higher frequency bands. Furthermore, active regions seem more prone to AD pathology. How functional connectivity is affected in AD subgroups of disease severity and how network hubs (highly connected brain areas) change is not known. We compared AD patients with different disease severity and controls in terms of functional connections, hub strength and hub location. We studied routine 21-channel resting-state electroencephalography (EEG) of 318 AD patients (divided into tertiles based on disease severity: mild, moderate and severe AD) and 133 age-matched controls. Functional connectivity between EEG channels was estimated with the Phase Lag Index (PLI). From the PLI-based connectivity matrix, the minimum spanning tree (MST) was derived. For each node (EEG channel) in the MST, the betweenness centrality (BC) was computed, a measure to quantify the relative importance of a node within the network. Then we derived color-coded head plots based on BC values and calculated the center of mass (the exact middle had x and y values of 0). A shifting of the hub locations was defined as a shift of the center of mass on the y-axis across groups. Multivariate general linear models with PLI or BC values as dependent variables and the groups as continuous variables were used in the five conventional frequency bands. We found that functional connectivity decreases with increasing disease severity in the alpha band. All, except for posterior, regions showed increasing BC values with increasing disease severity. The center of mass shifted from posterior to more anterior regions with increasing disease severity in the higher frequency bands, indicating a loss of relative functional importance of the posterior brain regions. In conclusion, we observed decreasing functional connectivity in the posterior regions, together with a shifted hub location from

  3. Towards Semi-Automatic Artifact Rejection for the Improvement of Alzheimer's Disease Screening from EEG Signals.

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    Solé-Casals, Jordi; Vialatte, François-Benoît

    2015-07-23

    A large number of studies have analyzed measurable changes that Alzheimer's disease causes on electroencephalography (EEG). Despite being easily reproducible, those markers have limited sensitivity, which reduces the interest of EEG as a screening tool for this pathology. This is for a large part due to the poor signal-to-noise ratio of EEG signals: EEG recordings are indeed usually corrupted by spurious extra-cerebral artifacts. These artifacts are responsible for a consequent degradation of the signal quality. We investigate the possibility to automatically clean a database of EEG recordings taken from patients suffering from Alzheimer's disease and healthy age-matched controls. We present here an investigation of commonly used markers of EEG artifacts: kurtosis, sample entropy, zero-crossing rate and fractal dimension. We investigate the reliability of the markers, by comparison with human labeling of sources. Our results show significant differences with the sample entropy marker. We present a strategy for semi-automatic cleaning based on blind source separation, which may improve the specificity of Alzheimer screening using EEG signals.

  4. Similarity Analysis of EEG Data Based on Self Organizing Map Neural Network

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    Ibrahim Salem Jahan

    2014-01-01

    Full Text Available The Electroencephalography (EEG is the recording of electrical activity along the scalp. This recorded data are very complex. EEG has a big role in several applications such as in the diagnosis of human brain diseases and epilepsy. Also, we can use the EEG signals to control an external device via Brain Computer Interface (BCI by our mind. There are many algorithms to analyse the recorded EEG data, but it still remains one of the big challenges in the world. In this article, we extended our previous proposed method. Our extended method uses Self-organizing Map (SOM as an EEG data classifier. The proposed method we can divide in following steps: capturing EEG raw data from the sensors, applying filters on this data, we will use the frequencies in the range from 0.5~Hz to 60~Hz, smoothing the data with 15-th order of Polynomial Curve Fitting, converting filtered data into text using Turtle Graphic, Lempel-Ziv complexity for measuring similarity between two EEG data trials and Self-Organizing Map Neural Network as a final classifiers. The experiment results show that our model is able to detect up to 96% finger movements correctly.

  5. Automatic Removal of Artifacts from EEG Signal based on Spatially Constrained ICA using Daubechies Wavelet

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    Vandana Roy

    2014-07-01

    Full Text Available This paper presents a boon and amend technique for eradicating the artifacts from the Electroencephalogram (EEG signals. The abolition of artifacts from scalp EEGs is of considerable implication for both the computerized and visual investigation of fundamental brainwave activities. These noise sources increase the difficulty in analyzing the EEG and procurement clinical information related to pathology. Hence it is critical to design a procedure for diminution of such artifacts in EEG archives. This paper uses a blind extraction algorithm, appropriate for the generality of complex-valued sources and both complex noncircular and circular, is introduced. This is achieved based on higher order statistics of dormant sources, and using the de?ation approach Spatially-Constrained Independent Component Analysis (SCICA to separate the Independent Components (ICs from the initial EEG signal. As the next phase, level-4 daubechies wavelet db-4 is applied to extract the brain activity from purged artifacts, and lastly the artifacts are projected back and detracted from EEG signals to get clean EEG data. Here, thresholding plays an imperative role in delineating the artifacts and hence an improved thresholding technique called Otsu’s thresholding is applied. Experimental consequences show that the proposed technique results in better removal of artifacts.

  6. Functional brain networks in Alzheimer's disease: EEG analysis based on limited penetrable visibility graph and phase space method

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    Wang, Jiang; Yang, Chen; Wang, Ruofan; Yu, Haitao; Cao, Yibin; Liu, Jing

    2016-10-01

    In this paper, EEG series are applied to construct functional connections with the correlation between different regions in order to investigate the nonlinear characteristic and the cognitive function of the brain with Alzheimer's disease (AD). First, limited penetrable visibility graph (LPVG) and phase space method map single EEG series into networks, and investigate the underlying chaotic system dynamics of AD brain. Topological properties of the networks are extracted, such as average path length and clustering coefficient. It is found that the network topology of AD in several local brain regions are different from that of the control group with no statistically significant difference existing all over the brain. Furthermore, in order to detect the abnormality of AD brain as a whole, functional connections among different brain regions are reconstructed based on similarity of clustering coefficient sequence (CCSS) of EEG series in the four frequency bands (delta, theta, alpha, and beta), which exhibit obvious small-world properties. Graph analysis demonstrates that for both methodologies, the functional connections between regions of AD brain decrease, particularly in the alpha frequency band. AD causes the graph index complexity of the functional network decreased, the small-world properties weakened, and the vulnerability increased. The obtained results show that the brain functional network constructed by LPVG and phase space method might be more effective to distinguish AD from the normal control than the analysis of single series, which is helpful for revealing the underlying pathological mechanism of the disease.

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

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

    2017-01-07

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

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

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    Richard N Henson

    2011-08-01

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

  9. The effects of visual stimuli on EEG mu rhythms in healthy adults

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    Kim, JiYoung; Kim, SeongYoel

    2016-01-01

    [Purpose] Several action observation/imagery training studies have been conducted in patients with limited physical activity showing improvements in motor function. However, most studies compared effects of action observation and imagery, so little is known about the changes caused by subsequent observation of target objects. Moreover, few studies analyzed brain wave changes in the EEG mu rhythm. [Subjects and Methods] Eighteen healthy female adults participated in this study, and were divided into two groups: ‘Visual Stimuli’ and ‘Non-Visual Stimuli’. EEG amplitude in the 8–13 Hz frequency band over the sensorimotor cortex was evaluated. [Results] Significant mu suppression was obtained in the action observation trials. Mu power showed a main effect of visual stimuli, with decreased power during action observation, and increased power post-observation in both conditions. Comparing the ‘Visual Stimuli’ and ‘Non-Visual Stimuli’ conditions during the post-observation period, mu power demonstrated a greater increase in the ‘Non-Visual Stimuli’ condition. Furthermore, mu power was lower post-observation than pre-observation. [Conclusion] These results show the effects of visual input between maintaining target objects and no visual input, and their relevance to modulations of the mirror neuron system. It also suggests that greater visual input may be more effective for cognitive rehabilitation. PMID:27390408

  10. A Matter of Time: The Influence of Recording Context on EEG Spectral Power in Adolescents and Young Adults with ADHD.

    Science.gov (United States)

    Kitsune, Glenn L; Cheung, Celeste H M; Brandeis, Daniel; Banaschewski, Tobias; Asherson, Philip; McLoughlin, Gráinne; Kuntsi, Jonna

    2015-07-01

    Elevated theta or theta/beta ratio is often reported in attention deficit hyperactivity disorder (ADHD), but the consistency across studies and the relation to hypoarousal are increasingly questioned. Reports of elevated delta related to maturational lag and of attenuated beta activity are less well replicated. Some critical inconsistencies could relate to differences in recording context. We examined if resting-state EEG power or global field synchronization (GFS) differed between recordings made at the beginning and end of a 1.5 h testing session in 76 adolescents and young adults with ADHD, and 85 controls. In addition, we aimed to examine the effect of IQ on any potential group differences. Both regional and midline electrodes yielded group main effects for delta, trends in theta, but no differences in alpha or theta/beta ratio. An additional group difference in beta was detected when using regions. Group by time interactions in delta and theta became significant when controlling for IQ. The ADHD group had higher delta and theta power at time-1, but not at time-2, whereas beta power was elevated only at time-2. GFS did not differ between groups or condition. We show some ADHD-control differences on EEG spectral power varied with recording time within a single recording session, with both IQ and electrode selection having a small but significant influence on observed differences. Our findings demonstrate the effect of recording context on resting-state EEG, and highlight the importance of accounting for these variables to ensure consistency of results in future studies.

  11. Migraine aura lasting 1-24 h in children: a sequence of EEG slow-wave abnormalities vs. vascular events.

    Science.gov (United States)

    Parain, D; Hitzel, A; Guegan-Massardier, E; Lebas, A; Blondeau, C; Fédina, I; Feray, D; Véra, P; Mihout, B

    2007-09-01

    The aim of this study was to describe the abnormalities associated with migraine aura lasting 1-24 h in children as shown by EEG, trancranial Doppler (TCD) and single photon emission computed tomography (SPECT). In this retrospective study, 11 patients each underwent EEG, TCD and brain SPECT on the day of admission and the day thereafter. On the day of admission, the migrainous hemisphere of all patients showed that the mean velocities were decreased in the middle cerebral artery by TCD, slow-wave abnormalities were recorded after several hours of aura by EEG and the SPECT showed hypoperfusion. On the day after, in the same hemisphere, slow waves were recorded only in the occipital area by EEG, and SPECT showed slight hyperperfusion. In these patients, there was a clear sequence of EEG, TCD and SPECT abnormalities.

  12. 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

  13. Sensorimotor cortical response during motion reflecting audiovisual stimulation: evidence from fractal EEG analysis.

    Science.gov (United States)

    Hadjidimitriou, S; Zacharakis, A; Doulgeris, P; Panoulas, K; Hadjileontiadis, L; Panas, S

    2010-06-01

    Sensorimotor activity in response to motion reflecting audiovisual titillation is studied in this article. EEG recordings, and especially the Mu-rhythm over the sensorimotor cortex (C3, CZ, and C4 electrodes), were acquired and explored. An experiment was designed to provide auditory (Modest Mussorgsky's "Promenade" theme) and visual (synchronized human figure walking) stimuli to advanced music students (AMS) and non-musicians (NM) as a control subject group. EEG signals were analyzed using fractal dimension (FD) estimation (Higuchi's, Katz's and Petrosian's algorithms) and statistical methods. Experimental results from the midline electrode (CZ) based on the Higuchi method showed significant differences between the AMS and the NM groups, with the former displaying substantial sensorimotor response during auditory stimulation and stronger correlation with the acoustic stimulus than the latter. This observation was linked to mirror neuron system activity, a neurological mechanism that allows trained musicians to detect action-related meanings underlying the structural patterns in musical excerpts. Contrarily, the response of AMS and NM converged during audiovisual stimulation due to the dominant presence of human-like motion in the visual stimulus. These findings shed light upon music perception aspects, exhibiting the potential of FD to respond to different states of cortical activity.

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

    Directory of Open Access Journals (Sweden)

    Adrienne L Tierney

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

  15. Modeling analysis of the relationship between EEG rhythms and connectivity among different neural populations.

    Science.gov (United States)

    Ursino, Mauro; Zavaglia, Melissa

    2007-12-01

    In our research, a neural mass model consisting of four interconnected neural groups (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow synaptic kinetics, and inhibitory interneurons with fast synaptic kinetics) is used to investigate the mechanisms which cause the appearance of multiple rhythms in EEG spectra, and to assess how these rhythms can be affected by connectivity among different populations. First, we showed that a single neural population, stimulated with white noise, can oscillate with its intrinsic rhythm, and that the position of this rhythm can be finely tuned (in the alpha, beta or gamma frequency ranges), acting on the population synaptic kinetics. Subsequently, we analyzed more complex circuits, composed of two or three interconnected populations, each with a different synaptic kinetics between neural groups within a population (hence, with a different intrinsic rhythm). The results demonstrates apex that a single population can exhibit many different simultaneous rhythms, provided that some of these come from external sources (for instance, from remote regions). The analysis of coherence, and of the position of the peaks in power spectral density, reveals important information on the possible connections among populations, and is especially useful to follow temporal changes in connectivity. In perspective, the results may be of value for a deeper comprehension of the mechanisms causing EEGs rhythms, for the study of connectivity among different neural populations and for the test of neurophysiological hypotheses.

  16. Automatic seizure detection: going from sEEG to iEEG

    DEFF Research Database (Denmark)

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

    2010-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Shih-Cheng Liao

    2017-06-01

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

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

    Science.gov (United States)

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

    2008-07-01

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

  19. 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.

  20. 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.

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

    Science.gov (United States)

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

    2016-03-01

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

  2. Impact of the reference choice on scalp EEG connectivity estimation

    Science.gov (United States)

    Chella, Federico; Pizzella, Vittorio; Zappasodi, Filippo; Marzetti, Laura

    2016-06-01

    Objective. Several scalp EEG functional connectivity studies, mostly clinical, seem to overlook the reference electrode impact. The subsequent interpretation of brain connectivity is thus often biased by the choice of a non-neutral reference. This study aims at systematically investigating these effects. Approach. As EEG reference, we examined the vertex electrode (Cz), the digitally linked mastoids (DLM), the average reference (AVE), and the reference electrode standardization technique (REST). As a connectivity metric, we used the imaginary part of the coherency. We tested simulated and real data (eyes-open resting state) by evaluating the influence of electrode density, the effect of head model accuracy in the REST transformation, and the impact on the characterization of the topology of functional networks from graph analysis. Main results. Simulations demonstrated that REST significantly reduced the distortion of connectivity patterns when compared to AVE, Cz, and DLM references. Moreover, the availability of high-density EEG systems and an accurate knowledge of the head model are crucial elements to improve REST performance, with the individual realistic head model being preferable to the standard realistic head model. For real data, a systematic change of the spatial pattern of functional connectivity depending on the chosen reference was also observed. The distortion of connectivity patterns was larger for the Cz reference, and progressively decreased when using the DLM, the AVE, and the REST. Strikingly, we also showed that network attributes derived from graph analysis, i.e. node degree and local efficiency, are significantly influenced by the EEG reference choice. Significance. Overall, this study highlights that significant differences arise in scalp EEG functional connectivity and graph network properties, in dependence on the chosen reference. We hope that our study will convey the message that caution should be used when interpreting and comparing

  3. Resting EEG asymmetry and spider phobia

    NARCIS (Netherlands)

    Merckelbach, H; Muris, P; Pool, K; de Jong, Peter

    1998-01-01

    This study examined whether resting EEG asymmetries are related to symptom severity and treatment outcome in spider phobia. Prior to treatment, EEG was recorded in a sample of spider phobic patients (N = 16). Correlations between frontal and parietal asymmetries in alpha power, on the one hand, and

  4. Objective Audiometry using Ear-EEG

    DEFF Research Database (Denmark)

    Christensen, Christian Bech; Kidmose, Preben

    life. Ear-EEG may therefore be an enabling technology for objective audiometry out of the clinic, allowing regularly fitting of the hearing aids to be made by the users in their everyday life environment. In this study we investigate the application of ear-EEG in objective audiometry....

  5. Continuous EEG Monitoring in Aneurysmal Subarachnoid Hemorrhage

    DEFF Research Database (Denmark)

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

    2015-01-01

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

  6. Nonlinear analysis of the alcoholic's EEG

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The nonlinear analysis is used to study the EEGs of the alcoholic and the control. Three kinds of expansions are discussed in order to get a more proper delay and then Liangyue Cao algorithm is implemented efficiently. Totally, there are 40 subjects involved in this study and the average values and the sample variances of D2s are computed. The results show that the average value of D2s of the alcoholic is larger than that of the control when the same electrode was used, which means that the brain dynamics of the alcoholic is more complex than that of the control. On the other hand, for most of the electrodes, the sample variance of D2s of the alcoholic is larger than that of the control, suggesting that the brain dynamics of the alcoholic is less steady.

  7. EMG parameters and EEG α Index change at fatigue period during different types of muscle contraction

    Science.gov (United States)

    Zhang, Li; Zhou, Bin; Song, Gaoqing

    2011-03-01

    The purpose of this study is to measure and analyze the characteristics in change of EMG and EEG parameters at muscle fatigue period in participants with different exercise capacity. Twenty participants took part in the tests. They were divided into two groups, Group A (constant exerciser) and Group B (seldom-exerciser). MVC dynamic and 1/3 isometric exercises were performed; EMG and EEG signals were recorded synchronously during different type of muscle contraction. Results indicated that values of MVC, RMS and IEMG in Group A were greater than Group B, but isometric exercise time was shorter than the time of dynamic exercise although its intensity was light. Turning point of IEMG and α Index occurred synchronously during constant muscle contraction of isometric or dynamic exercise. It is concluded that IEMG turning point may be an indication to justify muscle fatigue. Synchronization of EEG and EMG reflects its common characteristics on its bio-electric change.

  8. Wearable EEG via lossless compression.

    Science.gov (United States)

    Dufort, Guillermo; Favaro, Federico; Lecumberry, Federico; Martin, Alvaro; Oliver, Juan P; Oreggioni, Julian; Ramirez, Ignacio; Seroussi, Gadiel; Steinfeld, Leonardo

    2016-08-01

    This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176μW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.

  9. [EEG correlates of social creativity].

    Science.gov (United States)

    Razumnikova, O M; Finikov, S B

    2010-01-01

    EEG correlates of social creativity defined as ability to originally and flexibly interpret social significant situations were studied. It was found that the alpha2 and gamma2 rhythms are specific bands which make it possible to tell the difference between social creativity and control task. Solving socially significant problems in experimental conditions is accompanied by an increase in the power of the delta and gamma2 bands and desynchronization in the alpha2 band less pronounced in divergent tasks than during the interpretation of convergent visual stimuli.

  10. Neural complexity in patients with poststroke depression: A resting EEG study.

    Science.gov (United States)

    Zhang, Ying; Wang, Chunfang; Sun, Changcheng; Zhang, Xi; Wang, Yongjun; Qi, Hongzhi; He, Feng; Zhao, Xin; Wan, Baikun; Du, Jingang; Ming, Dong

    2015-12-01

    Poststroke depression (PSD) is one of the most common emotional disorders affecting post-stroke patients. However, the neurophysiological mechanism remains elusive. This study was aimed to study the relationship between complexity of neural electrical activity and PSD. Resting state eye-closed electroencephalogram (EEG) signals of 16 electrodes were recorded in 21 ischemic poststroke depression (PSD) patients, 22 ischemic poststroke non-depression (PSND) patients and 15 healthy controls (CONT). Lempel-Ziv Complexity (LZC) was used to evaluate changes in EEG complexity in PSD patients. Statistical analysis was performed to explore difference among different groups and electrodes. Correlation between the severity of depression (HDRS) and EEG complexity was determined with pearson correlation coefficients. Receiver operating characteristic (ROC) and binary logistic regression analysis were conducted to estimate the discriminating ability of LZC for PSD in specificity, sensitivity and accuracy. PSD patients showed lower neural complexity compared with PSND and CONT subjects in the whole brain regions. There was no significant difference among different brain regions, and no interactions between group and electrodes. None of the LZC significantly correlated with overall depression severity or differentiated symptom severity of 7 items in PSD patients, but in stroke patients, significant correlation was found between HDRS and LZC in the whole brain regions, especially in frontal and temporal. LZC parameters used for PSD recognition possessed more than 85% in specificity, sensitivity and accuracy, suggesting the feasibility of LZC to serve as screening indicators for PSD. Increased slow wave rhythms were found in PSD patients and clearly correlation was confirmed between neuronal complexity and spectral power of the four EEG rhythms. Lesion location of stroke patients in the study distributed in different brain regions, and most of the PSD patients were mild or moderate

  11. An online EEG BCI based on covert visuospatial attention in absence of exogenous stimulation

    Science.gov (United States)

    Tonin, L.; Leeb, R.; Sobolewski, A.; Millán, J. del R.

    2013-10-01

    Objective. In this work we present—for the first time—the online operation of an electroencephalogram (EEG) brain-computer interface (BCI) system based on covert visuospatial attention (CVSA), without relying on any evoked responses. Electrophysiological correlates of pure top-down CVSA have only recently been proposed as a control signal for BCI. Such systems are expected to share the ease of use of stimulus-driven BCIs (e.g. P300, steady state visually evoked potential) with the autonomy afforded by decoding voluntary modulations of ongoing activity (e.g. motor imagery). Approach. Eight healthy subjects participated in the study. EEG signals were acquired with an active 64-channel system. The classification method was based on a time-dependent approach tuned to capture the most discriminant spectral features of the temporal evolution of attentional processes. The system was used by all subjects over two days without retraining, to verify its robustness and reliability. Main results. We report a mean online accuracy across the group of 70.6 ± 1.5%, and 88.8 ± 5.8% for the best subject. Half of the participants produced stable features over the entire duration of the study. Additionally, we explain drops in performance in subjects showing stable features in terms of known electrophysiological correlates of fatigue, suggesting the prospect of online monitoring of mental states in BCI systems. Significance. This work represents the first demonstration of the feasibility of an online EEG BCI based on CVSA. The results achieved suggest the CVSA BCI as a promising alternative to standard BCI modalities.

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

    Science.gov (United States)

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

    2015-08-01

    Attention problems are common in youngsters with attention deficit hyperactivity disorder (ADHD) as well as in adolescents with combined autism spectrum disorder (ASD) and ADHD. However, it is unknown whether there is psychophysiological overlap and/or a difference in electroencephalogram (EEG) power spectra between ADHD and comorbid ASD and ADHD (ASD + ADHD), on and off stimulant medication. To explore potential differences and overlap, measures of theta and beta power in adolescents diagnosed with ADHD (n = 33) versus adolescents with combined ASD + ADHD (n = 20), categorized by stimulant medication use (57 % of the total sample), were compared. EEG measures were acquired in three conditions: (1) resting state, eyes closed (2) resting state, eyes open and (3) during an oddball task. In addition, performance on the d2 attention test was analyzed. Adolescents with ADHD displayed more absolute theta activity than adolescents with ASD + ADHD during the eyes open and task conditions, independent of stimulant medication use. In addition, only the adolescents with ADHD showed an association between diminished attention test performance and increased theta in the eyes open condition. Results of the current study suggest that although there is behavioral overlap between ADHD characteristics in adolescents with ADHD and adolescents with combined ASD + ADHD, the underlying psychophysiological mechanisms may be different. Adolescents with ASD + ADHD exhibited fewer of the EEG physiological signs usually associated with ADHD, although there was an overlap in attentional problems between the groups. This may indicate that treatments developed for ADHD work differently in some adolescents with ASD + ADHD and adolescents with ADHD only.

  13. Spatial patterning of the neonatal EEG suggests a need for a high number of electrodes.

    Science.gov (United States)

    Odabaee, Maryam; Freeman, Walter J; Colditz, Paul B; Ramon, Ceon; Vanhatalo, Sampsa

    2013-03-01

    There is an increasing demand for source analysis of neonatal EEG, but currently there is inadequate knowledge about i) the spatial patterning of neonatal scalp EEG and hence ii) the number of electrodes needed to capture neonatal EEG in full spatial detail. This study addresses these issues by using a very high density (2.5mm interelectrode spacing) linear electrode array to assess the spatial power spectrum, by using a high density (64 electrodes) EEG cap to assess the spatial extent of the common oscillatory bouts in the neonatal EEG and by using a neonatal size spherical head model to assess the effects of source depth and skull conductivities on the spatial frequency spectrum. The linear array recordings show that the spatial power spectrum decays rapidly until about 0.5-0.8 cycles per centimeter. The dense array EEG recordings show that the amplitude of oscillatory events decays within 4-6 cm to the level of global background activity, and that the higher frequencies (12-20 Hz) show the most rapid spatial decline in amplitude. Simulation with spherical head model showed that realistic variation in skull conductivity and source depths can both introduce orders of magnitude difference in the spatial frequency of the scalp EEG. Calculation of spatial Nyquist frequencies from the spatial power spectra suggests that an interelectrode distance of about 6-10mm would suffice to capture the full spatial texture of the raw EEG signal at the neonatal scalp without spatial aliasing or under-sampling. The spatial decay of oscillatory events suggests that a full representation of their spatial characteristics requires an interelectrode distance of 10-20mm. The findings show that the conventional way of recording neonatal EEG with about 10 electrodes ignores most spatial EEG content, that increasing the electrode density is necessary to improve neonatal EEG source localization and information extraction, and that prospective source models will need to carefully consider the

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

    Science.gov (United States)

    Markovska-Simoska, Silvana; Pop-Jordanova, Nada

    2017-01-01

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

  15. The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial.

    Science.gov (United States)

    Calabrò, Rocco Salvatore; Naro, Antonino; Russo, Margherita; Leo, Antonino; De Luca, Rosaria; Balletta, Tina; Buda, Antonio; La Rosa, Gianluca; Bramanti, Alessia; Bramanti, Placido

    2017-06-07

    Many studies have demonstrated the usefulness of repetitive task practice by using robotic-assisted gait training (RAGT) devices, including Lokomat, for the treatment of lower limb paresis. Virtual reality (VR) has proved to be a valuable tool to improve neurorehabilitation training. The aim of our pilot randomized clinical trial was to understand the neurophysiological basis of motor function recovery induced by the association between RAGT (by using Lokomat device) and VR (an animated avatar in a 2D VR) by studying electroencephalographic (EEG) oscillations. Twenty-four patients suffering from a first unilateral ischemic stroke in the chronic phase were randomized into two groups. One group performed 40 sessions of Lokomat with VR (RAGT + VR), whereas the other group underwent Lokomat without VR (RAGT-VR). The outcomes (clinical, kinematic, and EEG) were measured before and after the robotic intervention. As compared to the RAGT-VR group, all the patients of the RAGT + VR group improved in the Rivermead Mobility Index and Tinetti Performance Oriented Mobility Assessment. Moreover, they showed stronger event-related spectral perturbations in the high-γ and β bands and larger fronto-central cortical activations in the affected hemisphere. The robotic-based rehabilitation combined with VR in patients with chronic hemiparesis induced an improvement in gait and balance. EEG data suggest that the use of VR may entrain several brain areas (probably encompassing the mirror neuron system) involved in motor planning and learning, thus leading to an enhanced motor performance. Retrospectively registered in Clinical Trials on 21-11-2016, n. NCT02971371 .

  16. The Effect of Touching a Dolphin on the EEG Slow Waves hi Children

    OpenAIRE

    HOMMA Ayako:筆頭著者; Hara, Hideki; MATSUZAKI Kumiko; SASAKI Miki; MASAOKA Yuri; Homma, Ikuo

    2011-01-01

    Among animal-facilitated therapies, dolphin-facilitated therapy has been shown to beneficially affect human behavior, emotion and speech ability. We recently showed that touching a dolphin reduced the respiratory rate and state anxiety in healthy children. In this study, we collected electroencephalographic data (EEG), widely used for examining various brain functions, before and after touching dolphins. We examined the relationship between EEG power spectra and individual trait anxiety score...

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

    Science.gov (United States)

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

    2016-12-01

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

  18. EEG and MEG: sensitivity to epileptic spike activity as function of source orientation and depth.

    Science.gov (United States)

    Hunold, A; Funke, M E; Eichardt, R; Stenroos, M; Haueisen, J

    2016-07-01

    Simultaneous electroencephalography (EEG) and magnetoencephalography (MEG) recordings of neuronal activity from epileptic patients reveal situations in which either EEG or MEG or both modalities show visible interictal spikes. While different signal-to-noise ratios (SNRs) of the spikes in EEG and MEG have been reported, a quantitative relation of spike source orientation and depth as well as the background brain activity to the SNR has not been established. We investigated this quantitative relationship for both dipole and patch sources in an anatomically realistic cortex model. Altogether, 5600 dipole and 3300 patch sources were distributed on the segmented cortical surfaces of two volunteers. The sources were classified according to their quantified depths and orientations, ranging from 20 mm to 60 mm below the skin surface and radial and tangential, respectively. The source time-courses mimicked an interictal spike, and the simulated background activity emulated resting activity. Simulations were conducted with individual three-compartment boundary element models. The SNR was evaluated for 128 EEG, 102 MEG magnetometer, and 204 MEG gradiometer channels. For superficial dipole and superficial patch sources, EEG showed higher SNRs for dominantly radial orientations, and MEG showed higher values for dominantly tangential orientations. Gradiometers provided higher SNR than magnetometers for superficial sources, particularly for those with dominantly tangential orientations. The orientation dependent difference in SNR in EEG and MEG gradually changed as the sources were located deeper, where the interictal spikes generated higher SNRs in EEG compared to those in MEG for all source orientations. With deep sources, the SNRs in gradiometers and magnetometers were of the same order. To better detect spikes, both EEG and MEG should be used.

  19. Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding

    DEFF Research Database (Denmark)

    Kirov, Roumen; Weiss, Carsten; Siebner, Hartwig R

    2009-01-01

    The application of transcranial slow oscillation stimulation (tSOS; 0.75 Hz) was previously shown to enhance widespread endogenous EEG slow oscillatory activity when applied during a sleep period characterized by emerging endogenous slow oscillatory activity. Processes of memory consolidation...... typically occurring during this state of sleep were also enhanced. Here, we show that the same tSOS applied in the waking brain also induced an increase in endogenous EEG slow oscillations (0.4-1.2 Hz), although in a topographically restricted fashion. Applied during wakefulness tSOS, additionally, resulted...... in a marked and widespread increase in EEG theta (4-8 Hz) activity. During wake, tSOS did not enhance consolidation of memories when applied after learning, but improved encoding of hippocampus-dependent memories when applied during learning. We conclude that the EEG frequency and related memory processes...

  20. EEG Event-Related Desynchronization of patients with stroke during motor imagery of hand movement

    Science.gov (United States)

    Tabernig, Carolina B.; Carrere, Lucía C.; Lopez, Camila A.; Ballario, Carlos

    2016-04-01

    Brain Computer Interfaces (BCI) can be used for therapeutic purposes to improve voluntary motor control that has been affected post stroke. For this purpose, desynchronization of sensorimotor rhythms of the electroencephalographic signal (EEG) can be used. But it is necessary to study what happens in the affected motor cortex of this people. In this article, we analyse EEG recordings of hemiplegic stroke patients to determine if it is possible to detect desynchronization in the affected motor cortex during the imagination of movements of the affected hand. Six patients were included in the study; four evidenced desynchronization in the affected hemisphere, one of them showed no results and the EEG recordings of the last patient presented high noise level. These results suggest that we could use the desynchronization of sensorimotor rhythms of the EEG signal as a BCI paradigm in a rehabilitation programme.

  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. Sort entropy-based for the analysis of EEG during anesthesia

    Science.gov (United States)

    Ma, Liang; Huang, Wei-Zhi

    2010-08-01

    The monitoring of anesthetic depth is an absolutely necessary procedure in the process of surgical operation. To judge and control the depth of anesthesia has become a clinical issue which should be resolved urgently. EEG collected wiil be processed by sort entrop in this paper. Signal response of the surface of the cerebral cortex is determined for different stages of patients in the course of anesthesia. EEG is simulated and analyzed through the fast algorithm of sort entropy. The results show that discipline of phasic changes for EEG is very detected accurately,and it has better noise immunity in detecting the EEG anaesthetized than approximate entropy. In conclusion,the computing of Sort entropy algorithm requires shorter time. It has high efficiency and strong anti-interference.

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

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

  6. 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.

  7. 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.

  8. Frontal EEG/ERP correlates of attentional processes, cortisol and motivational states in adolescents from lower and higher socioeconomic status

    Directory of Open Access Journals (Sweden)

    Amedeo eD'angiulli

    2012-11-01

    Full Text Available Event-related potentials (ERPs and other electroencephalographic (EEG evidence show that frontal brain areas of higher and lower socioeconomic status (SES children are recruited differently during selective attention tasks. We assessed whether multiple variables related to self-regulation (perceived mental effort emotional states (e.g., anxiety, stress, etc. and motivational states (e.g., boredom, engagement, etc. may co-occur or interact with frontal attentional processing probed in two matched-samples of fourteen lower-SES and higher-SES adolescents. ERP and EEG activation were measured during a task probing selective attention to sequences of tones. Pre- and post-task salivary cortisol and self-reported emotional states were also measured. At similar performance level, the higher-SES group showed a greater ERP differentiation between attended (relevant and unattended (irrelevant tones than the lower-SES group. EEG power analysis revealed a cross-over interaction, specifically, lower-SES adolescents showed significantly higher theta power when ignoring rather than attending to tones, whereas, higher-SES adolescents showed the opposite pattern. Significant theta asymmetry differences were also found at midfrontal electrodes indicating left hypo-activity in lower-SES adolescents. The attended vs. unattended difference in right midfrontal theta increased with individual SES rank, and (independently from SES with lower cortisol task reactivity and higher boredom. Results suggest lower-SES children used additional compensatory resources to monitor/control response inhibition to distracters, perceiving also more mental effort, as compared to higher-SES counterparts. Nevertheless, stress, boredom and other task-related perceived states were unrelated to SES. Ruling out presumed confounds, this study confirms the midfrontal mechanisms responsible for the SES effects on selective attention reported previously and here reflect genuine cognitive

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

    LENUS (Irish Health Repository)

    O'Rourke, K

    2012-02-03

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

  10. EFFICACY OF ACTIVATION PROCEDURES TO ILLUSTRATE EEG CHANGES IN EPILEPSY

    Directory of Open Access Journals (Sweden)

    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.

  11. EEG-correlated fMRI of P3b component in P300 waves

    Institute of Scientific and Technical Information of China (English)

    LI Yuezhi; WANG Liqun; WANG Mingshi

    2005-01-01

    Electroencephalography-correlated functional magnetic resonance imaging (EEG/fMRI) can be used to identify blood oxygen level-dependent (BOLD) signal changes associated with both physiological and pathological EEG events. Here, we implemented continuous and simultaneous EEG/fMRI to identify BOLD signal changes related to P3b component of P300, and 64 channels of EEG were recorded in 11 subjects during Landot Ring task inside a 1.5 T functional magnet resonance (MR) scanner using an MR-compatible EEG recording system. Functional scanning by echoplanar imaging covered almost the entire cerebrum every 2 s, leaving gaps of 2 s without scanning. Off-line MRI artifact subtraction software was applied to obtain continuous EEG data. Additionally, a P300 wave matched filter was constructed to inspect P300 wave occurrence following every target stimulus, target stimuli inspected to induce P300 were detected and their MRI scan number were then used as input for an event-related fMRI analysis. Finally MRI statistical parametric maps were constructed and corrected for multiple comparisons. By random effect group analysis, activations were detected in the right superior parietal lobule and bilaterally in inferior parietal lobule(p<0.001, uncorrected). The results demonstrated the upper regions were sources of P3b component and involved in target detection in memory comparison task.

  12. ICTAL AND INTERICTAL EEG ABNORMALITIES IN 100 MIGRAINEURS WITH AND WITHOUT AURA

    OpenAIRE

    2007-01-01

    There are several conflicting reports about the EEG of the migraineurs. In this study we report the ictal and interictal EEGs of 100 migraineurs, in comparison with control group. The range age for patient and control groups were 9-48 (mean: 26 ± 1.8) and 10-46 (mean: 23 ± 2.1) years respectively. 32% of the patients were less than 14 years old and the remaining 68% were more than 14 years. In the patient group, 68% of cases had migraine without aura and 32% suffered fro...

  13. EEG complexity as a biomarker for autism spectrum disorder risk

    Directory of Open Access Journals (Sweden)

    Tierney Adrienne

    2011-02-01

    Full Text Available Abstract Background Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD, defined on the basis of an older sibling with ASD. Methods Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. Results Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90% at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. Conclusions This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is

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

    Institute of Scientific and Technical Information of China (English)

    JIANG Zheng-yan

    2005-01-01

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

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

    Directory of Open Access Journals (Sweden)

    João Paulo Silva Cunha

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

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

    Science.gov (United States)

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

    2016-01-01

    Epilepsy is a common neurological disorder which affects 0.5–1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure−induced body movements − called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video−EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists’ subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient’s body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m3 vs. 0.14 m3, p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms−3 vs 172 ms−3, p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting

  17. Transcranial direct current stimulation and EEG-based motor imagery BCI for upper limb stroke rehabilitation.

    Science.gov (United States)

    Ang, Kai Keng; Guan, Cuntai; Phua, Kok Soon; Wang, Chuanchu; Teh, Irvin; Chen, Chang Wu; Chew, Effie

    2012-01-01

    Clinical studies had shown that EEG-based motor imagery Brain-Computer Interface (MI-BCI) combined with robotic feedback is effective in upper limb stroke rehabilitation, and transcranial Direct Current Stimulation (tDCS) combined with other rehabilitation techniques further enhanced the facilitating effect of tDCS. This motivated the current clinical study to investigate the effects of combining tDCS with MI-BCI and robotic feedback compared to sham-tDCS for upper limb stroke rehabilitation. The stroke patients recruited were randomized to receive 20 minutes of tDCS or sham-tDCS prior to 10 sessions of 1-hour MI-BCI with robotic feedback for 2 weeks. The online accuracies of detecting motor imagery from idle condition were assessed and offline accuracies of classifying motor imagery from background rest condition were assessed from the EEG of the evaluation and therapy parts of the 10 rehabilitation sessions respectively. The results showed no evident differences between the online accuracies on the evaluation part from both groups, but the offline analysis on the therapy part yielded higher averaged accuracies for subjects who received tDCS (n=3) compared to sham-tDCS (n=2). The results suggest towards tDCS effect in modulating motor imagery in stroke, but a more conclusive result can be drawn when more data are collected in the ongoing study.

  18. Classification of EEG Single Trial Microstates Using Local Global Graphs and Discrete Hidden Markov Models.

    Science.gov (United States)

    Michalopoulos, Kostas; Zervakis, Michalis; Deiber, Marie-Pierre; Bourbakis, Nikolaos

    2016-09-01

    We present a novel synergistic methodology for the spatio-temporal analysis of single Electroencephalogram (EEG) trials. This new methodology is based on the novel synergy of Local Global Graph (LG graph) to characterize define the structural features of the EEG topography as a global descriptor for robust comparison of dominant topographies (microstates) and Hidden Markov Models (HMM) to model the topographic sequence in a unique way. In particular, the LG graph descriptor defines similarity and distance measures that can be successfully used for the difficult comparison of the extracted LG graphs in the presence of noise. In addition, hidden states represent periods of stationary distribution of topographies that constitute the equivalent of the microstates in the model. The transitions between the different microstates and the formed syntactic patterns can reveal differences in the processing of the input stimulus between different pathologies. We train the HMM model to learn the transitions between the different microstates and express the syntactic patterns that appear in the single trials in a compact and efficient way. We applied this methodology in single trials consisting of normal subjects and patients with Progressive Mild Cognitive Impairment (PMCI) to discriminate these two groups. The classification results show that this approach is capable to efficiently discriminate between control and Progressive MCI single trials. Results indicate that HMMs provide physiologically meaningful results that can be used in the syntactic analysis of Event Related Potentials.

  19. Decreased Modulation of EEG Oscillations in High-Functioning Autism during a Motor Control Task

    Science.gov (United States)

    Ewen, Joshua B.; Lakshmanan, Balaji M.; Pillai, Ajay S.; McAuliffe, Danielle; Nettles, Carrie; Hallett, Mark; Crone, Nathan E.; Mostofsky, Stewart H.

    2016-01-01

    Autism spectrum disorders (ASD) are thought to result in part from altered cortical excitatory-inhibitory balance; this pathophysiology may impact the generation of oscillations on electroencephalogram (EEG). We investigated premotor-parietal cortical physiology associated with praxis, which has strong theoretical and empirical associations with ASD symptomatology. Twenty five children with high-functioning ASD (HFA) and 33 controls performed a praxis task involving the pantomiming of tool use, while EEG was recorded. We assessed task-related modulation of signal power in alpha and beta frequency bands. Compared with controls, subjects with HFA showed 27% less left central (motor/premotor) beta (18–22 Hz) event-related desynchronization (ERD; p = 0.030), as well as 24% less left parietal alpha (7–13 Hz) ERD (p = 0.046). Within the HFA group, blunting of central ERD attenuation was associated with impairments in clinical measures of praxis imitation (r = −0.4; p = 0.04) and increased autism severity (r = 0.48; p = 0.016). The modulation of central beta activity is associated, among other things, with motor imagery, which may be necessary for imitation. Impaired imitation has been associated with core features of ASD. Altered modulation of oscillatory activity may be mechanistically involved in those aspects of motor network function that relate to the core symptoms of ASD. PMID:27199719

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

    Directory of Open Access Journals (Sweden)

    Joshua Benjamin Ewen

    2016-05-01

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

  1. Significance of ambulatory EEG on prognosis evaluation of patients with coma

    Directory of Open Access Journals (Sweden)

    Jun-su YANG

    2016-10-01

    Full Text Available The result of ambulatory EEG (AEEG examination on 168 comatose patients showed that AEEG grading was negatively correlated with Glasgow Coma Scale (GCS score (r = - 0.995, P = 0.005. More serious the patients' condition was and the deeper coma they were in, the lower GCS score and the higher EEG grade they would got. Among all patients, there were 84 cases with AEEG grade Ⅱ, in whom 74 cases (88.10% had favorable prognosis; 26 cases (49.06% of 53 cases with grade Ⅲ and 4 cases (12.90% of 31 cases with grade Ⅳ - Ⅴ had favorable prognosis. The differences between groups had statistical significance (χ2 = 60.565, P = 0.042. AEEG is non-invasive, repeatable and easy to operate, which is in favor of the neurological evaluation and prognosis of patients with coma. DOI: 10.3969/j.issn.1672-6731.2016.10.013

  2. The neurophysiological bases of EEG and EEG measurement: a review for the rest of us.

    Science.gov (United States)

    Jackson, Alice F; Bolger, Donald J

    2014-11-01

    A thorough understanding of the EEG signal and its measurement is necessary to produce high quality data and to draw accurate conclusions from those data. However, publications that discuss relevant topics are written for divergent audiences with specific levels of expertise: explanations are either at an abstract level that leaves readers with a fuzzy understanding of the electrophysiology involved, or are at a technical level that requires mastery of the relevant physics to understand. A clear, comprehensive review of the origin and measurement of EEG that bridges these high and low levels of explanation fills a critical gap in the literature and is necessary for promoting better research practices and peer review. The present paper addresses the neurophysiological source of EEG, propagation of the EEG signal, technical aspects of EEG measurement, and implications for interpretation of EEG data.

  3. Analyzing Electroencephalogram Signal Using EEG Lab

    Directory of Open Access Journals (Sweden)

    Mukesh BHARDWAJ

    2009-01-01

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

  4. Changes in EEG pre and post awakening.

    Science.gov (United States)

    Voss, Ursula

    2010-01-01

    This chapter is concerned with behavioral and electrophysiologic evidence of awakenings. Awakenings are understood here as a state change from sleeping to waking. We will discuss the methodological issues and the problem of properly defining an awakening. With regard to phenomena preceding an awakening, we will look at arousals and compare background to event-related activity in the electroencephalography (EEG). As arousability varies between and within species, the relevant EEG correlates of this variability are described. Concerning EEG changes following an awakening, the discussion focuses on sleep inertia effects.

  5. Challenges in pediatric video-EEG monitoring.

    Science.gov (United States)

    Sullivan, Joseph E; Corcoran-Donnelly, Maureen; Dlugos, Dennis J

    2007-06-01

    Video-EEG (VEEG) monitoring is now commonly used in children. When designing a pediatric video-EEG monitoring unit, there are many issues that need to be considered to take full advantage of this technology. Topics such as the physical layout of the VEEG unit, VEEG equipment, networking, staffing, and lines of communication regarding referrals and VEEG interpretation must be considered. Only after careful consideration of these issues, can video-EEG monitoring be successful and provide safe, state of the art clinical care in an efficient manner.

  6. Assessment of preconscious sucrose perception using EEG

    DEFF Research Database (Denmark)

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

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

  7. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI

    OpenAIRE

    Tae-Ju Lee; Seung-Min Park; Kwee-Bo Sim

    2013-01-01

    This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accura...

  8. Biogas plants in EEG. 3. new rev. and enl. ed.; Biogasanlagen im EEG

    Energy Technology Data Exchange (ETDEWEB)

    Loibl, Helmut; Maslaton, Martin; Bredow, Hartwig von; Walter, Rene (eds.)

    2013-06-01

    EEG 2012 is a complete revision for new EEG plants whereby the previous requirements of the EEG 2009 can be maintained for the existing plants. The authors of the book under consideration fully focus on the splitting into two different legal systems and the implications. It describes possibilities of solution for problems from the daily practice. The book provides a complete commentation of the biomass ordinance as well as the statements on the connection to the gas grid of biomethane plants.

  9. Short-Term EEG Spectral Pattern as a Single Event in EEG Phenomenology

    OpenAIRE

    Fingelkurts, Al. A; Fingelkurts, An. A

    2010-01-01

    Spectral decomposition, to this day, still remains the main analytical paradigm for the analysis of EEG oscillations. However, conventional spectral analysis assesses the mean characteristics of the EEG power spectra averaged out over extended periods of time and/or broad frequency bands, thus resulting in a “static” picture which cannot reflect adequately the underlying neurodynamic. A relatively new promising area in the study of EEG is based on reducing the signal to elementary short-term ...

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

    Energy Technology Data Exchange (ETDEWEB)

    Uesugi, Hideji; Onuma, Teiichi; Matsuda, Hiroshi; Ishida, Shiro [National Center Hospital for Mental, Nervous and Muscular Disorders, National Center of Neurology and Psychiatry, Kodaira, Tokyo (Japan)

    1996-02-01

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

  11. Extraversion and fronto-posterior EEG spectral power gradient: an independent component analysis.

    Science.gov (United States)

    Knyazev, Gennady G; Bocharov, Andrey V; Pylkova, Liudmila V

    2012-02-01

    Several studies show that the fronto-posterior EEG spectral power gradient is a stable individual characteristic related to personality. Whether this characteristic is specifically related to agentic extraversion and theta band of frequencies or is associated with a broader set of personality traits and frequency bands is a matter of debate, as well as the specific cortical regions contributing to this effect. To clarify these questions, we used group independent component analysis (ICA) and source localization techniques. Agentic extraversion was associated with higher theta activity in the default mode network's (DMN) posterior hub and lower theta activity in the orbitofrontal cortex (OFC). Regression analyses showed that theta activity predicted agentic extraversion better than other frequency bands and agentic extraversion predicted posterior versus frontal activity better than other personality dimensions. These results are taken to indicate higher tonic activity in OFC and lower activity in DMN in extraverts as compared to introverts.

  12. Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements

    Directory of Open Access Journals (Sweden)

    Patrick David Schelenz

    2013-11-01

    Full Text Available Combined EEG-fMRI analysis correlates time courses from single electrodes or independent EEG components with the hemodynamic response. Implementing information from only one electrode, however, may miss relevant information from complex electrophysiological networks. Component based analysis, in turn, depends on a priori knowledge of the signal topography. Complex designs such as studies on multisensory integration of emotions investigate subtle differences in distributed networks based on only a few trials per condition. Thus, they require a sensitive and comprehensive approach which does not rely on a-priori knowledge about the underlying neural processes. In this pilot study, feasibility and sensitivity of source localization-driven analysis for EEG-fMRI was tested using a multisensory integration paradigm. Dynamic audiovisual stimuli consisting of emotional talking faces and pseudowords with emotional prosody were rated in a delayed response task. The trials comprised affectively congruent and incongruent displays.In addition to event-locked EEG and fMRI analyses, induced oscillatory EEG responses at estimated cortical sources and in specific temporo-spectral windows were correlated with the corresponding BOLD responses. EEG analysis showed high data quality with less than 10% trial rejection. In an early time window, alpha oscillations were suppressed in bilateral occipital cortices and fMRI analysis confirmed high data quality with reliable activation in auditory, visual and frontal areas to the presentation of multisensory stimuli. In line with previous studies, we obtained reliable correlation patterns for event locked occipital alpha suppression and BOLD signal time course.Our results suggest a valid methodological approach to investigate complex stimuli using the present source localization driven method for EEG-fMRI. This novel procedure may help to investigate combined EEG-fMRI data from novel complex paradigms with high spatial and

  13. Multisensory integration of dynamic emotional faces and voices: method for simultaneous EEG-fMRI measurements.

    Science.gov (United States)

    Schelenz, Patrick D; Klasen, Martin; Reese, Barbara; Regenbogen, Christina; Wolf, Dhana; Kato, Yutaka; Mathiak, Klaus

    2013-01-01

    Combined EEG-fMRI analysis correlates time courses from single electrodes or independent EEG components with the hemodynamic response. Implementing information from only one electrode, however, may miss relevant information from complex electrophysiological networks. Component based analysis, in turn, depends on a priori knowledge of the signal topography. Complex designs such as studies on multisensory integration of emotions investigate subtle differences in distributed networks based on only a few trials per condition. Thus, they require a sensitive and comprehensive approach which does not rely on a-priori knowledge about the underlying neural processes. In this pilot study, feasibility and sensitivity of source localization-driven analysis for EEG-fMRI was tested using a multisensory integration paradigm. Dynamic audiovisual stimuli consisting of emotional talking faces and pseudowords with emotional prosody were rated in a delayed response task. The trials comprised affectively congruent and incongruent displays. In addition to event-locked EEG and fMRI analyses, induced oscillatory EEG responses at estimated cortical sources and in specific temporo-spectral windows were correlated with the corresponding BOLD responses. EEG analysis showed high data quality with less than 10% trial rejection. In an early time window, alpha oscillations were suppressed in bilateral occipital cortices and fMRI analysis confirmed high data quality with reliable activation in auditory, visual and frontal areas to the presentation of multisensory stimuli. In line with previous studies, we obtained reliable correlation patterns for event locked occipital alpha suppression and BOLD signal time course. Our results suggest a valid methodological approach to investigate complex stimuli using the present source localization driven method for EEG-fMRI. This novel procedure may help to investigate combined EEG-fMRI data from novel complex paradigms with high spatial and temporal

  14. 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

  15. EEG and MEG Data Analysis in SPM8

    Directory of Open Access Journals (Sweden)

    Vladimir Litvak

    2011-01-01

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

  16. 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.

  17. Detection of neonatal seizures through computerized EEG analysis.

    Science.gov (United States)

    Liu, A; Hahn, J S; Heldt, G P; Coen, R W

    1992-01-01

    Neonatal seizures are a symptom of central nervous system disturbances. Neonatal seizures may be identified by direct clinical observation by the majority of electrographic seizures are clinically silent or subtle. Electrographic seizures in the newborn consist of periodic or rhythmic discharges that are distinctively different from normal background cerebral activity. Utilizing these differences, we have developed a technique to identify electrographic seizure activity. In this study, autocorrelation analysis was used to distinguish seizures from background electrocerebral activity. Autocorrelation data were scored to quantify the periodicity using a newly developed scoring system. This method, Scored Autocorrelation Moment (SAM) analysis, successfully distinguished epochs of EEGs with seizures from those without (N = 117 epochs, 58 with seizure and 59 without). SAM analysis showed a sensitivity of 84% and a specificity of 98%. SAM analysis of EEG may provide a method for monitoring electrographic seizures in high-risk newborns.

  18. 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...

  19. Individual musical tempo preference correlates with EEG beta rhythm.

    Science.gov (United States)

    Bauer, Anna-Katharina R; Kreutz, Gunter; Herrmann, Christoph S

    2015-04-01

    Every individual has a preferred musical tempo, which peaks slightly above 120 beats per minute and is subject to interindividual variation. The preferred tempo is believed to be associated with rhythmic body movements as well as motor cortex activity. However, a long-standing question is whether preferred tempo is determined biologically. To uncover the neural correlates of preferred tempo, we first determined an individual's preferred tempo using a multistep procedure. Subsequently, we correlated the preferred tempo with a general EEG timing parameter as well as perceptual and motor EEG correlates-namely, individual alpha frequency, auditory evoked gamma band response, and motor beta activity. Results showed a significant relation between preferred tempo and the frequency of motor beta activity. These findings suggest that individual tempo preferences result from neural activity in the motor cortex, explaining the interindividual variation.

  20. EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

    Science.gov (United States)

    Falk, Tiago H.; Fraga, Francisco J.; Trambaiolli, Lucas; Anghinah, Renato

    2012-12-01

    Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

  1. NONINVASIVE DETECTION OF BRAIN ACTIVITY VARIATION UNDER DIFFERENT DEPTH OF ANESTHESIA BY EEG COMPLEXITY

    Institute of Scientific and Technical Information of China (English)

    Xu Jin; Li Wenwen; Zheng Chongxun; Jing Guixia; Liu Xueliang

    2006-01-01

    Objective To detect the change of brain activity under different depth of anesthesia (DOA)noninvasively. Methods The Lempel-Ziv complexity C(n) was used to analyze EEG and its four components (delta,theta, alpha, beta), which was recorded from SD rats under different DOA. The relationship between C(n) and DOA was studied. Results The C(n) of EEG will decrease while the depth of anesthesia increasing and vice versa. It can be used to detect the change of DOA sensitively. Compared with power spectrum, the change of C(n) is opposite to that of power spectru,. Only the C(n) of delta rhythm has obvious variations induced by the change of DOA, and the variations of delta is as similar as the EEG's. Conclusion The study shows that the desynchronized EEG is replaced by the synchronized EEG when rat goes into anesthesia state from awake, that is just the reason why complexity and power spectrum appear corresponding changes under different DOA. C(n) of delta rhythm dynamic change leads to the change of EEG, and the delta rhythm is the dominant rhythm during anesthesia for rats.

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

    Directory of Open Access Journals (Sweden)

    Ran Xiao

    2013-01-01

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

  3. EEG-assisted retrospective motion correction for fMRI: E-REMCOR

    CERN Document Server

    Zotev, Vadim; Phillips, Raquel; Bodurka, Jerzy

    2012-01-01

    We propose a method for retrospective motion correction of fMRI data in simultaneous EEG-fMRI that employs the EEG array as a sensitive motion detector. EEG motion artifacts are used to generate motion regressors describing rotational head movements with millisecond temporal resolution. These regressors are employed for slice-specific motion correction of unprocessed fMRI data. The method does not require any specialized equipment beyond the standard EEG-fMRI instrumentation. Its performance is demonstrated by correction of fMRI data from four patients with major depressive disorder, who exhibited random head movements by 1-3 mm during a resting EEG-fMRI run. The fMRI datasets, corrected using eight EEG-based motion regressors, show significant improvements in temporal SNR (tSNR) of fMRI time series, particularly in the frontal brain regions and near the surface of the brain. The tSNR improvements are as high as 50% for large brain areas in single-subject analysis and as high as 25% when the results are avera...

  4. EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

    Science.gov (United States)

    Diykh, Mohammed; Li, Yan; Wen, Peng

    2016-11-01

    The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

  5. Noise Reduction in Brainwaves by Using Both EEG Signals and Frontal Viewing Camera Images

    Directory of Open Access Journals (Sweden)

    Kang Ryoung Park

    2013-05-01

    Full Text Available Electroencephalogram (EEG-based brain-computer interfaces (BCIs have been used in various applications, including human–computer interfaces, diagnosis of brain diseases, and measurement of cognitive status. However, EEG signals can be contaminated with noise caused by user’s head movements. Therefore, we propose a new method that combines an EEG acquisition device and a frontal viewing camera to isolate and exclude the sections of EEG data containing these noises. This method is novel in the following three ways. First, we compare the accuracies of detecting head movements based on the features of EEG signals in the frequency and time domains and on the motion features of images captured by the frontal viewing camera. Second, the features of EEG signals in the frequency domain and the motion features captured by the frontal viewing camera are selected as optimal ones. The dimension reduction of the features and feature selection are performed using linear discriminant analysis. Third, the combined features are used as inputs to support vector machine (SVM, which improves the accuracy in detecting head movements. The experimental results show that the proposed method can detect head movements with an average error rate of approximately 3.22%, which is smaller than that of other methods.

  6. Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks

    Directory of Open Access Journals (Sweden)

    Charles W. Anderson

    1995-01-01

    Full Text Available EEG analysis has played a key role in the modeling of the brain's cortical dynamics, but relatively little effort has been devoted to developing EEG as a limited means of communication. If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device such as a wheelchair by composing sequences of these mental states. EEG pattern recognition is a difficult problem and hinges on the success of finding representations of the EEG signals in which the patterns can be distinguished. In this article, we report on a study comparing three EEG representations, the unprocessed signals, a reduced-dimensional representation using the Karhunen – Loève transform, and a frequency-based representation. Classification is performed with a two-layer neural network implemented on a CNAPS server (128 processor, SIMD architecture by Adaptive Solutions, Inc. Execution time comparisons show over a hundred-fold speed up over a Sun Sparc 10. The best classification accuracy on untrained samples is 73% using the frequency-based representation.

  7. Aligning strategies for using EEG as a surrogate biomarker: a review of preclinical and clinical research.

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    Leiser, Steven C; Dunlop, John; Bowlby, Mark R; Devilbiss, David M

    2011-06-15

    Electroencephalography (EEG) and related methodologies offer the promise of predicting the likelihood that novel therapies and compounds will exhibit clinical efficacy early in preclinical development. These analyses, including quantitative EEG (e.g. brain mapping) and evoked/event-related potentials (EP/ERP), can provide a physiological endpoint that may be used to facilitate drug discovery, optimize lead or candidate compound selection, as well as afford patient stratification and Go/No-Go decisions in clinical trials. Currently, the degree to which these different methodologies hold promise for translatability between preclinical models and the clinic have not been well summarized. To address this need, we review well-established and emerging EEG analytic approaches that are currently being integrated into drug discovery programs throughout preclinical development and clinical research. Furthermore, we present the use of EEG in the drug development process in the context of a number of major central nervous system disorders including Alzheimer's disease, schizophrenia, depression, attention deficit hyperactivity disorder, and pain. Lastly, we discuss the requirements necessary to consider EEG technologies as a biomarker. Many of these analyses show considerable translatability between species and are used to predict clinical efficacy from preclinical data. Nonetheless, the next challenge faced is the selection and validation of EEG endpoints that provide a set of robust and translatable biomarkers bridging preclinical and clinical programs.

  8. IPS Interest in the EEG of Patients after a Single Epileptic Seizure

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    Mounach, Jamal; Satte, Amal; Ouhabi, Hamid; El Hessni, Aboubaker

    2016-01-01

    Objective. This study aims to evaluate the incidence of pathological cerebral activity responses to intermittent rhythmic photic stimulation (IPS) after a single epileptic seizure. Patients and Methods. One hundred and thirty-seven EEGs were performed at the Neurophysiology Department of Mohamed V Teaching Military Hospital in Rabat. Clinical and EEG data was collected. Results. 9.5% of our patients had photoparoxysmal discharges (PPD). Incidence was higher in males than in females, but p value was not significant (p = 0.34), and it was higher in children compared to adults with significant p value (p = 0.08). The most epileptogenic frequencies were within the range 15–20 Hz. 63 patients had an EEG after 72 hours; among them 11 were photosensitive (p = 0.001). The frequency of the PPR was significantly higher in patients with generalized abnormalities than in focal abnormalities (p = 0.001). EEG confirmed a genetic generalized epilepsy in 8 cases among 13 photosensitive patients. Conclusion. PPR is age related. The frequencies within the range 15–20 Hz should inevitably be included in EEG protocols. The presence of PPR after a first seizure is probably more in favor of generalized seizure rather than the other type of seizure. PPR seems independent from the delay Seizure-EEG. Our study did not show an association between sex and photosensitivity. PMID:27635393

  9. IPS Interest in the EEG of Patients after a Single Epileptic Seizure

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    Fatima Zahra Taoufiqi

    2016-01-01

    Full Text Available Objective. This study aims to evaluate the incidence of pathological cerebral activity responses to intermittent rhythmic photic stimulation (IPS after a single epileptic seizure. Patients and Methods. One hundred and thirty-seven EEGs were performed at the Neurophysiology Department of Mohamed V Teaching Military Hospital in Rabat. Clinical and EEG data was collected. Results. 9.5% of our patients had photoparoxysmal discharges (PPD. Incidence was higher in males than in females, but p value was not significant (p=0.34, and it was higher in children compared to adults with significant p value (p=0.08. The most epileptogenic frequencies were within the range 15–20 Hz. 63 patients had an EEG after 72 hours; among them 11 were photosensitive (p=0.001. The frequency of the PPR was significantly higher in patients with generalized abnormalities than in focal abnormalities (p=0.001. EEG confirmed a genetic generalized epilepsy in 8 cases among 13 photosensitive patients. Conclusion. PPR is age related. The frequencies within the range 15–20 Hz should inevitably be included in EEG protocols. The presence of PPR after a first seizure is probably more in favor of generalized seizure rather than the other type of seizure. PPR seems independent from the delay Seizure-EEG. Our study did not show an association between sex and photosensitivity.

  10. Tracking non-stationary EEG sources using adaptive online recursive independent component analysis.

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    Hsu, Sheng-Hsiou; Pion-Tonachini, Luca; Jung, Tzyy-Ping; Cauwenberghs, Gert

    2015-01-01

    Electroencephalographic (EEG) source-level analyses such as independent component analysis (ICA) have uncovered features related to human cognitive functions or artifactual activities. Among these methods, Online Recursive ICA (ORICA) has been shown to achieve fast convergence in decomposing high-density EEG data for real-time applications. However, its adaptation performance has not been fully explored due to the difficulty in choosing an appropriate forgetting factor: the weight applied to new data in a recursive update which determines the trade-off between the adaptation capability and convergence quality. This study proposes an adaptive forgetting factor for ORICA (adaptive ORICA) to learn and adapt to non-stationarity in the EEG data. Using a realistically simulated non-stationary EEG dataset, we empirically show adaptive forgetting factors outperform other commonly-used non-adaptive rules when underlying source dynamics are changing. Standard offline ICA can only extract a subset of the changing sources while adaptive ORICA can recover all. Applied to actual EEG data recorded from a task-switching experiments, adaptive ORICA can learn and re-learn the task-related components as they change. With an adaptive forgetting factor, adaptive ORICA can track non-stationary EEG sources, opening many new online applications in brain-computer interfaces and in monitoring of brain dynamics.

  11. EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

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    Boubchir, Larbi; Touati, Youcef; Daachi, Boubaker; Chérif, Arab Ali

    2015-08-01

    In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials (SSVEP) techniques, ErrP, which appear when a classification error occurs, are not easily recognizable by only examining the temporal or frequency characteristics of EEG signals. A supplementary classification process is therefore needed to identify them in order to stop the course of the action and back up to a recovery state. This paper presents a set of time-frequency (t-f) features for the detection and classification of EEG ErrP in extra-brain activities due to misclassification observed by a user exploiting non-invasive BMI and robot control in the task space. The proposed features are able to characterize and detect ErrP activities in the t-f domain. These features are derived from the information embedded in the t-f representation of EEG signals, and include the Instantaneous Frequency (IF), t-f information complexity, SVD information, energy concentration and sub-bands' energies. The experiment results on real EEG data show that the use of the proposed t-f features for detecting and classifying EEG ErrP achieved an overall classification accuracy up to 97% for 50 EEG segments using 2-class SVM classifier.

  12. A COMPARISION BETWEEN WALSHHADAMARD AND FOURIER ANALYSIS OF THE EEG SIGNALS

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    AZADEH BASTANI

    2011-07-01

    Full Text Available Electroencephalography (EEG is one of the most important diagnostic tools in neurology and getting information about the brain activity. One of this is real-time and quantified study of brain activities to measure the stage of unconsciousness due to injection drug in operation room. EEG signal is a stochastic non-stationary process. Regarding the complexity of brain activities on EEG process, studies are based on time-frequency features analysis of EEG signals. Most of these analyses are based on Fourier Transform and the most significant are classic and parametric estimation of power spectral density analysis. Considering the origins of EEG in the brain, it seems that Walsh-Hadamard transform is more effective than Fourier transform in feature extracting of these signals. In this paper the efficiency of Walsh-Hadamard transform features were comparedwith extracted features from Fourier transform. To evaluate these features, three different classifying algorithms are used. The results showed that Walsh-Hadamard extracted features are suitable tools for recognition of difference between different stages of EEG signals. Simplicity and speed of Walsh-Hadamard transform calculation made it preferable then Fourier spectral features. The fast Walsh-Hadamard transform is an attractive alternative to the fast fourier transform because it is computationally more efficient, and thus faster to perform on a digital computer.

  13. Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis.

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    Timothy, Leena T; Krishna, Bindu M; Nair, Usha

    2017-10-01

    The present study is aimed at the classification of mild cognitive impairment (MCI) EEG by combining complexity and synchronization features based on quantifiers from the common platform of recurrence based analysis. Recurrence rate (RR) of recurrence quantification analysis (RQA) is used for complexity analysis and RR of cross recurrence quantification analysis (CRQA) is used for synchronization analysis. The investigations are carried out on EEG from two states (i) resting eyes closed (EC) and (ii) short term memory task (STM).The results of our analysis show lower levels of complexity and higher levels of inter and intra hemispheric synchronisation in the MCI EEG compared to that of normal controls (NC) as indicated by the statistically significant higher value of RQA RR and CRQA RR. The results also evidence the effectiveness of memory activation task by bringing out the characteristic features of MCI EEG in task specific regions of temporal, parietal and frontal lobes under the STM condition.A new approach of combining complexity and synchronization features for EEG classification of MCI subjects is proposed, based on the geometrical signal separation in a feature space formed by RQA and CRQA RR values. The results of linear classification analysis of MCI and NC EEG also reveals the effectiveness of task state analysis by the enhanced classification efficiency under the cognitive load of STM condition compared to that of EC condition. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Stochastic relevance analysis of epileptic EEG signals for channel selection and classification.

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    Duque-Muñoz, L; Guerrero-Mosquera, C; Castellanos-Dominguez, G

    2013-01-01

    Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.

  15. Effect of chronic electrical stimulation of the centromedian thalamic nuclei on various intractable seizure patterns: I. Clinical seizures and paroxysmal EEG activity.

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    Velasco, F; Velasco, M; Velasco, A L; Jiménez, F

    1993-01-01

    Twenty-three patients with various intractable seizure patterns were divided into four groups based on their most frequent seizure type and their clinical and EEG response to chronic electrical stimulation of the centromedian thalamic nuclei (ESCM): group A, generalized tonic-clonic (GTC, n = 9); group B, partial motor (Rasmussen type) (n = 3); group C, complex partial seizures (CPS, n = 5); and group D, generalized tonic seizures (Lennox-Gastaut type) (n = 6). CM were radiologically and electrophysiologically localized by means of stereotaxic landmarks and by thalamically induced scalp recruiting-like responses and desynchronization. ESCM consisted of daily 2-h stimulation sessions for 3 months. Each stimulus consisted of a 1-min train of square pulses with a 4-min interstimulus interval, alternating right and left CM. Each pulse was 1.0 ms in duration at 60/s frequency and 8-15 V (400-1,250 microA) amplitude. Voltage (V), current flow (microA) and impedance (k omega) at the electrode tips were kept constant. A significant decrease in the number of seizures per month and paroxysmal EEG waves per 10-s spochs occurred in group A patients between the baseline period (BL) and the ESCM period. These changes persisted for > 3 months after discontinuation of ESCM (poststimulation period, Post). Post was accompanied by a significant decrease in the number of paroxysmal EEG discharges. A substantial decrease in seizures and paroxysmal discharges was also observed in patients of group B. In contrast, patients of groups C and D showed no significant changes from BL to ESCM and Post periods, except for a significant decrease in the number of seizures in group D patients from BL to Post periods.

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

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    Adrián Poblano

    2006-12-01

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

  17. Ictal kissing with subdural EEG recording.

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    Alsemari, Abdulaziz; Alotaibi, Faisal; Baz, Salah

    2013-01-01

    Ictal kissing has been described in the literature. Five cases were reported and associated with temporal lobe epilepsy lateralizing to the nondominant hemisphere. A case of ictal kissing was identified. The aim was to demonstrate the clinical, clinical and electrophysiological features (as recorded by subdural electrodes). The surgical procedure, histopathology, and imaging data were reviewed and correlated with the literature. A 29-year-old right-handed female, who presented with ictal right hand left arm dystonic posturing, and lip smacking, was studied. The automatism was usually followed by prolonged emotional gestures and by hugging and kissing her relative and/or attendant nurse. Magnetic resonance imaging of the brain showed right small cortical and subcortical lesions of the right inferior frontal lobe with gliosis but without mass effect and normal-sized hippocampi. The PET scan showed hypometabolism of the right temporal lobe. Neuropsychological evaluation showed deficit in her nonverbal memory. The subdural electrodes showed high amplitude spikes over right mesial temporal lobe strips. The offsite of the ictal discharges was usually at the right frontal strips. Right standard temporal lobectomy with amygdalohippocampectomy and right inferior frontal lesionectomy were performed. The patient continued to be seizure-free for one year postoperatively. Our case report supports with subdural EEG recording the findings of the few reported cases of ictal kissing behavior lateralized to the nondominant hemisphere. However, the affectionate kissing behavior was associated with spread of the epileptic discharges to the right frontal lobe.

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

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

    2011-06-01

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

  19. Epileptiform abnormalities and quantitative EEG in children with attention-deficit / hyperactivity disorder Atividade epileptiforme e eletrencefalograma quantitativo em crianças com transtorno de déficit de atenção/hiperatividade

    Directory of Open Access Journals (Sweden)

    Lineu Corrêa Fonseca

    2008-09-01

    <