Review on solving the forward problem in EEG source analysis

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Vergult Anneleen

2007-11-01

Full Text Available Abstract Background The aim of electroencephalogram (EEG source localization is to find the brain areas responsible for EEG waves of interest. It consists of solving forward and inverse problems. The forward problem is solved by starting from a given electrical source and calculating the potentials at the electrodes. These evaluations are necessary to solve the inverse problem which is defined as finding brain sources which are responsible for the measured potentials at the EEG electrodes. Methods While other reviews give an extensive summary of the both forward and inverse problem, this review article focuses on different aspects of solving the forward problem and it is intended for newcomers in this research field. Results It starts with focusing on the generators of the EEG: the post-synaptic potentials in the apical dendrites of pyramidal neurons. These cells generate an extracellular current which can be modeled by Poisson's differential equation, and Neumann and Dirichlet boundary conditions. The compartments in which these currents flow can be anisotropic (e.g. skull and white matter. In a three-shell spherical head model an analytical expression exists to solve the forward problem. During the last two decades researchers have tried to solve Poisson's equation in a realistically shaped head model obtained from 3D medical images, which requires numerical methods. The following methods are compared with each other: the boundary element method (BEM, the finite element method (FEM and the finite difference method (FDM. In the last two methods anisotropic conducting compartments can conveniently be introduced. Then the focus will be set on the use of reciprocity in EEG source localization. It is introduced to speed up the forward calculations which are here performed for each electrode position rather than for each dipole position. Solving Poisson's equation utilizing FEM and FDM corresponds to solving a large sparse linear system. Iterative

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.

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Delorme, Arnaud; Makeig, Scott

2004-03-15

We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.

FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data.

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Oostenveld, Robert; Fries, Pascal; Maris, Eric; Schoffelen, Jan-Mathijs

2011-01-01

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

Model-Based Analysis and Optimization of the Mapping of Cortical Sources in the Spontaneous Scalp EEG

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Andrei V. Sazonov

2007-01-01

Full Text Available The mapping of brain sources into the scalp electroencephalogram (EEG depends on volume conduction properties of the head and on an electrode montage involving a reference. Mathematically, this source mapping (SM is fully determined by an observation function (OF matrix. This paper analyses the OF-matrix for a generation model for the desynchronized spontaneous EEG. The model involves a four-shell spherical volume conductor containing dipolar sources that are mutually uncorrelated so as to reflect the desynchronized EEG. The reference is optimized in order to minimize the impact in the SM of the sources located distant from the electrodes. The resulting reference is called the localized reference (LR. The OF-matrix is analyzed in terms of the relative power contribution of the sources and the cross-channel correlation coefficient for five existing references as well as for the LR. It is found that the Hjorth Laplacian reference is a fair approximation of the LR, and thus is close to optimum for practical intents and purposes. The other references have a significantly poorer performance. Furthermore, the OF-matrix is analyzed for limits to the spatial resolution for the EEG. These are estimated to be around 2 cm.

Source localization of rhythmic ictal EEG activity: a study of diagnostic accuracy following STARD criteria.

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Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana; Åkeson, Per; Pedersen, Birthe; Pinborg, Lars H; Ziebell, Morten; Jespersen, Bo; Fuglsang-Frederiksen, Anders

2013-10-01

Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model. Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy (STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average (LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision. Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval (CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones. Source localization of rhythmic ictal activity using a distributed source model (LAURA) for the ictal EEG signals selected with a standardized method

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

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Liu, Quanying; Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2018-01-01

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis. PMID:29551969

Effect of Brain-to-Skull Conductivity Ratio on EEG Source Localization Accuracy

OpenAIRE

Gang Wang; Doutian Ren

2013-01-01

The goal of this study was to investigate the influence of the brain-to-skull conductivity ratio (BSCR) on EEG source localization accuracy. In this study, we evaluated four BSCRs: 15, 20, 25, and 80, which were mainly discussed according to the literature. The scalp EEG signals were generated by BSCR-related forward computation for each cortical dipole source. Then, for each scalp EEG measurement, the source reconstruction was performed to identify the estimated dipole sources by the actual ...

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

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

2014-01-01

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

EEG-Informed fMRI: A Review of Data Analysis Methods

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Abreu, Rodolfo; Leal, Alberto; Figueiredo, Patrícia

2018-01-01

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest. PMID:29467634

EEG-Informed fMRI: A Review of Data Analysis Methods

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Rodolfo Abreu

2018-02-01

Full Text Available The simultaneous acquisition of electroencephalography (EEG with functional magnetic resonance imaging (fMRI is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

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

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

2016-12-01

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

Blind Source Separation of Event-Related EEG/MEG.

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

2017-09-01

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

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...

Use Case Analysis: The Ambulatory EEG in Navy Medicine for Traumatic Brain Injuries

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2016-12-01

science of binaural beats . Retrieved from http://binauralbrains.com/the-science-of- binaural - beats / Biosignal. (2016). MicroEEG. Retrieved from http...Cap. Source: Binaural Brains (n.d.). ....................................4  Figure 3.  EEG Machine. Source: Refine Medical Technology (n.d...EEG. Figures 2, 3, and 4 display images of a standard EEG cap, EEG machine, and an EEG recording. Figure 2. Standard EEG Cap. Source: Binaural Brains

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

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

2007-01-01

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

Source-based neurofeedback methods using EEG recordings: training altered brain activity in a functional brain source derived from blind source separation

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White, David J.; Congedo, Marco; Ciorciari, Joseph

2014-01-01

A developing literature explores the use of neurofeedback in the treatment of a range of clinical conditions, particularly ADHD and epilepsy, whilst neurofeedback also provides an experimental tool for studying the functional significance of endogenous brain activity. A critical component of any neurofeedback method is the underlying physiological signal which forms the basis for the feedback. While the past decade has seen the emergence of fMRI-based protocols training spatially confined BOLD activity, traditional neurofeedback has utilized a small number of electrode sites on the scalp. As scalp EEG at a given electrode site reflects a linear mixture of activity from multiple brain sources and artifacts, efforts to successfully acquire some level of control over the signal may be confounded by these extraneous sources. Further, in the event of successful training, these traditional neurofeedback methods are likely influencing multiple brain regions and processes. The present work describes the use of source-based signal processing methods in EEG neurofeedback. The feasibility and potential utility of such methods were explored in an experiment training increased theta oscillatory activity in a source derived from Blind Source Separation (BSS) of EEG data obtained during completion of a complex cognitive task (spatial navigation). Learned increases in theta activity were observed in two of the four participants to complete 20 sessions of neurofeedback targeting this individually defined functional brain source. Source-based EEG neurofeedback methods using BSS may offer important advantages over traditional neurofeedback, by targeting the desired physiological signal in a more functionally and spatially specific manner. Having provided preliminary evidence of the feasibility of these methods, future work may study a range of clinically and experimentally relevant brain processes where individual brain sources may be targeted by source-based EEG neurofeedback. PMID

EEG source imaging during two Qigong meditations.

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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.

Combined MEG-EEG source localisation in patients with sub-acute sclerosing pan-encephalitis.

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Velmurugan, J; Sinha, Sanjib; Nagappa, Madhu; Mariyappa, N; Bindu, P S; Ravi, G S; Hazra, Nandita; Thennarasu, K; Ravi, V; Taly, A B; Satishchandra, P

2016-08-01

To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag(®) TRIUX™ system. Qualitative analysis of 80-160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the 'onset' and at 'earliest significant peak' of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4-16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG-EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.

Information-Theoretical Analysis of EEG Microstate Sequences in Python

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Frederic von Wegner

2018-06-01

Full Text Available We present an open-source Python package to compute information-theoretical quantities for electroencephalographic data. Electroencephalography (EEG measures the electrical potential generated by the cerebral cortex and the set of spatial patterns projected by the brain's electrical potential on the scalp surface can be clustered into a set of representative maps called EEG microstates. Microstate time series are obtained by competitively fitting the microstate maps back into the EEG data set, i.e., by substituting the EEG data at a given time with the label of the microstate that has the highest similarity with the actual EEG topography. As microstate sequences consist of non-metric random variables, e.g., the letters A–D, we recently introduced information-theoretical measures to quantify these time series. In wakeful resting state EEG recordings, we found new characteristics of microstate sequences such as periodicities related to EEG frequency bands. The algorithms used are here provided as an open-source package and their use is explained in a tutorial style. The package is self-contained and the programming style is procedural, focusing on code intelligibility and easy portability. Using a sample EEG file, we demonstrate how to perform EEG microstate segmentation using the modified K-means approach, and how to compute and visualize the recently introduced information-theoretical tests and quantities. The time-lagged mutual information function is derived as a discrete symbolic alternative to the autocorrelation function for metric time series and confidence intervals are computed from Markov chain surrogate data. The software package provides an open-source extension to the existing implementations of the microstate transform and is specifically designed to analyze resting state EEG recordings.

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.

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Puce, Aina; Hämäläinen, Matti S

2017-05-31

Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.

A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies

Directory of Open Access Journals (Sweden)

Aina Puce

2017-05-01

Full Text Available Electroencephalography (EEG and magnetoencephalography (MEG are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.

Combined process automation for large-scale EEG analysis.

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Sfondouris, John L; Quebedeaux, Tabitha M; Holdgraf, Chris; Musto, Alberto E

2012-01-01

Epileptogenesis is a dynamic process producing increased seizure susceptibility. Electroencephalography (EEG) data provides information critical in understanding the evolution of epileptiform changes throughout epileptic foci. We designed an algorithm to facilitate efficient large-scale EEG analysis via linked automation of multiple data processing steps. Using EEG recordings obtained from electrical stimulation studies, the following steps of EEG analysis were automated: (1) alignment and isolation of pre- and post-stimulation intervals, (2) generation of user-defined band frequency waveforms, (3) spike-sorting, (4) quantification of spike and burst data and (5) power spectral density analysis. This algorithm allows for quicker, more efficient EEG analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sparse EEG/MEG source estimation via a group lasso.

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Michael Lim

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

Spatiotemporal analysis of single-trial EEG of emotional pictures based on independent component analysis and source location

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Liu, Jiangang; Tian, Jie

2007-03-01

The present study combined the Independent Component Analysis (ICA) and low-resolution brain electromagnetic tomography (LORETA) algorithms to identify the spatial distribution and time course of single-trial EEG record differences between neural responses to emotional stimuli vs. the neutral. Single-trial multichannel (129-sensor) EEG records were collected from 21 healthy, right-handed subjects viewing the emotion emotional (pleasant/unpleasant) and neutral pictures selected from International Affective Picture System (IAPS). For each subject, the single-trial EEG records of each emotional pictures were concatenated with the neutral, and a three-step analysis was applied to each of them in the same way. First, the ICA was performed to decompose each concatenated single-trial EEG records into temporally independent and spatially fixed components, namely independent components (ICs). The IC associated with artifacts were isolated. Second, the clustering analysis classified, across subjects, the temporally and spatially similar ICs into the same clusters, in which nonparametric permutation test for Global Field Power (GFP) of IC projection scalp maps identified significantly different temporal segments of each emotional condition vs. neutral. Third, the brain regions accounted for those significant segments were localized spatially with LORETA analysis. In each cluster, a voxel-by-voxel randomization test identified significantly different brain regions between each emotional condition vs. the neutral. Compared to the neutral, both emotional pictures elicited activation in the visual, temporal, ventromedial and dorsomedial prefrontal cortex and anterior cingulated gyrus. In addition, the pleasant pictures activated the left middle prefrontal cortex and the posterior precuneus, while the unpleasant pictures activated the right orbitofrontal cortex, posterior cingulated gyrus and somatosensory region. Our results were well consistent with other functional imaging

Affective attitudes to face images associated with intracerebral EEG source location before face viewing.

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Pizzagalli, D; Koenig, T; Regard, M; Lehmann, D

1999-01-01

We investigated whether different, personality-related affective attitudes are associated with different brain electric field (EEG) sources before any emotional challenge (stimulus exposure). A 27-channel EEG was recorded in 15 subjects during eyes-closed resting. After recording, subjects rated 32 images of human faces for affective appeal. The subjects in the first (i.e., most negative) and fourth (i.e., most positive) quartile of general affective attitude were further analyzed. The EEG data (mean=25+/-4. 8 s/subject) were subjected to frequency-domain model dipole source analysis (FFT-Dipole-Approximation), resulting in 3-dimensional intracerebral source locations and strengths for the delta-theta, alpha, and beta EEG frequency band, and for the full range (1.5-30 Hz) band. Subjects with negative attitude (compared to those with positive attitude) showed the following source locations: more inferior for all frequency bands, more anterior for the delta-theta band, more posterior and more right for the alpha, beta and 1.5-30 Hz bands. One year later, the subjects were asked to rate the face images again. The rating scores for the same face images were highly correlated for all subjects, and original and retest affective mean attitude was highly correlated across subjects. The present results show that subjects with different affective attitudes to face images had different active, cerebral, neural populations in a task-free condition prior to viewing the images. We conclude that the brain functional state which implements affective attitude towards face images as a personality feature exists without elicitors, as a continuously present, dynamic feature of brain functioning. Copyright 1999 Elsevier Science B.V.

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

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

2017-09-01

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

An alternative subspace approach to EEG dipole source localization

Science.gov (United States)

Xu, Xiao-Liang; Xu, Bobby; He, Bin

2004-01-01

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist.

An alternative subspace approach to EEG dipole source localization

International Nuclear Information System (INIS)

Xu Xiaoliang; Xu, Bobby; He Bin

2004-01-01

In the present study, we investigate a new approach to electroencephalography (EEG) three-dimensional (3D) dipole source localization by using a non-recursive subspace algorithm called FINES. In estimating source dipole locations, the present approach employs projections onto a subspace spanned by a small set of particular vectors (FINES vector set) in the estimated noise-only subspace instead of the entire estimated noise-only subspace in the case of classic MUSIC. The subspace spanned by this vector set is, in the sense of principal angle, closest to the subspace spanned by the array manifold associated with a particular brain region. By incorporating knowledge of the array manifold in identifying FINES vector sets in the estimated noise-only subspace for different brain regions, the present approach is able to estimate sources with enhanced accuracy and spatial resolution, thus enhancing the capability of resolving closely spaced sources and reducing estimation errors. The present computer simulations show, in EEG 3D dipole source localization, that compared to classic MUSIC, FINES has (1) better resolvability of two closely spaced dipolar sources and (2) better estimation accuracy of source locations. In comparison with RAP-MUSIC, FINES' performance is also better for the cases studied when the noise level is high and/or correlations among dipole sources exist

Analysis of EEG Related Saccadic Eye Movement

Science.gov (United States)

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

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

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

Science.gov (United States)

Dalal, Sarang S; Zumer, Johanna M; Guggisberg, Adrian G; Trumpis, Michael; Wong, Daniel D E; Sekihara, Kensuke; Nagarajan, Srikantan S

2011-01-01

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.

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.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆

Science.gov (United States)

López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874

Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging

Science.gov (United States)

Nguyen, Thinh; Potter, Thomas; Grossman, Robert; Zhang, Yingchun

2018-06-01

Objective. Neuroimaging has been employed as a promising approach to advance our understanding of brain networks in both basic and clinical neuroscience. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) represent two neuroimaging modalities with complementary features; EEG has high temporal resolution and low spatial resolution while fMRI has high spatial resolution and low temporal resolution. Multimodal EEG inverse methods have attempted to capitalize on these properties but have been subjected to localization error. The dynamic brain transition network (DBTN) approach, a spatiotemporal fMRI constrained EEG source imaging method, has recently been developed to address these issues by solving the EEG inverse problem in a Bayesian framework, utilizing fMRI priors in a spatial and temporal variant manner. This paper presents a computer simulation study to provide a detailed characterization of the spatial and temporal accuracy of the DBTN method. Approach. Synthetic EEG data were generated in a series of computer simulations, designed to represent realistic and complex brain activity at superficial and deep sources with highly dynamical activity time-courses. The source reconstruction performance of the DBTN method was tested against the fMRI-constrained minimum norm estimates algorithm (fMRIMNE). The performances of the two inverse methods were evaluated both in terms of spatial and temporal accuracy. Main results. In comparison with the commonly used fMRIMNE method, results showed that the DBTN method produces results with increased spatial and temporal accuracy. The DBTN method also demonstrated the capability to reduce crosstalk in the reconstructed cortical time-course(s) induced by neighboring regions, mitigate depth bias and improve overall localization accuracy. Significance. The improved spatiotemporal accuracy of the reconstruction allows for an improved characterization of complex neural activity. This improvement can be

Hierarchical Bayesian Model for Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE)

DEFF Research Database (Denmark)

Stahlhut, Carsten; Mørup, Morten; Winther, Ole

2009-01-01

In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface, and ele......In this paper we propose an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model is motivated by the many uncertain contributions that form the forward propagation model including the tissue conductivity distribution, the cortical surface...

fMRI activation patterns in an analytic reasoning task: consistency with EEG source localization

Science.gov (United States)

Li, Bian; Vasanta, Kalyana C.; O'Boyle, Michael; Baker, Mary C.; Nutter, Brian; Mitra, Sunanda

2010-03-01

Functional magnetic resonance imaging (fMRI) is used to model brain activation patterns associated with various perceptual and cognitive processes as reflected by the hemodynamic (BOLD) response. While many sensory and motor tasks are associated with relatively simple activation patterns in localized regions, higher-order cognitive tasks may produce activity in many different brain areas involving complex neural circuitry. We applied a recently proposed probabilistic independent component analysis technique (PICA) to determine the true dimensionality of the fMRI data and used EEG localization to identify the common activated patterns (mapped as Brodmann areas) associated with a complex cognitive task like analytic reasoning. Our preliminary study suggests that a hybrid GLM/PICA analysis may reveal additional regions of activation (beyond simple GLM) that are consistent with electroencephalography (EEG) source localization patterns.

EEG and MEG source localization using recursively applied (RAP) MUSIC

Energy Technology Data Exchange (ETDEWEB)

Mosher, J.C. [Los Alamos National Lab., NM (United States); Leahy, R.M. [University of Southern California, Los Angeles, CA (United States). Signal and Image Processing Inst.

1996-12-31

The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. Here we describe a novel extension of this approach which we refer to as RAP (Recursively APplied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections, which uses the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of `diverse polarization,` are easily extracted using the associated principal vectors.

Equivalent charge source model based iterative maximum neighbor weight for sparse EEG source localization.

Science.gov (United States)

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

2008-12-01

How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

Anatomically constrained dipole adjustment (ANACONDA) for accurate MEG/EEG focal source localizations

Science.gov (United States)

Im, Chang-Hwan; Jung, Hyun-Kyo; Fujimaki, Norio

2005-10-01

This paper proposes an alternative approach to enhance localization accuracy of MEG and EEG focal sources. The proposed approach assumes anatomically constrained spatio-temporal dipoles, initial positions of which are estimated from local peak positions of distributed sources obtained from a pre-execution of distributed source reconstruction. The positions of the dipoles are then adjusted on the cortical surface using a novel updating scheme named cortical surface scanning. The proposed approach has many advantages over the conventional ones: (1) as the cortical surface scanning algorithm uses spatio-temporal dipoles, it is robust with respect to noise; (2) it requires no a priori information on the numbers and initial locations of the activations; (3) as the locations of dipoles are restricted only on a tessellated cortical surface, it is physiologically more plausible than the conventional ECD model. To verify the proposed approach, it was applied to several realistic MEG/EEG simulations and practical experiments. From the several case studies, it is concluded that the anatomically constrained dipole adjustment (ANACONDA) approach will be a very promising technique to enhance accuracy of focal source localization which is essential in many clinical and neurological applications of MEG and EEG.

Anatomically constrained dipole adjustment (ANACONDA) for accurate MEG/EEG focal source localizations

International Nuclear Information System (INIS)

Im, Chang-Hwan; Jung, Hyun-Kyo; Fujimaki, Norio

2005-01-01

This paper proposes an alternative approach to enhance localization accuracy of MEG and EEG focal sources. The proposed approach assumes anatomically constrained spatio-temporal dipoles, initial positions of which are estimated from local peak positions of distributed sources obtained from a pre-execution of distributed source reconstruction. The positions of the dipoles are then adjusted on the cortical surface using a novel updating scheme named cortical surface scanning. The proposed approach has many advantages over the conventional ones: (1) as the cortical surface scanning algorithm uses spatio-temporal dipoles, it is robust with respect to noise; (2) it requires no a priori information on the numbers and initial locations of the activations; (3) as the locations of dipoles are restricted only on a tessellated cortical surface, it is physiologically more plausible than the conventional ECD model. To verify the proposed approach, it was applied to several realistic MEG/EEG simulations and practical experiments. From the several case studies, it is concluded that the anatomically constrained dipole adjustment (ANACONDA) approach will be a very promising technique to enhance accuracy of focal source localization which is essential in many clinical and neurological applications of MEG and EEG

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

Science.gov (United States)

López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

Independent component analysis separates spikes of different origin in the EEG.

Science.gov (United States)

Urrestarazu, Elena; Iriarte, Jorge; Artieda, Julio; Alegre, Manuel; Valencia, Miguel; Viteri, César

2006-02-01

Independent component analysis (ICA) is a novel system that finds independent sources in recorded signals. Its usefulness in separating epileptiform activity of different origin has not been determined. The goal of this study was to demonstrate that ICA is useful for separating different spikes using samples of EEG of patients with focal epilepsy. Digital EEG samples from four patients with focal epilepsy were included. The patients had temporal (n = 2), centrotemporal (n = 1) or frontal spikes (n = 1). Twenty-six samples with two (or more) spikes from two different patients were created. The selection of the two spikes for each mixed EEG was performed randomly, trying to have all the different combinations and rejecting the mixture of two spikes from the same patient. Two different examiners studied the EEGs using ICA with JADE paradigm in Matlab platform, trying to separate and to identify the spikes. They agreed in the correct separation of the spikes in 24 of the 26 samples, classifying the spikes as frontal, temporal or centrotemporal, left or right sided. The demonstration of the possibility of detecting different artificially mixed spikes confirms that ICA may be useful in separating spikes or other elements in real EEGs.

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

Science.gov (United States)

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

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

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG

Science.gov (United States)

Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals. PMID:27340397

PWC-ICA: A Method for Stationary Ordered Blind Source Separation with Application to EEG.

Science.gov (United States)

Ball, Kenneth; Bigdely-Shamlo, Nima; Mullen, Tim; Robbins, Kay

2016-01-01

Independent component analysis (ICA) is a class of algorithms widely applied to separate sources in EEG data. Most ICA approaches use optimization criteria derived from temporal statistical independence and are invariant with respect to the actual ordering of individual observations. We propose a method of mapping real signals into a complex vector space that takes into account the temporal order of signals and enforces certain mixing stationarity constraints. The resulting procedure, which we call Pairwise Complex Independent Component Analysis (PWC-ICA), performs the ICA in a complex setting and then reinterprets the results in the original observation space. We examine the performance of our candidate approach relative to several existing ICA algorithms for the blind source separation (BSS) problem on both real and simulated EEG data. On simulated data, PWC-ICA is often capable of achieving a better solution to the BSS problem than AMICA, Extended Infomax, or FastICA. On real data, the dipole interpretations of the BSS solutions discovered by PWC-ICA are physically plausible, are competitive with existing ICA approaches, and may represent sources undiscovered by other ICA methods. In conjunction with this paper, the authors have released a MATLAB toolbox that performs PWC-ICA on real, vector-valued signals.

SCoT: a Python toolbox for EEG source connectivity.

Science.gov (United States)

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

2014-01-01

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

Matching pursuit and source deflation for sparse EEG/MEG dipole moment estimation.

Science.gov (United States)

Wu, Shun Chi; Swindlehurst, A Lee

2013-08-01

In this paper, we propose novel matching pursuit (MP)-based algorithms for EEG/MEG dipole source localization and parameter estimation for multiple measurement vectors with constant sparsity. The algorithms combine the ideas of MP for sparse signal recovery and source deflation, as employed in estimation via alternating projections. The source-deflated matching pursuit (SDMP) approach mitigates the problem of residual interference inherent in sequential MP-based methods or recursively applied (RAP)-MUSIC. Furthermore, unlike prior methods based on alternating projection, SDMP allows one to efficiently estimate the dipole orientation in addition to its location. Simulations show that the proposed algorithms outperform existing techniques under various conditions, including those with highly correlated sources. Results using real EEG data from auditory experiments are also presented to illustrate the performance of these algorithms.

Independent component analysis of gait-related movement artifact recorded using EEG electrodes during treadmill walking.

Directory of Open Access Journals (Sweden)

Kristine Lynne Snyder

2015-12-01

Full Text Available There has been a recent surge in the use of electroencephalography (EEG as a tool for mobile brain imaging due to its portability and fine time resolution. When EEG is combined with independent component analysis (ICA and source localization techniques, it can model electrocortical activity as arising from temporally independent signals located in spatially distinct cortical areas. However, for mobile tasks, it is not clear how movement artifacts influence ICA and source localization. We devised a novel method to collect pure movement artifact data (devoid of any electrophysiological signals with a 256-channel EEG system. We first blocked true electrocortical activity using a silicone swim cap. Over the silicone layer, we placed a simulated scalp with electrical properties similar to real human scalp. We collected EEG movement artifact signals from ten healthy, young subjects wearing this setup as they walked on a treadmill at speeds from 0.4-1.6 m/s. We performed ICA and dipole fitting on the EEG movement artifact data to quantify how accurately these methods would identify the artifact signals as non-neural. ICA and dipole fitting accurately localized 99% of the independent components in non-neural locations or lacked dipolar characteristics. The remaining 1% of sources had locations within the brain volume and low residual variances, but had topographical maps, power spectra, time courses, and event related spectral perturbations typical of non-neural sources. Caution should be exercised when interpreting ICA for data that includes semi-periodic artifacts including artifact arising from human walking. Alternative methods are needed for the identification and separation of movement artifact in mobile EEG signals, especially methods that can be performed in real time. Separating true brain signals from motion artifact could clear the way for EEG brain computer interfaces for assistance during mobile activities, such as walking.

Dipole estimation errors due to not incorporating anisotropic conductivities in realistic head models for EEG source analysis

Science.gov (United States)

Hallez, Hans; Staelens, Steven; Lemahieu, Ignace

2009-10-01

EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10°. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.

Dipole estimation errors due to not incorporating anisotropic conductivities in realistic head models for EEG source analysis

International Nuclear Information System (INIS)

Hallez, Hans; Staelens, Steven; Lemahieu, Ignace

2009-01-01

EEG source analysis is a valuable tool for brain functionality research and for diagnosing neurological disorders, such as epilepsy. It requires a geometrical representation of the human head or a head model, which is often modeled as an isotropic conductor. However, it is known that some brain tissues, such as the skull or white matter, have an anisotropic conductivity. Many studies reported that the anisotropic conductivities have an influence on the calculated electrode potentials. However, few studies have assessed the influence of anisotropic conductivities on the dipole estimations. In this study, we want to determine the dipole estimation errors due to not taking into account the anisotropic conductivities of the skull and/or brain tissues. Therefore, head models are constructed with the same geometry, but with an anisotropically conducting skull and/or brain tissue compartment. These head models are used in simulation studies where the dipole location and orientation error is calculated due to neglecting anisotropic conductivities of the skull and brain tissue. Results show that not taking into account the anisotropic conductivities of the skull yields a dipole location error between 2 and 25 mm, with an average of 10 mm. When the anisotropic conductivities of the brain tissues are neglected, the dipole location error ranges between 0 and 5 mm. In this case, the average dipole location error was 2.3 mm. In all simulations, the dipole orientation error was smaller than 10 deg. We can conclude that the anisotropic conductivities of the skull have to be incorporated to improve the accuracy of EEG source analysis. The results of the simulation, as presented here, also suggest that incorporation of the anisotropic conductivities of brain tissues is not necessary. However, more studies are needed to confirm these suggestions.

MEG and EEG data analysis with MNE-Python.

Science.gov (United States)

Gramfort, Alexandre; Luessi, Martin; Larson, Eric; Engemann, Denis A; Strohmeier, Daniel; Brodbeck, Christian; Goj, Roman; Jas, Mainak; Brooks, Teon; Parkkonen, Lauri; Hämäläinen, Matti

2013-12-26

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods. As part of the MNE software suite, MNE-Python is an open-source software package that addresses this challenge by providing state-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. Moreover, MNE-Python is tightly integrated with the core Python libraries for scientific comptutation (NumPy, SciPy) and visualization (matplotlib and Mayavi), as well as the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD license allowing code reuse, even in commercial products. Although MNE-Python has only been under heavy development for a couple of years, it has rapidly evolved with expanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices. MNE-Python also gives easy access to preprocessed datasets, helping users to get started quickly and facilitating reproducibility of methods by other researchers. Full documentation, including dozens of examples, is available at http://martinos.org/mne.

EEG source localization in full-term newborns with hypoxic-ischemia

NARCIS (Netherlands)

Jennekens, W.; Dankers, F.; Blijham, P.; Cluitmans, P.; van Pul, C.; Andriessen, P.

2013-01-01

The aim of this study was to evaluate EEG source localization by standardized weighted low-resolution brain electromagnetic tomography (swLORETA) for monitoring of fullterm newborns with hypoxic-ischemic encephalopathy, using a standard anatomic head model. Three representative examples of neonatal

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

Directory of Open Access Journals (Sweden)

Chin-Teng Lin

2018-01-01

Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

Brain electric correlates of strong belief in paranormal phenomena: intracerebral EEG source and regional Omega complexity analyses.

Science.gov (United States)

Pizzagalli, D; Lehmann, D; Gianotti, L; Koenig, T; Tanaka, H; Wackermann, J; Brugger, P

2000-12-22

The neurocognitive processes underlying the formation and maintenance of paranormal beliefs are important for understanding schizotypal ideation. Behavioral studies indicated that both schizotypal and paranormal ideation are based on an overreliance on the right hemisphere, whose coarse rather than focussed semantic processing may favor the emergence of 'loose' and 'uncommon' associations. To elucidate the electrophysiological basis of these behavioral observations, 35-channel resting EEG was recorded in pre-screened female strong believers and disbelievers during resting baseline. EEG data were subjected to FFT-Dipole-Approximation analysis, a reference-free frequency-domain dipole source modeling, and Regional (hemispheric) Omega Complexity analysis, a linear approach estimating the complexity of the trajectories of momentary EEG map series in state space. Compared to disbelievers, believers showed: more right-located sources of the beta2 band (18.5-21 Hz, excitatory activity); reduced interhemispheric differences in Omega complexity values; higher scores on the Magical Ideation scale; more general negative affect; and more hypnagogic-like reveries after a 4-min eyes-closed resting period. Thus, subjects differing in their declared paranormal belief displayed different active, cerebral neural populations during resting, task-free conditions. As hypothesized, believers showed relatively higher right hemispheric activation and reduced hemispheric asymmetry of functional complexity. These markers may constitute the neurophysiological basis for paranormal and schizotypal ideation.

SCoT: A Python Toolbox for EEG Source Connectivity

Directory of Open Access Journals (Sweden)

Martin eBillinger

2014-03-01

Full Text Available Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG. Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs require single-trial estimation methods.In this paper, we present SCoT – a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with theMVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting.We demonstrate basic usage of SCoT on motor imagery (MI data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1 brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2 offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.

EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

KAUST Repository

Tian, Tian Siva; Huang, Jianhua Z.; Shen, Haipeng; Li, Zhimin

2013-01-01

In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously

Bayesian model selection of template forward models for EEG source reconstruction.

Science.gov (United States)

Strobbe, Gregor; van Mierlo, Pieter; De Vos, Maarten; Mijović, Bogdan; Hallez, Hans; Van Huffel, Sabine; López, José David; Vandenberghe, Stefaan

2014-06-01

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.

Science.gov (United States)

Sarrigiannis, Ptolemaios G; Zhao, Yifan; Wei, Hua-Liang; Billings, Stephen A; Fotheringham, Jayne; Hadjivassiliou, Marios

2014-01-01

To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing

NARCIS (Netherlands)

Cohen, M.X.; Ridderinkhof, K.R.

2013-01-01

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict

EEG Neurofeedback treatments in children with ADHD: An updated meta-analysis of Randomized Controlled Trials

Directory of Open Access Journals (Sweden)

Jean-Arthur eMicoulaud Franchi

2014-11-01

Full Text Available Objective We undertook a meta-analysis of published Randomized Controlled Trials (RCT with semi-active control and sham-NF groups to determine whether EEG-NF significantly improves the overall symptoms, inattention and hyperactivity/impulsivity dimensions for probably unblinded assessment (parent assessment and probably blinded assessment (teacher assessment in children with Attention Deficit Hyperactivity Disorder (ADHD.Data Sources A systematic review identified independent studies that were eligible for inclusion in a random effects meta-analysis.Data Extraction Effect sizes for ADHD symptoms were expressed as standardized mean differences (SMD with 95% confidence intervals.ResultsFive identified studies met eligibility criteria, 263 patients with ADHD were included, 146 patients were trained with EEG-NF. On parent assessment (probably unblinded assessment, the overall ADHD score (SMD=-0.49 [-0.74, -0.24], the inattention score (SMD=-0.46 [-0.76, -0.15] and the hyperactivity/impulsivity score (SMD=-0.34 [-0.59, -0.09] were significantly improved in patients receiving EEG-NF compared to controls. On teacher assessment (probably blinded assessment, only the inattention score was significantly improved in patients receiving EEG-NF compared to controls (SMD=-0.30 [-0.58, -0.03]. ConclusionsThis meta-analysis of EEG-NF in children with ADHD highlights improvement in the inattention dimension of ADHD symptoms. Future investigations should pay greater attention to adequately blinded studies and EEG-NF protocols that carefully control the implementation and embedding of training.

The role of the standard EEG in clinical psychiatry.

LENUS (Irish Health Repository)

O'Sullivan, S S

2012-02-03

BACKGROUND: The EEG is a commonly requested test on patients attending psychiatric services, predominantly to investigate for a possible organic brain syndrome causing behavioural changes. AIMS: To assess referrals for EEG from psychiatric services in comparison with those from other sources. We determine which clinical factors were associated with an abnormal EEG in patients referred from psychiatric sources. METHODS: A retrospective review of EEG requests in a 1-year period was performed. Analysis of referral reasons for psychiatric patients was undertaken, and outcome of patients referred from psychiatric services post-EEG was reviewed. RESULTS: One thousand four hundred and seventy EEGs were reviewed, of which 91 (6.2%) were referred from psychiatry. Neurology service referrals had detection rates of abnormal EEGs of 27%, with psychiatric referrals having the lowest abnormality detection rate of 17.6% (p < 0.1). In psychiatric-referred patients the only significant predictors found of an abnormal EEG were a known history of epilepsy (p < 0.001), being on clozapine (p < 0.05), and a possible convulsive seizure (RR = 6.51). Follow-up data of 53 patients did not reveal a significant clinical impact of EEG results on patient management. CONCLUSIONS: Many patients are referred for EEG from psychiatric sources despite a relatively low index of suspicion of an organic brain disorders, based on reasons for referral documented, with an unsurprising low clinical yield.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring.

Science.gov (United States)

Zibrandtsen, I C; Kidmose, P; Christensen, C B; Kjaer, T W

2017-12-01

Ear-EEG is recording of electroencephalography from a small device in the ear. This is the first study to compare ictal and interictal abnormalities recorded with ear-EEG and simultaneous scalp-EEG in an epilepsy monitoring unit. We recorded and compared simultaneous ear-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal spikes. There were no differences in sensitivity or specificity for seizure detection. Mean correlation coefficient between ear-EEG and nearest scalp electrode was above 0.6 with a statistically significant decreasing trend with increasing distance away from the ear. Ictal morphology and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Localizing Brain Activity from Multiple Distinct Sources via EEG

Directory of Open Access Journals (Sweden)

George Dassios

2014-01-01

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

Multivariate spectral-analysis of movement-related EEG data

International Nuclear Information System (INIS)

Andrew, C. M.

1997-01-01

The univariate method of event-related desynchronization (ERD) analysis, which quantifies the temporal evolution of power within specific frequency bands from electroencephalographic (EEG) data recorded during a task or event, is extended to an event related multivariate spectral analysis method. With this method, time courses of cross-spectra, phase spectra, coherence spectra, band-averaged coherence values (event-related coherence, ERCoh), partial power spectra and partial coherence spectra are estimated from an ensemble of multivariate event-related EEG trials. This provides a means of investigating relationships between EEG signals recorded over different scalp areas during the performance of a task or the occurrence of an event. The multivariate spectral analysis method is applied to EEG data recorded during three different movement-related studies involving discrete right index finger movements. The first study investigates the impact of the EEG derivation type on the temporal evolution of interhemispheric coherence between activity recorded at electrodes overlying the left and right sensorimotor hand areas during cued finger movement. The question results whether changes in coherence necessarily reflect changes in functional coupling of the cortical structures underlying the recording electrodes. The method is applied to data recorded during voluntary finger movement and a hypothesis, based on an existing global/local model of neocortical dynamics, is formulated to explain the coherence results. The third study applies partial spectral analysis too, and investigates phase relationships of, movement-related data recorded from a full head montage, thereby providing further results strengthening the global/local hypothesis. (author)

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.

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.

Simultaneous EEG Source and Forward Model Reconstruction (SOFOMORE) using a Hierarchical Bayesian Approach

DEFF Research Database (Denmark)

Stahlhut, Carsten; Mørup, Morten; Winther, Ole

2011-01-01

We present an approach to handle forward model uncertainty for EEG source reconstruction. A stochastic forward model representation is motivated by the many random contributions to the path from sources to measurements including the tissue conductivity distribution, the geometry of the cortical s...

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

Science.gov (United States)

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

2014-01-01

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

Zoomed MRI Guided by Combined EEG/MEG Source Analysis: A Multimodal Approach for Optimizing Presurgical Epilepsy Work-up and its Application in a Multi-focal Epilepsy Patient Case Study.

Science.gov (United States)

Aydin, Ü; Rampp, S; Wollbrink, A; Kugel, H; Cho, J -H; Knösche, T R; Grova, C; Wellmer, J; Wolters, C H

2017-07-01

In recent years, the use of source analysis based on electroencephalography (EEG) and magnetoencephalography (MEG) has gained considerable attention in presurgical epilepsy diagnosis. However, in many cases the source analysis alone is not used to tailor surgery unless the findings are confirmed by lesions, such as, e.g., cortical malformations in MRI. For many patients, the histology of tissue resected from MRI negative epilepsy shows small lesions, which indicates the need for more sensitive MR sequences. In this paper, we describe a technique to maximize the synergy between combined EEG/MEG (EMEG) source analysis and high resolution MRI. The procedure has three main steps: (1) construction of a detailed and calibrated finite element head model that considers the variation of individual skull conductivities and white matter anisotropy, (2) EMEG source analysis performed on averaged interictal epileptic discharges (IED), (3) high resolution (0.5 mm) zoomed MR imaging, limited to small areas centered at the EMEG source locations. The proposed new diagnosis procedure was then applied in a particularly challenging case of an epilepsy patient: EMEG analysis at the peak of the IED coincided with a right frontal focal cortical dysplasia (FCD), which had been detected at standard 1 mm resolution MRI. Of higher interest, zoomed MR imaging (applying parallel transmission, 'ZOOMit') guided by EMEG at the spike onset revealed a second, fairly subtle, FCD in the left fronto-central region. The evaluation revealed that this second FCD, which had not been detectable with standard 1 mm resolution, was the trigger of the seizures.

Quantitative EEG and Current Source Density Analysis of Combined Antiepileptic Drugs and Dopaminergic Agents in Genetic Epilepsy: Two Case Studies.

Science.gov (United States)

Emory, Hamlin; Wells, Christopher; Mizrahi, Neptune

2015-07-01

Two adolescent females with absence epilepsy were classified, one as attention deficit and the other as bipolar disorder. Physical and cognitive exams identified hypotension, bradycardia, and cognitive dysfunction. Their initial electroencephalograms (EEGs) were considered slightly slow, but within normal limits. Quantitative EEG (QEEG) data included relative theta excess and low alpha mean frequencies. A combined treatment of antiepileptic drugs with a catecholamine agonist/reuptake inhibitor was sequentially used. Both patients' physical and cognitive functions improved and they have remained seizure free. The clinical outcomes were correlated with statistically significant changes in QEEG measures toward normal Z-scores in both anterior and posterior regions. In addition, low resolution electromagnetic tomography (LORETA) Z-scored source correlation analyses of the initial and treated QEEG data showed normalized patterns, supporting a neuroanatomic resolution. This study presents preliminary evidence for a neurophysiologic approach to patients with absence epilepsy and comorbid disorders and may provide a method for further research. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy

DEFF Research Database (Denmark)

Zibrandtsen, I. C.; Kidmose, P.; Christensen, C. B.

2017-01-01

-EEG and scalp-EEG from 15 patients with suspected temporal lobe epilepsy. EEGs were compared visually by independent neurophysiologists. Correlation and time-frequency analysis was used to quantify the similarity between ear and scalp electrodes. Spike-averages were used to assess similarity of interictal...... and frequency dynamics can be observed from visual inspection and time-frequency analysis. Spike averages derived from ear-EEG electrodes yield a recognizable spike appearance. Conclusions Our results suggest that ear-EEG can reliably detect electroencephalographic patterns associated with focal temporal lobe...... seizures. Interictal spike morphology from sufficiently large temporal spike sources can be sampled using ear-EEG. Significance Ear-EEG is likely to become an important tool in clinical epilepsy monitoring and diagnosis....

Probabilistic M/EEG source imaging from sparse spatio-temporal event structure

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Wipf, David

While MEG and EEG source imaging methods have to tackle a severely ill-posed problem their success can be stated as their ability to constrain the solutions using appropriate priors. In this paper we propose a hierarchical Bayesian model facilitating spatio-temporal patterns through the use of bo...

Integration of EEG source imaging and fMRI during continuous viewing of natural movies.

Science.gov (United States)

Whittingstall, Kevin; Bartels, Andreas; Singh, Vanessa; Kwon, Soyoung; Logothetis, Nikos K

2010-10-01

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are noninvasive neuroimaging tools which can be used to measure brain activity with excellent temporal and spatial resolution, respectively. By combining the neural and hemodynamic recordings from these modalities, we can gain better insight into how and where the brain processes complex stimuli, which may be especially useful in patients with different neural diseases. However, due to their vastly different spatial and temporal resolutions, the integration of EEG and fMRI recordings is not always straightforward. One fundamental obstacle has been that paradigms used for EEG experiments usually rely on event-related paradigms, while fMRI is not limited in this regard. Therefore, here we ask whether one can reliably localize stimulus-driven EEG activity using the continuously varying feature intensities occurring in natural movie stimuli presented over relatively long periods of time. Specifically, we asked whether stimulus-driven aspects in the EEG signal would be co-localized with the corresponding stimulus-driven BOLD signal during free viewing of a movie. Secondly, we wanted to integrate the EEG signal directly with the BOLD signal, by estimating the underlying impulse response function (IRF) that relates the BOLD signal to the underlying current density in the primary visual area (V1). We made sequential fMRI and 64-channel EEG recordings in seven subjects who passively watched 2-min-long segments of a James Bond movie. To analyze EEG data in this natural setting, we developed a method based on independent component analysis (ICA) to reject EEG artifacts due to blinks, subject movement, etc., in a way unbiased by human judgment. We then calculated the EEG source strength of this artifact-free data at each time point of the movie within the entire brain volume using low-resolution electromagnetic tomography (LORETA). This provided for every voxel in the brain (i.e., in 3D space) an

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

Directory of Open Access Journals (Sweden)

Arjon Turnip

2014-12-01

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

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

Directory of Open Access Journals (Sweden)

Roberta eSclocco

2014-04-01

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

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

Science.gov (United States)

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

2013-01-01

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

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

Directory of Open Access Journals (Sweden)

Anna Elisabetta eVaudano

2013-11-01

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

Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

Science.gov (United States)

Owen, Julia P; Wipf, David P; Attias, Hagai T; Sekihara, Kensuke; Nagarajan, Srikantan S

2012-03-01

In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. Copyright © 2011 Elsevier Inc. All rights reserved.

Singular spectrum analysis of sleep EEG in insomnia.

Science.gov (United States)

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

2011-08-01

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

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

Science.gov (United States)

Mäkelä, Niko; Stenroos, Matti; Sarvas, Jukka; Ilmoniemi, Risto J

2018-02-15

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications. Copyright © 2017 Elsevier Inc. All rights reserved.

MEG and EEG data analysis with MNE-Python

Directory of Open Access Journals (Sweden)

Alexandre eGramfort

2013-12-01

Full Text Available Magnetoencephalography and electroencephalography (M/EEG measure the weakelectromagnetic signals generated by neuronal activity in the brain. Using thesesignals to characterize and locate neural activation in the brain is achallenge that requires expertise in physics, signalprocessing, statistics, and numerical methods. As part of the MNE softwaresuite, MNE-Python is an open-sourcesoftware package that addresses this challenge by providingstate-of-the-art algorithms implemented in Python that cover multiple methods of data preprocessing, source localization, statistical analysis, and estimation offunctional connectivity between distributed brain regions.All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysispipelines by writing Python scripts.Moreover, MNE-Python is tightly integrated with the core Python libraries for scientificcomptutation (Numpy, Scipy and visualization (matplotlib and Mayavi, as wellas the greater neuroimaging ecosystem in Python via the Nibabel package. The code is provided under the new BSD licenseallowing code reuse, even in commercial products. Although MNE-Python has onlybeen under heavy development for a couple of years, it has rapidly evolved withexpanded analysis capabilities and pedagogical tutorials because multiple labs have collaborated during code development to help share best practices.MNE-Python also gives easy access to preprocessed datasets,helping users to get started quickly and facilitating reproducibility ofmethods by other researchers. Full documentation, including dozens ofexamples, is available at http://martinos.org/mne.

The Role of Skull Modeling in EEG Source Imaging for Patients with Refractory Temporal Lobe Epilepsy.

Science.gov (United States)

Montes-Restrepo, Victoria; Carrette, Evelien; Strobbe, Gregor; Gadeyne, Stefanie; Vandenberghe, Stefaan; Boon, Paul; Vonck, Kristl; Mierlo, Pieter van

2016-07-01

We investigated the influence of different skull modeling approaches on EEG source imaging (ESI), using data of six patients with refractory temporal lobe epilepsy who later underwent successful epilepsy surgery. Four realistic head models with different skull compartments, based on finite difference methods, were constructed for each patient: (i) Three models had skulls with compact and spongy bone compartments as well as air-filled cavities, segmented from either computed tomography (CT), magnetic resonance imaging (MRI) or a CT-template and (ii) one model included a MRI-based skull with a single compact bone compartment. In all patients we performed ESI of single and averaged spikes marked in the clinical 27-channel EEG by the epileptologist. To analyze at which time point the dipole estimations were closer to the resected zone, ESI was performed at two time instants: the half-rising phase and peak of the spike. The estimated sources for each model were validated against the resected area, as indicated by the postoperative MRI. Our results showed that single spike analysis was highly influenced by the signal-to-noise ratio (SNR), yielding estimations with smaller distances to the resected volume at the peak of the spike. Although averaging reduced the SNR effects, it did not always result in dipole estimations lying closer to the resection. The proposed skull modeling approaches did not lead to significant differences in the localization of the irritative zone from clinical EEG data with low spatial sampling density. Furthermore, we showed that a simple skull model (MRI-based) resulted in similar accuracy in dipole estimation compared to more complex head models (based on CT- or CT-template). Therefore, all the considered head models can be used in the presurgical evaluation of patients with temporal lobe epilepsy to localize the irritative zone from low-density clinical EEG recordings.

The FieldTrip-SimBio pipeline for EEG forward solutions.

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Vorwerk, Johannes; Oostenveld, Robert; Piastra, Maria Carla; Magyari, Lilla; Wolters, Carsten H

2018-03-27

Accurately solving the electroencephalography (EEG) forward problem is crucial for precise EEG source analysis. Previous studies have shown that the use of multicompartment head models in combination with the finite element method (FEM) can yield high accuracies both numerically and with regard to the geometrical approximation of the human head. However, the workload for the generation of multicompartment head models has often been too high and the use of publicly available FEM implementations too complicated for a wider application of FEM in research studies. In this paper, we present a MATLAB-based pipeline that aims to resolve this lack of easy-to-use integrated software solutions. The presented pipeline allows for the easy application of five-compartment head models with the FEM within the FieldTrip toolbox for EEG source analysis. The FEM from the SimBio toolbox, more specifically the St. Venant approach, was integrated into the FieldTrip toolbox. We give a short sketch of the implementation and its application, and we perform a source localization of somatosensory evoked potentials (SEPs) using this pipeline. We then evaluate the accuracy that can be achieved using the automatically generated five-compartment hexahedral head model [skin, skull, cerebrospinal fluid (CSF), gray matter, white matter] in comparison to a highly accurate tetrahedral head model that was generated on the basis of a semiautomatic segmentation with very careful and time-consuming manual corrections. The source analysis of the SEP data correctly localizes the P20 component and achieves a high goodness of fit. The subsequent comparison to the highly detailed tetrahedral head model shows that the automatically generated five-compartment head model performs about as well as a highly detailed four-compartment head model (skin, skull, CSF, brain). This is a significant improvement in comparison to a three-compartment head model, which is frequently used in praxis, since the importance of

Analytic sensing for multi-layer spherical models with application to EEG source imaging

OpenAIRE

Kandaswamy, Djano; Blu, Thierry; Van De Ville, Dimitri

2013-01-01

Source imaging maps back boundary measurements to underlying generators within the domain; e. g., retrieving the parameters of the generating dipoles from electrical potential measurements on the scalp such as in electroencephalography (EEG). Fitting such a parametric source model is non-linear in the positions of the sources and renewed interest in mathematical imaging has led to several promising approaches. One important step in these methods is the application of a sensing principle that ...

EEG analysis in a telemedical virtual world

NARCIS (Netherlands)

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

1999-01-01

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

Unsupervised EEG analysis for automated epileptic seizure detection

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Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

2016-07-01

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

Estimation of effective brain connectivity with dual Kalman filter and EEG source localization methods.

Science.gov (United States)

Rajabioun, Mehdi; Nasrabadi, Ali Motie; Shamsollahi, Mohammad Bagher

2017-09-01

Effective connectivity is one of the most important considerations in brain functional mapping via EEG. It demonstrates the effects of a particular active brain region on others. In this paper, a new method is proposed which is based on dual Kalman filter. In this method, firstly by using a brain active localization method (standardized low resolution brain electromagnetic tomography) and applying it to EEG signal, active regions are extracted, and appropriate time model (multivariate autoregressive model) is fitted to extracted brain active sources for evaluating the activity and time dependence between sources. Then, dual Kalman filter is used to estimate model parameters or effective connectivity between active regions. The advantage of this method is the estimation of different brain parts activity simultaneously with the calculation of effective connectivity between active regions. By combining dual Kalman filter with brain source localization methods, in addition to the connectivity estimation between parts, source activity is updated during the time. The proposed method performance has been evaluated firstly by applying it to simulated EEG signals with interacting connectivity simulation between active parts. Noisy simulated signals with different signal to noise ratios are used for evaluating method sensitivity to noise and comparing proposed method performance with other methods. Then the method is applied to real signals and the estimation error during a sweeping window is calculated. By comparing proposed method results in different simulation (simulated and real signals), proposed method gives acceptable results with least mean square error in noisy or real conditions.

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

Science.gov (United States)

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

2015-07-30

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

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

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Bleichner, Martin G.; Debener, Stefan

2017-01-01

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

Multi-scale symbolic transfer entropy analysis of EEG

Science.gov (United States)

Yao, Wenpo; Wang, Jun

2017-10-01

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

Optimal Measurement Conditions for Spatiotemporal EEG/MEG Source Analysis.

Science.gov (United States)

Huizenga, Hilde M.; Heslenfeld, Dirk J.; Molenaar, Peter C. M.

2002-01-01

Developed a method to determine the required number and position of sensors for human brain electromagnetic source analysis. Studied the method through a simulation study and an empirical study on visual evoked potentials in one adult male. Results indicate the method is fast and reliable and improves source precision. (SLD)

EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing

OpenAIRE

Cohen, M.X.; Ridderinkhof, K.R.

2013-01-01

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (?200 ms post-stimulus) conflict modulation in ...

Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models.

Science.gov (United States)

Paz-Linares, Deirel; Vega-Hernández, Mayrim; Rojas-López, Pedro A; Valdés-Hernández, Pedro A; Martínez-Montes, Eduardo; Valdés-Sosa, Pedro A

2017-01-01

The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods

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

Science.gov (United States)

Jäncke, Lutz; Alahmadi, Nsreen

2016-01-01

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

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%.

Feature Selection and Blind Source Separation in an EEG-Based Brain-Computer Interface

Directory of Open Access Journals (Sweden)

Michael H. Thaut

2005-11-01

Full Text Available Most EEG-based BCI systems make use of well-studied patterns of brain activity. However, those systems involve tasks that indirectly map to simple binary commands such as “yes” or “no” or require many weeks of biofeedback training. We hypothesized that signal processing and machine learning methods can be used to discriminate EEG in a direct “yes”/“no” BCI from a single session. Blind source separation (BSS and spectral transformations of the EEG produced a 180-dimensional feature space. We used a modified genetic algorithm (GA wrapped around a support vector machine (SVM classifier to search the space of feature subsets. The GA-based search found feature subsets that outperform full feature sets and random feature subsets. Also, BSS transformations of the EEG outperformed the original time series, particularly in conjunction with a subset search of both spaces. The results suggest that BSS and feature selection can be used to improve the performance of even a “direct,” single-session BCI.

Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals

Science.gov (United States)

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

2016-05-01

Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.

EEG Based Inference of Spatio-Temporal Brain Dynamics

DEFF Research Database (Denmark)

Hansen, Sofie Therese

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

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

Directory of Open Access Journals (Sweden)

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.

The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction.

Science.gov (United States)

Strohmeier, Daniel; Bekhti, Yousra; Haueisen, Jens; Gramfort, Alexandre

2016-10-01

Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l 1 -norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l 0.5 -quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.

Market and system integration of renewable energy sources. A legal analysis of the regulations for direct marketing in the EEG 2012; Markt- und Systemintegration der Erneuerbaren-Energien. Eine rechtliche Analyse der Regeln zur Direktvermarktung im EEG 2012

Energy Technology Data Exchange (ETDEWEB)

Lehnert, Wieland [Kanzlei Becker Buettner Held, Berlin (Germany)

2012-07-01

The EEG 2012 presents optional funding instruments for direct marketing of EEG electricity, in particular a market bonus and a flexibility bonus. The author presents the new regulation for direct marketing in the EEG 2012, in consideration of the practice in energy law. Important aspects are a systematic integration of the new specifications in energy law and an analysis of their contents. Effects on the practice of energy management are investigated, as well as the chances and risks connected with implementation by the energy market partners.

Evaluation of a finite-element reciprocity method for epileptic EEG source localization: Accuracy, computational complexity and noise robustness

DEFF Research Database (Denmark)

Shirvany, Yazdan; Rubæk, Tonny; Edelvik, Fredrik

2013-01-01

The aim of this paper is to evaluate the performance of an EEG source localization method that combines a finite element method (FEM) and the reciprocity theorem.The reciprocity method is applied to solve the forward problem in a four-layer spherical head model for a large number of test dipoles...... noise and electrode misplacement.The results show approximately 3% relative error between numerically calculated potentials done by the reciprocity theorem and the analytical solutions. When adding EEG noise with SNR between 5 and 10, the mean localization error is approximately 4.3 mm. For the case...... with 10 mm electrode misplacement the localization error is 4.8 mm. The reciprocity EEG source localization speeds up the solution of the inverse problem with more than three orders of magnitude compared to the state-of-the-art methods.The reciprocity method has high accuracy for modeling the dipole...

A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA

Science.gov (United States)

Cosandier-Rimélé, D.; Ramantani, G.; Zentner, J.; Schulze-Bonhage, A.; Dümpelmann, M.

2017-10-01

Objective. Electrical source localization (ESL) deriving from scalp EEG and, in recent years, from intracranial EEG (iEEG), is an established method in epilepsy surgery workup. We aimed to validate the distributed ESL derived from scalp EEG and iEEG, particularly regarding the spatial extent of the source, using a realistic epileptic spike activity simulator. Approach. ESL was applied to the averaged scalp EEG and iEEG spikes of two patients with drug-resistant structural epilepsy. The ESL results for both patients were used to outline the location and extent of epileptic cortical patches, which served as the basis for designing a spatiotemporal source model. EEG signals for both modalities were then generated for different anatomic locations and spatial extents. ESL was subsequently performed on simulated signals with sLORETA, a commonly used distributed algorithm. ESL accuracy was quantitatively assessed for iEEG and scalp EEG. Main results. The source volume was overestimated by sLORETA at both EEG scales, with the error increasing with source size, particularly for iEEG. For larger sources, ESL accuracy drastically decreased, and reconstruction volumes shifted to the center of the head for iEEG, while remaining stable for scalp EEG. Overall, the mislocalization of the reconstructed source was more pronounced for iEEG. Significance. We present a novel multiscale framework for the evaluation of distributed ESL, based on realistic multiscale EEG simulations. Our findings support that reconstruction results for scalp EEG are often more accurate than for iEEG, owing to the superior 3D coverage of the head. Particularly the iEEG-derived reconstruction results for larger, widespread generators should be treated with caution.

A realistic multimodal modeling approach for the evaluation of distributed source analysis: application to sLORETA.

Science.gov (United States)

Cosandier-Rimélé, D; Ramantani, G; Zentner, J; Schulze-Bonhage, A; Dümpelmann, M

2017-10-01

Electrical source localization (ESL) deriving from scalp EEG and, in recent years, from intracranial EEG (iEEG), is an established method in epilepsy surgery workup. We aimed to validate the distributed ESL derived from scalp EEG and iEEG, particularly regarding the spatial extent of the source, using a realistic epileptic spike activity simulator. ESL was applied to the averaged scalp EEG and iEEG spikes of two patients with drug-resistant structural epilepsy. The ESL results for both patients were used to outline the location and extent of epileptic cortical patches, which served as the basis for designing a spatiotemporal source model. EEG signals for both modalities were then generated for different anatomic locations and spatial extents. ESL was subsequently performed on simulated signals with sLORETA, a commonly used distributed algorithm. ESL accuracy was quantitatively assessed for iEEG and scalp EEG. The source volume was overestimated by sLORETA at both EEG scales, with the error increasing with source size, particularly for iEEG. For larger sources, ESL accuracy drastically decreased, and reconstruction volumes shifted to the center of the head for iEEG, while remaining stable for scalp EEG. Overall, the mislocalization of the reconstructed source was more pronounced for iEEG. We present a novel multiscale framework for the evaluation of distributed ESL, based on realistic multiscale EEG simulations. Our findings support that reconstruction results for scalp EEG are often more accurate than for iEEG, owing to the superior 3D coverage of the head. Particularly the iEEG-derived reconstruction results for larger, widespread generators should be treated with caution.

Localization of extended brain sources from EEG/MEG: the ExSo-MUSIC approach.

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Birot, Gwénaël; Albera, Laurent; Wendling, Fabrice; Merlet, Isabelle

2011-05-01

We propose a new MUSIC-like method, called 2q-ExSo-MUSIC (q ≥ 1). This method is an extension of the 2q-MUSIC (q ≥ 1) approach for solving the EEG/MEG inverse problem, when spatially-extended neocortical sources ("ExSo") are considered. It introduces a novel ExSo-MUSIC principle. The novelty is two-fold: i) the parameterization of the spatial source distribution that leads to an appropriate metric in the context of distributed brain sources and ii) the introduction of an original, efficient and low-cost way of optimizing this metric. In 2q-ExSo-MUSIC, the possible use of higher order statistics (q ≥ 2) offers a better robustness with respect to Gaussian noise of unknown spatial coherence and modeling errors. As a result we reduced the penalizing effects of both the background cerebral activity that can be seen as a Gaussian and spatially correlated noise, and the modeling errors induced by the non-exact resolution of the forward problem. Computer results on simulated EEG signals obtained with physiologically-relevant models of both the sources and the volume conductor show a highly increased performance of our 2q-ExSo-MUSIC method as compared to the classical 2q-MUSIC algorithms. Copyright © 2011 Elsevier Inc. All rights reserved.

Optimizing detection and analysis of slow waves in sleep EEG.

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Mensen, Armand; Riedner, Brady; Tononi, Giulio

2016-12-01

Analysis of individual slow waves in EEG recording during sleep provides both greater sensitivity and specificity compared to spectral power measures. However, parameters for detection and analysis have not been widely explored and validated. We present a new, open-source, Matlab based, toolbox for the automatic detection and analysis of slow waves; with adjustable parameter settings, as well as manual correction and exploration of the results using a multi-faceted visualization tool. We explore a large search space of parameter settings for slow wave detection and measure their effects on a selection of outcome parameters. Every choice of parameter setting had some effect on at least one outcome parameter. In general, the largest effect sizes were found when choosing the EEG reference, type of canonical waveform, and amplitude thresholding. Previously published methods accurately detect large, global waves but are conservative and miss the detection of smaller amplitude, local slow waves. The toolbox has additional benefits in terms of speed, user-interface, and visualization options to compare and contrast slow waves. The exploration of parameter settings in the toolbox highlights the importance of careful selection of detection METHODS: The sensitivity and specificity of the automated detection can be improved by manually adding or deleting entire waves and or specific channels using the toolbox visualization functions. The toolbox standardizes the detection procedure, sets the stage for reliable results and comparisons and is easy to use without previous programming experience. Copyright © 2016 Elsevier B.V. All rights reserved.

Preparing laboratory and real-world EEG data for large-scale analysis: A containerized approach

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Nima eBigdely-Shamlo

2016-03-01

Full Text Available Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface (BCI models.. However, the absence of standard-ized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the diffi-culty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a containerized approach and freely available tools we have developed to facilitate the process of an-notating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-analysis. The EEG Study Schema (ESS comprises three data Levels, each with its own XML-document schema and file/folder convention, plus a standardized (PREP pipeline to move raw (Data Level 1 data to a basic preprocessed state (Data Level 2 suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are in-creasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at eegstudy.org, and a central cata-log of over 850 GB of existing data in ESS format is available at study-catalog.org. These tools and resources are part of a larger effort to ena-ble data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org.

Multifractal analysis of real and imaginary movements: EEG study

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

2018-04-01

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

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

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

2018-02-26

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

Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal

Directory of Open Access Journals (Sweden)

Shanzhi Xu

2018-02-01

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

EEG-MEG Integration Enhances the Characterization of Functional and Effective Connectivity in the Resting State Network

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Mideksa, Kidist Gebremariam; Anwar, Abdul Rauf; Stephani, Ulrich; Deuschl, Günther; Freitag, Christine M.; Siniatchkin, Michael

2015-01-01

At the sensor level many aspects, such as spectral power, functional and effective connectivity as well as relative-power-ratio ratio (RPR) and spatial resolution have been comprehensively investigated through both electroencephalography (EEG) and magnetoencephalography (MEG). Despite this, differences between both modalities have not yet been systematically studied by direct comparison. It remains an open question as to whether the integration of EEG and MEG data would improve the information obtained from the above mentioned parameters. Here, EEG (64-channel system) and MEG (275 sensor system) were recorded simultaneously in conditions with eyes open (EO) and eyes closed (EC) in 29 healthy adults. Spectral power, functional and effective connectivity, RPR, and spatial resolution were analyzed at five different frequency bands (delta, theta, alpha, beta and gamma). Networks of functional and effective connectivity were described using a spatial filter approach called the dynamic imaging of coherent sources (DICS) followed by the renormalized partial directed coherence (RPDC). Absolute mean power at the sensor level was significantly higher in EEG than in MEG data in both EO and EC conditions. At the source level, there was a trend towards a better performance of the combined EEG+MEG analysis compared with separate EEG or MEG analyses for the source mean power, functional correlation, effective connectivity for both EO and EC. The network of coherent sources and the spatial resolution were similar for both the EEG and MEG data if they were analyzed separately. Results indicate that the combined approach has several advantages over the separate analyses of both EEG and MEG. Moreover, by a direct comparison of EEG and MEG, EEG was characterized by significantly higher values in all measured parameters in both sensor and source level. All the above conclusions are specific to the resting state task and the specific analysis used in this study to have general

Raviart–Thomas-type sources adapted to applied EEG and MEG: implementation and results

International Nuclear Information System (INIS)

Pursiainen, S

2012-01-01

This paper studies numerically electroencephalography and magnetoencephalography (EEG and MEG), two non-invasive imaging modalities in which external measurements of the electric potential and the magnetic field are, respectively, utilized to reconstruct the primary current density (neuronal activity) of the human brain. The focus is on adapting a Raviart–Thomas-type source model to meet the needs of EEG and MEG applications. The goal is to construct a model that provides an accurate approximation of dipole source currents and can be flexibly applied to different reconstruction strategies as well as to realistic computation geometries. The finite element method is applied in the simulation of the data. Least-squares fit interpolation is used to establish Cartesian source directions, which guarantee that the recovered current field is minimally dependent on the underlying finite element mesh. Implementation is explained in detail and made accessible, e.g., by using quadrature-free formulae and the Gaussian one-point rule in numerical integration. Numerical results are presented concerning, for example, the iterative alternating sequential inverse algorithm as well as resolution, smoothness and local refinement of the finite element mesh. Both spherical and pseudo-realistic head models, as well as real MEG data, are utilized in the numerical experiments. (paper)

Resonance detection of EEG signals using two-layer wavelet analysis

International Nuclear Information System (INIS)

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

2000-01-01

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

FFT transformed quantitative EEG analysis of short term memory load.

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Singh, Yogesh; Singh, Jayvardhan; Sharma, Ratna; Talwar, Anjana

2015-07-01

The EEG is considered as building block of functional signaling in the brain. The role of EEG oscillations in human information processing has been intensively investigated. To study the quantitative EEG correlates of short term memory load as assessed through Sternberg memory test. The study was conducted on 34 healthy male student volunteers. The intervention consisted of Sternberg memory test, which runs on a version of the Sternberg memory scanning paradigm software on a computer. Electroencephalography (EEG) was recorded from 19 scalp locations according to 10-20 international system of electrode placement. EEG signals were analyzed offline. To overcome the problems of fixed band system, individual alpha frequency (IAF) based frequency band selection method was adopted. The outcome measures were FFT transformed absolute powers in the six bands at 19 electrode positions. Sternberg memory test served as model of short term memory load. Correlation analysis of EEG during memory task was reflected as decreased absolute power in Upper alpha band in nearly all the electrode positions; increased power in Theta band at Fronto-Temporal region and Lower 1 alpha band at Fronto-Central region. Lower 2 alpha, Beta and Gamma band power remained unchanged. Short term memory load has distinct electroencephalographic correlates resembling the mentally stressed state. This is evident from decreased power in Upper alpha band (corresponding to Alpha band of traditional EEG system) which is representative band of relaxed mental state. Fronto-temporal Theta power changes may reflect the encoding and execution of memory task.

High-density EEG coherence analysis using functional units applied to mental fatigue

NARCIS (Netherlands)

Caat, Michael ten; Lorist, Monicque M.; Bezdan, Eniko; Roerdink, Jos B.T.M.; Maurits, Natasha M.

2008-01-01

Electroencephalography (EEG) coherence provides a quantitative measure of functional brain connectivity which is calculated between pairs of signals as a function of frequency. Without hypotheses, traditional coherence analysis would be cumbersome for high-density EEG which employs a large number of

Comparison of Amplitude-Integrated EEG and Conventional EEG in a Cohort of Premature Infants.

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Meledin, Irina; Abu Tailakh, Muhammad; Gilat, Shlomo; Yogev, Hagai; Golan, Agneta; Novack, Victor; Shany, Eilon

2017-03-01

To compare amplitude-integrated EEG (aEEG) and conventional EEG (EEG) activity in premature neonates. Biweekly aEEG and EEG were simultaneously recorded in a cohort of infants born less than 34 weeks gestation. aEEG recordings were visually assessed for lower and upper border amplitude and bandwidth. EEG recordings were compressed for visual evaluation of continuity and assessed using a signal processing software for interburst intervals (IBI) and frequencies' amplitude. Ten-minute segments of aEEG and EEG indices were compared using regression analysis. A total of 189 recordings from 67 infants were made, from which 1697 aEEG/EEG pairs of 10-minute segments were assessed. Good concordance was found for visual assessment of continuity between the 2 methods. EEG IBI, alpha and theta frequencies' amplitudes were negatively correlated to the aEEG lower border while conceptional age (CA) was positively correlated to aEEG lower border ( P continuity and amplitude.

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...

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

International Nuclear Information System (INIS)

Xu, Peng; Xiong, Xiu Chun; Tian, Yin; Zhang, Rui; Li, Pei Yang; Yao, De Zhong; Xue, Qing; Wang, Yu Ping; Peng, Yueheng

2014-01-01

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future. (paper)

Feature analysis for correlation studies of simultaneous EEG-fMRI data: A proof of concept for neurofeedback approaches.

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Simoes, Simões; Lima, João; Direito, Bruno; Castelhano, João; Ferreira, Carlos; Carvalho, Paulo; Castelo-Branco, Miguel

2015-01-01

The identification and interpretation of facial expressions is an important feature of social cognition. This characteristic is often impaired in various neurodevelopmental disorders. Recent therapeutic approaches to intervene in social communication impairments include neurofeedback (NF). In this study, we present a NF real-time functional Magnetic Resonance Imaging (rt-fMRI), combined with electroencephalography (EEG) to train social communication skills. In this sense, we defined the right Superior Temporal Sulcus as our target region-of-interest. To analyze the correlation between the fMRI regions of interest and the EEG data, we transposed the sources located at the nearest cortical location to the target region. We extracted a set of 75 features from EEG segments and performed a correlation analysis with the brain activations extracted from rt-fMRI in the right pSTS region. The finding of significant correlations of simultaneously measured signals in distinct modalities (EEG and fMRI) is promising. Future studies should address whether the observed correlation levels between local brain activity and scalp measures are sufficient to implement NF approaches.

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

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

2017-09-01

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

EEG frequency PCA in EEG-ERP dynamics.

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Barry, Robert J; De Blasio, Frances M

2018-05-01

Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG-ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes-closed and eyes-open resting conditions, followed by an equiprobable go/no-go task. Frequency PCA of the EEG, including the nontask resting and within-task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA-derived go and no-go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data-driven components from both the ERP and EEG. © 2017 Society for Psychophysiological Research.

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

Science.gov (United States)

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

2008-08-01

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

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

Directory of Open Access Journals (Sweden)

Koch Klaus

2011-08-01

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

Combining Different Tools for EEG Analysis to Study the Distributed Character of Language Processing

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Armando Freitas da Rocha

2015-01-01

Full Text Available Recent studies on language processing indicate that language cognition is better understood if assumed to be supported by a distributed intelligent processing system enrolling neurons located all over the cortex, in contrast to reductionism that proposes to localize cognitive functions to specific cortical structures. Here, brain activity was recorded using electroencephalogram while volunteers were listening or reading small texts and had to select pictures that translate meaning of these texts. Several techniques for EEG analysis were used to show this distributed character of neuronal enrollment associated with the comprehension of oral and written descriptive texts. Low Resolution Tomography identified the many different sets (si of neurons activated in several distinct cortical areas by text understanding. Linear correlation was used to calculate the information H(ei provided by each electrode of the 10/20 system about the identified si. H(ei Principal Component Analysis (PCA was used to study the temporal and spatial activation of these sources si. This analysis evidenced 4 different patterns of H(ei covariation that are generated by neurons located at different cortical locations. These results clearly show that the distributed character of language processing is clearly evidenced by combining available EEG technologies.

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Science.gov (United States)

Safieddine, Doha; Kachenoura, Amar; Albera, Laurent; Birot, Gwénaël; Karfoul, Ahmad; Pasnicu, Anca; Biraben, Arnaud; Wendling, Fabrice; Senhadji, Lotfi; Merlet, Isabelle

2012-12-01

Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that

Kurtosis based blind source extraction of complex noncircular signals with application in EEG artifact removal in real-time

Directory of Open Access Journals (Sweden)

Soroush eJavidi

2011-10-01

Full Text Available A new class of complex domain blind source extraction (BSE algorithms suitable for the extraction of both circular and noncircular complex signals is proposed. This is achieved through sequential extraction based on the degree of kurtosis and in the presence of noncircular measurement noise. The existence and uniqueness analysis of the solution is followed by a study of fast converging variants of the algorithm. The performance is first assessed through simulations on well understood benchmark signals, followed by a case study on real-time artifact removal from EEG signals, verified using both qualitative and quantitative metrics. The results illustrate the power of the proposed approach in real-time blind extraction of general complex-valued sources.

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

Science.gov (United States)

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

2016-09-01

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a

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

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

2015-07-01

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

Prediction of advertisement preference by fusing EEG response and sentiment analysis.

Science.gov (United States)

Gauba, Himaanshu; Kumar, Pradeep; Roy, Partha Pratim; Singh, Priyanka; Dogra, Debi Prosad; Raman, Balasubramanian

2017-08-01

This paper presents a novel approach to predict rating of video-advertisements based on a multimodal framework combining physiological analysis of the user and global sentiment-rating available on the internet. We have fused Electroencephalogram (EEG) waves of user and corresponding global textual comments of the video to understand the user's preference more precisely. In our framework, the users were asked to watch the video-advertisement and simultaneously EEG signals were recorded. Valence scores were obtained using self-report for each video. A higher valence corresponds to intrinsic attractiveness of the user. Furthermore, the multimedia data that comprised of the comments posted by global viewers, were retrieved and processed using Natural Language Processing (NLP) technique for sentiment analysis. Textual contents from review comments were analyzed to obtain a score to understand sentiment nature of the video. A regression technique based on Random forest was used to predict the rating of an advertisement using EEG data. Finally, EEG based rating is combined with NLP-based sentiment score to improve the overall prediction. The study was carried out using 15 video clips of advertisements available online. Twenty five participants were involved in our study to analyze our proposed system. The results are encouraging and these suggest that the proposed multimodal approach can achieve lower RMSE in rating prediction as compared to the prediction using only EEG data. Copyright © 2017 Elsevier Ltd. All rights reserved.

A three domain covariance framework for EEG/MEG data.

Science.gov (United States)

Roś, Beata P; Bijma, Fetsje; de Gunst, Mathisca C M; de Munck, Jan C

2015-10-01

In this paper we introduce a covariance framework for the analysis of single subject EEG and MEG data that takes into account observed temporal stationarity on small time scales and trial-to-trial variations. We formulate a model for the covariance matrix, which is a Kronecker product of three components that correspond to space, time and epochs/trials, and consider maximum likelihood estimation of the unknown parameter values. An iterative algorithm that finds approximations of the maximum likelihood estimates is proposed. Our covariance model is applicable in a variety of cases where spontaneous EEG or MEG acts as source of noise and realistic noise covariance estimates are needed, such as in evoked activity studies, or where the properties of spontaneous EEG or MEG are themselves the topic of interest, like in combined EEG-fMRI experiments in which the correlation between EEG and fMRI signals is investigated. We use a simulation study to assess the performance of the estimator and investigate the influence of different assumptions about the covariance factors on the estimated covariance matrix and on its components. We apply our method to real EEG and MEG data sets. Copyright © 2015 Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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

2017-10-01

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

Interval analysis of interictal EEG: pathology of the alpha rhythm in focal epilepsy

Science.gov (United States)

Pyrzowski, Jan; Siemiński, Mariusz; Sarnowska, Anna; Jedrzejczak, Joanna; Nyka, Walenty M.

2015-11-01

The contemporary use of interictal scalp electroencephalography (EEG) in the context of focal epilepsy workup relies on the visual identification of interictal epileptiform discharges. The high-specificity performance of this marker comes, however, at a cost of only moderate sensitivity. Zero-crossing interval analysis is an alternative to Fourier analysis for the assessment of the rhythmic component of EEG signals. We applied this method to standard EEG recordings of 78 patients divided into 4 subgroups: temporal lobe epilepsy (TLE), frontal lobe epilepsy (FLE), psychogenic nonepileptic seizures (PNES) and nonepileptic patients with headache. Interval-analysis based markers were capable of effectively discriminating patients with epilepsy from those in control subgroups (AUC~0.8) with diagnostic sensitivity potentially exceeding that of visual analysis. The identified putative epilepsy-specific markers were sensitive to the properties of the alpha rhythm and displayed weak or non-significant dependences on the number of antiepileptic drugs (AEDs) taken by the patients. Significant AED-related effects were concentrated in the theta interval range and an associated marker allowed for identification of patients on AED polytherapy (AUC~0.9). Interval analysis may thus, in perspective, increase the diagnostic yield of interictal scalp EEG. Our findings point to the possible existence of alpha rhythm abnormalities in patients with epilepsy.

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

Science.gov (United States)

Somers, Ben; Bertrand, Alexander

2016-12-01

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

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

Science.gov (United States)

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

Estimating Driving Performance Based on EEG Spectrum Analysis

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Jung Tzyy-Ping

2005-01-01

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

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...

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

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Federico Chella

2017-05-01

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

Semi-automated analysis of EEG spikes in the preterm fetal sheep using wavelet analysis

International Nuclear Information System (INIS)

Walbran, A.C.; Unsworth, C.P.; Gunn, A.J.; Benett, L.

2010-01-01

Full text: Presentation Preference Oral Presentation Perinatal hypoxia plays a key role in the cause of brain injury in premature infants. Cerebral hypothermia commenced in the latent phase of evolving injury (first 6-8 h post hypoxic-ischemic insult) is the lead candidate for treatment however currently there is no means to identify which infants can benefit from treatment. Recent studies suggest that epileptiform transients in latent phase are predictive of neural outcome. To quantify this, an automated means of EEG analysis is required as EEG monitoring produces vast amounts of data which is timely to analyse manually. We have developed a semi-automated EEG spike detection method which employs a discretized version of the continuous wavelet transform (CWT). EEG data was obtained from a fetal sheep at approximately 0.7 of gestation. Fetal asphyxia was maintained for 25 min and the EEG recorded for 8 h before and after asphyxia. The CWT was calculated followed by the power of the wavelet transform coefficients. Areas of high power corresponded to spike waves so thresholding was employed to identify the spikes. The performance of the method was found have a good sensitivity and selectivity, thus demonstrating that this method is a simple, robust and potentially effective spike detection algorithm.

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

Science.gov (United States)

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

2014-10-01

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

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

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Mariel Rosenblatt

2014-11-01

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

Joint optimization of algorithmic suites for EEG analysis.

Science.gov (United States)

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

2014-01-01

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

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

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Balbir Singh

2017-01-01

Full Text Available EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA, which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

Science.gov (United States)

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

Equivalent physical models and formulation of equivalent source layer in high-resolution EEG imaging

International Nuclear Information System (INIS)

Yao Dezhong; He Bin

2003-01-01

In high-resolution EEG imaging, both equivalent dipole layer (EDL) and equivalent charge layer (ECL) assumed to be located just above the cortical surface have been proposed as high-resolution imaging modalities or as intermediate steps to estimate the epicortical potential. Presented here are the equivalent physical models of these two equivalent source layers (ESL) which show that the strength of EDL is proportional to the surface potential of the layer when the outside of the layer is filled with an insulator, and that the strength of ECL is the normal current of the layer when the outside is filled with a perfect conductor. Based on these equivalent physical models, closed solutions of ECL and EDL corresponding to a dipole enclosed by a spherical layer are given. These results provide the theoretical basis of ESL applications in high-resolution EEG mapping

Equivalent physical models and formulation of equivalent source layer in high-resolution EEG imaging

Energy Technology Data Exchange (ETDEWEB)

Yao Dezhong [School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu City, 610054, Sichuan Province (China); He Bin [The University of Illinois at Chicago, IL (United States)

2003-11-07

In high-resolution EEG imaging, both equivalent dipole layer (EDL) and equivalent charge layer (ECL) assumed to be located just above the cortical surface have been proposed as high-resolution imaging modalities or as intermediate steps to estimate the epicortical potential. Presented here are the equivalent physical models of these two equivalent source layers (ESL) which show that the strength of EDL is proportional to the surface potential of the layer when the outside of the layer is filled with an insulator, and that the strength of ECL is the normal current of the layer when the outside is filled with a perfect conductor. Based on these equivalent physical models, closed solutions of ECL and EDL corresponding to a dipole enclosed by a spherical layer are given. These results provide the theoretical basis of ESL applications in high-resolution EEG mapping.

Investigation of mental fatigue through EEG signal processing based on nonlinear analysis: Symbolic dynamics

International Nuclear Information System (INIS)

Azarnoosh, Mahdi; Motie Nasrabadi, Ali; Mohammadi, Mohammad Reza; Firoozabadi, Mohammad

2011-01-01

Highlights: Mental fatigue indices’ variation discussed during simple long-term attentive task. Symbolic dynamics of reaction time and EEG signal determine mental state variation. Nonlinear quantifiers such as entropy can display chaotic behaviors of the brain. Frontal and central lobes of the brain are effective in attention investigations. Mental fatigue causes a reduction in the complexity of the brain’s activity. Abstract: To investigate nonlinear analysis of attention physiological indices this study used a simple repetitive attentive task in four consecutive trials that resulted in mental fatigue. Traditional performance indices, such as reaction time, error responses, and EEG signals, were simultaneously recorded to evaluate differences between the trials. Performance indices analysis demonstrated that a selected task leads to mental fatigue. In addition, the study aimed to find a method to determine mental fatigue based on nonlinear analysis of EEG signals. Symbolic dynamics was selected as a qualitative method used to extract some quantitative qualifiers such as entropy. This method was executed on the reaction time of responses, and EEG signals to distinguish mental states. The results revealed that nonlinear analysis of reaction time, and EEG signals of the frontal and central lobes of the brain could differentiate between attention, and occurrence of mental fatigue in trials. In addition, the trend of entropy variation displayed a reduction in the complexity of mental activity as fatigue occurred.

Bluetooth Communication Interface for EEG Signal Recording in Hyperbaric Chambers.

Science.gov (United States)

Pastena, Lucio; Formaggio, Emanuela; Faralli, Fabio; Melucci, Massimo; Rossi, Marco; Gagliardi, Riccardo; Ricciardi, Lucio; Storti, Silvia F

2015-07-01

Recording biological signals inside a hyperbaric chamber poses technical challenges (the steel walls enclosing it greatly attenuate or completely block the signals as in a Faraday cage), practical (lengthy cables creating eddy currents), and safety (sparks hazard from power supply to the electronic apparatus inside the chamber) which can be overcome with new wireless technologies. In this technical report we present the design and implementation of a Bluetooth system for electroencephalographic (EEG) recording inside a hyperbaric chamber and describe the feasibility of EEG signal transmission outside the chamber. Differently from older systems, this technology allows the online recording of amplified signals, without interference from eddy currents. In an application of this technology, we measured EEG activity in professional divers under three experimental conditions in a hyperbaric chamber to determine how oxygen, assumed at a constant hyperbaric pressure of 2.8 ATA , affects the bioelectrical activity. The EEG spectral power estimated by fast Fourier transform and the cortical sources of the EEG rhythms estimated by low-resolution brain electromagnetic analysis were analyzed in three different EEG acquisitions: breathing air at sea level; breathing oxygen at a simulated depth of 18 msw, and breathing air at sea level after decompression.

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

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Yuan-Pin Lin

2017-07-01

Full Text Available Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability, arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18 and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40% as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing neither achieved the cross-day classification (the accuracy was around chance level nor replicated the encouraging improvement due to the inter-day EEG variability. This result

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

Energy Technology Data Exchange (ETDEWEB)

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

2013-05-01

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

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

Science.gov (United States)

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

2013-01-01

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

EEG spectral analysis of attention in ADHD: implications for neurofeedback training?

Directory of Open Access Journals (Sweden)

Hartmut eHeinrich

2014-08-01

Full Text Available Objective: In children with attention-deficit/hyperactivity disorder (ADHD, an increased theta/beta ratio in the resting EEG typically serves as a rationale to conduct theta/beta neurofeedback training. However, this finding is increasingly challenged. As neurofeedback may rather target an active than a passive state, we studied the EEG in a condition that requires attention.Methods: In children with ADHD of the DSM-IV combined type (ADHD-C; N=15 and of the predominantly inattentive type (ADHD-I; N=9 and in typically developing children (N=19, EEG spectral analysis was conducted for segments during the attention network test without processing of stimuli and overt behavior. Frontal (F3, Fz, F4, central (C3, Cz, C4 and parietal (P3, Pz, P4 electrodes were included in the statistical analysis. To investigate if EEG spectral parameters are related to performance measures, correlation coefficients were calculated.Results: Particularly in the ADHD-C group, higher theta and alpha activity was found with the most prominent effect in the upper-theta/lower-alpha (5.5-10.5 Hz range. In the ADHD-I group, a significantly higher theta/beta ratio was observed at single electrodes (F3, Fz and a tendency for a higher theta/beta ratio when considering all electrodes (large effect size. Higher 5.5-10.5 Hz activity was associated with higher reaction time variability with the effect most prominent in the ADHD-C group. A higher theta/beta ratio was associated with higher reaction times, particularly in the ADHD-I group.Conclusions: 1. In an attention demanding period, children with ADHD are characterized by an underactivated state in the EEG with subtype-specific differences. 2. The functional relevance of related EEG parameters is indicated by associations with performance (reaction time measures. 3. Findings provide a rationale for applying NF protocols targeting theta (and alpha activity and the theta/beta ratio in subgroups of children with ADHD.

Short analysis of the increase of the EEG apportionment 2013; Kurzanalyse des Anstiegs der EEG-Umlage 2013

Energy Technology Data Exchange (ETDEWEB)

Loreck, Charlotte; Matthes, Felix C.; Hermann, Hauke; Jung, Frederieke; Emele, Lukas

2012-10-15

At 15th October, 2012 the transmission system operators had published the EEG apportionment (EEG - Energy Economy Law). For the year 2013. This apportionment amounts 5,277 ct/kWh for non-privileged consumers in comparison to 3,59 ct/kWh for the year 2012. The ongoing enhancement of the renewable energies increases the EEG apportionment by an amount of 0.74 ct/kWh. With 0.26 ct/kWh the photovoltaics has the largest proportion in comparison to photovoltaics. The power generation from biomass as well as from wind energy at onshore sites contribute with 0.21 ct/kWh to the EEG apportionment. The greatest item of 0.48 ct/kWh is the debit balancing of the EEG account. The liquidity reserve for the year 2013 will be enhanced to 10% of the budget deficit. The expansion of the privileged status of the power consumption increases the EEG apportionment by 0.12 ct/kWh.

Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification.

Science.gov (United States)

Ping-Keng Jao; Yuan-Pin Lin; Yi-Hsuan Yang; Tzyy-Ping Jung

2015-08-01

An emerging challenge for emotion classification using electroencephalography (EEG) is how to effectively alleviate day-to-day variability in raw data. This study employed the robust principal component analysis (RPCA) to address the problem with a posed hypothesis that background or emotion-irrelevant EEG perturbations lead to certain variability across days and somehow submerge emotion-related EEG dynamics. The empirical results of this study evidently validated our hypothesis and demonstrated the RPCA's feasibility through the analysis of a five-day dataset of 12 subjects. The RPCA allowed tackling the sparse emotion-relevant EEG dynamics from the accompanied background perturbations across days. Sequentially, leveraging the RPCA-purified EEG trials from more days appeared to improve the emotion-classification performance steadily, which was not found in the case using the raw EEG features. Therefore, incorporating the RPCA with existing emotion-aware machine-learning frameworks on a longitudinal dataset of each individual may shed light on the development of a robust affective brain-computer interface (ABCI) that can alleviate ecological inter-day variability.

A Study on Analysis of EEG Caused by Grating Stimulation Imaging

Science.gov (United States)

Urakawa, Hiroshi; Nishimura, Toshihiro; Tsubai, Masayoshi; Itoh, Kenji

Recently, many researchers have studied a visual perception. Focus is attended to studies of the visual perception phenomenon by using the grating stimulation images. The previous researches have suggested that a subset of retinal ganglion cells responds to motion in the receptive field center, but only if the wider surround moves with a different trajectory. We discuss the function of human retina, and measure and analysis EEG(electroencephalography) of a normal subject who looks on grating stimulation images. We confirmed the visual perception of human by EEG signal analysis. We also have obtained that a sinusoidal grating stimulation was given, asymmetry was observed the α wave element in EEG of the symmetric part in a left hemisphere and a right hemisphere of the brain. Therefore, it is presumed that projected image is even when the still picture is seen and the image projected onto retinas of right and left eyes is not even for the dynamic scene. It evaluated it by taking the envelope curve for the detected α wave, and using the average and standard deviation.

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.

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

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

2015-08-01

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

Matrix kernels for MEG and EEG source localization and imaging

International Nuclear Information System (INIS)

Mosher, J.C.; Lewis, P.S.; Leahy, R.M.

1994-01-01

The most widely used model for electroencephalography (EEG) and magnetoencephalography (MEG) assumes a quasi-static approximation of Maxwell's equations and a piecewise homogeneous conductor model. Both models contain an incremental field element that linearly relates an incremental source element (current dipole) to the field or voltage at a distant point. The explicit form of the field element is dependent on the head modeling assumptions and sensor configuration. Proper characterization of this incremental element is crucial to the inverse problem. The field element can be partitioned into the product of a vector dependent on sensor characteristics and a matrix kernel dependent only on head modeling assumptions. We present here the matrix kernels for the general boundary element model (BEM) and for MEG spherical models. We show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration. We then describe how this kernel is easily applied to ''gain'' or ''transfer'' matrices used in multiple dipole and source imaging models

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

Analysis and correction of ballistocardiogram contamination of EEG recordings in MR

International Nuclear Information System (INIS)

Jaeger, L.; Hoffmann, A.; Reiser, M.F.; Werhahn, K.J.

2005-01-01

Purpose: to examine the influence of cardiac activity-related head movements and varying blood pulse frequencies on the shape of electroencephalography (EEG) recordings in a high magnetic field, and to implement a post-processing technique to eliminate cardiac activity-related artifacts. Material and methods: respiratory thoracic movements, changes of blood pulse frequency and passive head movements to 20 healthy subjects were examined outside and inside an MR magnet at rest in a simultaneously recorded 21-channel surface EEG. An electrocardiogram (ECG) was recorded simultaneously. On the basis of the correlation of the left ventricular ejection time (LVET) with the heart-rate, a post-processing heart-rate dependent subtraction of the cardiac activity-related artifacts of the EEG was developed. The quality of the post-processed EEG was tested by detecting alpha-activity in the pre- and post-processed EEGs. Results: inside the magnet, passive head motion but not respiratory thoracic movements resulted in EEG artifacts that correlated strongly with cardiac activity-related artifacts of the EEG. The blood pulse frequency influenced the appearance of the cardiac activity-related artifacts of the EEG. The removal of the cardiac activity-related artifacts of the EEG by the implemented post-processing algorithm resulted in an EEG of diagnostic quality with detected alpha-activity. Conclusion: when recording an EEG in MR environment, heart rate-dependent subtraction of EEG artifacts caused by ballistocardiogram contamination is essential to obtain EEG recordings of diagnostic quality and reliability. (orig.)

The EEG 2017 in the overview

International Nuclear Information System (INIS)

Altrock, Martin; Vollprecht, Jens

2016-01-01

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

Multifractal Detrended Fluctuation Analysis of alpha and theta EEG rhythms with musical stimuli

International Nuclear Information System (INIS)

Maity, Akash Kumar; Pratihar, Ruchira; Mitra, Anubrato; Dey, Subham; Agrawal, Vishal; Sanyal, Shankha; Banerjee, Archi; Sengupta, Ranjan; Ghosh, Dipak

2015-01-01

Highlights: • EEG was done to record the brain electrical activity of 10 subjects in response to simple acoustical tanpura stimuli. • Empirical Mode Decomposition (EMD) technique used to make the EEG signal free from blink and other muscular artifacts. • Multifractal Detrended Fluctuation Analysis (MFDFA) performed to assess the complexity of extracted alpha and theta brain rhythms. • The findings show spectral width i.e. complexity of alpha and theta rhythms increase in all the seven frontal locations studied, under the effect of musical stimuli. - Abstract: Electroencephalography (EEG) was performed on 10 participants using a simple acoustical stimuli i.e. a tanpura drone. The tanpura drone is free from any semantic content and is used with a hypothesis that it provides a specific resting environment for the listeners. The EEG data was extracted for all the frontal electrodes viz. F3, F4, F7, F8, Fp1, Fp2 and Fz. Empirical Mode Decomposition (EMD) was applied on the acquired raw EEG signal to make it free from blink as well as other muscular artifacts. Wavelet Transform (WT) technique was used to segregate alpha and theta waves from the denoised EEG signal. Non-linear analysis in the form of Multifractal Detrended Fluctuation Analysis (MFDFA) was carried out on the extracted alpha and theta time series data to study the variation of their complexity. It was found that in all the frontal electrodes alpha as well as theta complexity increases as is evident from the increase of multifractal spectral width. This study is entirely new and gives interesting data regarding neural activation of the alpha and theta brain rhythms while listening to simple acoustical stimuli. The importance of this study lies in the context of emotion quantification using multifractal spectral width as a parameter as well as in the field of cognitive music therapy. The results are discussed in detail.

Time-varying bispectral analysis of visually evoked multi-channel EEG

Science.gov (United States)

Chandran, Vinod

2012-12-01

Theoretical foundations of higher order spectral analysis are revisited to examine the use of time-varying bicoherence on non-stationary signals using a classical short-time Fourier approach. A methodology is developed to apply this to evoked EEG responses where a stimulus-locked time reference is available. Short-time windowed ensembles of the response at the same offset from the reference are considered as ergodic cyclostationary processes within a non-stationary random process. Bicoherence can be estimated reliably with known levels at which it is significantly different from zero and can be tracked as a function of offset from the stimulus. When this methodology is applied to multi-channel EEG, it is possible to obtain information about phase synchronization at different regions of the brain as the neural response develops. The methodology is applied to analyze evoked EEG response to flash visual stimulii to the left and right eye separately. The EEG electrode array is segmented based on bicoherence evolution with time using the mean absolute difference as a measure of dissimilarity. Segment maps confirm the importance of the occipital region in visual processing and demonstrate a link between the frontal and occipital regions during the response. Maps are constructed using bicoherence at bifrequencies that include the alpha band frequency of 8Hz as well as 4 and 20Hz. Differences are observed between responses from the left eye and the right eye, and also between subjects. The methodology shows potential as a neurological functional imaging technique that can be further developed for diagnosis and monitoring using scalp EEG which is less invasive and less expensive than magnetic resonance imaging.

Continuous EEG signal analysis for asynchronous BCI application.

Science.gov (United States)

Hsu, Wei-Yen

2011-08-01

In this study, we propose a two-stage recognition system for continuous analysis of electroencephalogram (EEG) signals. An independent component analysis (ICA) and correlation coefficient are used to automatically eliminate the electrooculography (EOG) artifacts. Based on the continuous wavelet transform (CWT) and Student's two-sample t-statistics, active segment selection then detects the location of active segment in the time-frequency domain. Next, multiresolution fractal feature vectors (MFFVs) are extracted with the proposed modified fractal dimension from wavelet data. Finally, the support vector machine (SVM) is adopted for the robust classification of MFFVs. The EEG signals are continuously analyzed in 1-s segments, and every 0.5 second moves forward to simulate asynchronous BCI works in the two-stage recognition architecture. The segment is first recognized as lifted or not in the first stage, and then is classified as left or right finger lifting at stage two if the segment is recognized as lifting in the first stage. Several statistical analyses are used to evaluate the performance of the proposed system. The results indicate that it is a promising system in the applications of asynchronous BCI work.

Propofol Anesthesia and Sleep: A High-Density EEG Study

Science.gov (United States)

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

2011-01-01

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

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

Science.gov (United States)

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

2012-12-01

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

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

Science.gov (United States)

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

2016-08-30

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

Quantitative topographic differentiation of the neonatal EEG.

Science.gov (United States)

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

2006-09-01

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

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

Science.gov (United States)

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

2018-05-01

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

The Discontinuous Galerkin Finite Element Method for Solving the MEG and the Combined MEG/EEG Forward Problem

Directory of Open Access Journals (Sweden)

Maria Carla Piastra

2018-02-01

Full Text Available In Electro- (EEG and Magnetoencephalography (MEG, one important requirement of source reconstruction is the forward model. The continuous Galerkin finite element method (CG-FEM has become one of the dominant approaches for solving the forward problem over the last decades. Recently, a discontinuous Galerkin FEM (DG-FEM EEG forward approach has been proposed as an alternative to CG-FEM (Engwer et al., 2017. It was shown that DG-FEM preserves the property of conservation of charge and that it can, in certain situations such as the so-called skull leakages, be superior to the standard CG-FEM approach. In this paper, we developed, implemented, and evaluated two DG-FEM approaches for the MEG forward problem, namely a conservative and a non-conservative one. The subtraction approach was used as source model. The validation and evaluation work was done in statistical investigations in multi-layer homogeneous sphere models, where an analytic solution exists, and in a six-compartment realistically shaped head volume conductor model. In agreement with the theory, the conservative DG-FEM approach was found to be superior to the non-conservative DG-FEM implementation. This approach also showed convergence with increasing resolution of the hexahedral meshes. While in the EEG case, in presence of skull leakages, DG-FEM outperformed CG-FEM, in MEG, DG-FEM achieved similar numerical errors as the CG-FEM approach, i.e., skull leakages do not play a role for the MEG modality. In particular, for the finest mesh resolution of 1 mm sources with a distance of 1.59 mm from the brain-CSF surface, DG-FEM yielded mean topographical errors (relative difference measure, RDM% of 1.5% and mean magnitude errors (MAG% of 0.1% for the magnetic field. However, if the goal is a combined source analysis of EEG and MEG data, then it is highly desirable to employ the same forward model for both EEG and MEG data. Based on these results, we conclude that the newly presented

The Discontinuous Galerkin Finite Element Method for Solving the MEG and the Combined MEG/EEG Forward Problem.

Science.gov (United States)

Piastra, Maria Carla; Nüßing, Andreas; Vorwerk, Johannes; Bornfleth, Harald; Oostenveld, Robert; Engwer, Christian; Wolters, Carsten H

2018-01-01

In Electro- (EEG) and Magnetoencephalography (MEG), one important requirement of source reconstruction is the forward model. The continuous Galerkin finite element method (CG-FEM) has become one of the dominant approaches for solving the forward problem over the last decades. Recently, a discontinuous Galerkin FEM (DG-FEM) EEG forward approach has been proposed as an alternative to CG-FEM (Engwer et al., 2017). It was shown that DG-FEM preserves the property of conservation of charge and that it can, in certain situations such as the so-called skull leakages , be superior to the standard CG-FEM approach. In this paper, we developed, implemented, and evaluated two DG-FEM approaches for the MEG forward problem, namely a conservative and a non-conservative one. The subtraction approach was used as source model. The validation and evaluation work was done in statistical investigations in multi-layer homogeneous sphere models, where an analytic solution exists, and in a six-compartment realistically shaped head volume conductor model. In agreement with the theory, the conservative DG-FEM approach was found to be superior to the non-conservative DG-FEM implementation. This approach also showed convergence with increasing resolution of the hexahedral meshes. While in the EEG case, in presence of skull leakages, DG-FEM outperformed CG-FEM, in MEG, DG-FEM achieved similar numerical errors as the CG-FEM approach, i.e., skull leakages do not play a role for the MEG modality. In particular, for the finest mesh resolution of 1 mm sources with a distance of 1.59 mm from the brain-CSF surface, DG-FEM yielded mean topographical errors (relative difference measure, RDM%) of 1.5% and mean magnitude errors (MAG%) of 0.1% for the magnetic field. However, if the goal is a combined source analysis of EEG and MEG data, then it is highly desirable to employ the same forward model for both EEG and MEG data. Based on these results, we conclude that the newly presented conservative DG

Convolutive ICA for Spatio-Temporal Analysis of EEG

DEFF Research Database (Denmark)

Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai

2007-01-01

in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....

Extended seizure detection algorithm for intracranial EEG recordings

DEFF Research Database (Denmark)

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

2010-01-01

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

Assessment of preconscious sucrose perception using EEG

DEFF Research Database (Denmark)

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

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

Recognizing emotions from EEG subbands using wavelet analysis.

Science.gov (United States)

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

2015-01-01

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

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

Probabilistic forward model for electroencephalography source analysis

International Nuclear Information System (INIS)

Plis, Sergey M; George, John S; Jun, Sung C; Ranken, Doug M; Volegov, Petr L; Schmidt, David M

2007-01-01

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer-Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates

Functional community analysis of brain: a new approach for EEG-based investigation of the brain pathology.

Science.gov (United States)

Ahmadlou, Mehran; Adeli, Hojjat

2011-09-15

Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs. Copyright © 2011. Published by Elsevier Inc.

Diagnostic Accuracy of microEEG: A Miniature, Wireless EEG Device

OpenAIRE

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

2014-01-01

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

Analysis of routine EEG usage in a general adult ICU.

LENUS (Irish Health Repository)

McHugh, J C

2009-09-01

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

Dynamic Principal Component Analysis with Nonoverlapping Moving Window and Its Applications to Epileptic EEG Classification

Directory of Open Access Journals (Sweden)

Shengkun Xie

2014-01-01

Full Text Available Classification of electroencephalography (EEG is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

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

International Nuclear Information System (INIS)

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

2009-01-01

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

The PREP Pipeline: Standardized preprocessing for large-scale EEG analysis

Directory of Open Access Journals (Sweden)

Nima eBigdelys Shamlo

2015-06-01

Full Text Available The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode/.

The PREP pipeline: standardized preprocessing for large-scale EEG analysis.

Science.gov (United States)

Bigdely-Shamlo, Nima; Mullen, Tim; Kothe, Christian; Su, Kyung-Min; Robbins, Kay A

2015-01-01

The technology to collect brain imaging and physiological measures has become portable and ubiquitous, opening the possibility of large-scale analysis of real-world human imaging. By its nature, such data is large and complex, making automated processing essential. This paper shows how lack of attention to the very early stages of an EEG preprocessing pipeline can reduce the signal-to-noise ratio and introduce unwanted artifacts into the data, particularly for computations done in single precision. We demonstrate that ordinary average referencing improves the signal-to-noise ratio, but that noisy channels can contaminate the results. We also show that identification of noisy channels depends on the reference and examine the complex interaction of filtering, noisy channel identification, and referencing. We introduce a multi-stage robust referencing scheme to deal with the noisy channel-reference interaction. We propose a standardized early-stage EEG processing pipeline (PREP) and discuss the application of the pipeline to more than 600 EEG datasets. The pipeline includes an automatically generated report for each dataset processed. Users can download the PREP pipeline as a freely available MATLAB library from http://eegstudy.org/prepcode.

EEG/MEG Source Reconstruction with Spatial-Temporal Two-Way Regularized Regression

KAUST Repository

Tian, Tian Siva

2013-07-11

In this work, we propose a spatial-temporal two-way regularized regression method for reconstructing neural source signals from EEG/MEG time course measurements. The proposed method estimates the dipole locations and amplitudes simultaneously through minimizing a single penalized least squares criterion. The novelty of our methodology is the simultaneous consideration of three desirable properties of the reconstructed source signals, that is, spatial focality, spatial smoothness, and temporal smoothness. The desirable properties are achieved by using three separate penalty functions in the penalized regression framework. Specifically, we impose a roughness penalty in the temporal domain for temporal smoothness, and a sparsity-inducing penalty and a graph Laplacian penalty in the spatial domain for spatial focality and smoothness. We develop a computational efficient multilevel block coordinate descent algorithm to implement the method. Using a simulation study with several settings of different spatial complexity and two real MEG examples, we show that the proposed method outperforms existing methods that use only a subset of the three penalty functions. © 2013 Springer Science+Business Media New York.

Hemodynamic Response to Interictal Epileptiform Discharges Addressed by Personalized EEG-fNIRS Recordings

Science.gov (United States)

Pellegrino, Giovanni; Machado, Alexis; von Ellenrieder, Nicolas; Watanabe, Satsuki; Hall, Jeffery A.; Lina, Jean-Marc; Kobayashi, Eliane; Grova, Christophe

2016-01-01

Objective: We aimed at studying the hemodynamic response (HR) to Interictal Epileptic Discharges (IEDs) using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG) and functional Near InfraRed Spectroscopy (fNIRS) recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF) and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (seven patients), followed by oxy-hemoglobin decreases (six patients). HR was lateralized in six patients and lasted from 8.5 to 30 s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result). The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30 s. Conclusions: (i) EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; (ii) cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function (iii) the HR is often bilateral and lasts up to 30 s. PMID:27047325

Hemodynamic response to Interictal Epileptiform Discharges addressed by personalized EEG-fNIRS recordings

Directory of Open Access Journals (Sweden)

Giovanni ePellegrino

2016-03-01

Full Text Available Objective: We aimed at studying the hemodynamic response (HR to Interictal Epileptic Discharges (IEDs using patient-specific and prolonged simultaneous ElectroEncephaloGraphy (EEG and functional Near InfraRed Spectroscopy (fNIRS recordings. Methods: The epileptic generator was localized using Magnetoencephalography source imaging. fNIRS montage was tailored for each patient, using an algorithm to optimize the sensitivity to the epileptic generator. Optodes were glued using collodion to achieve prolonged acquisition with high quality signal. fNIRS data analysis was handled with no a priori constraint on HR time course, averaging fNIRS signals to similar IEDs. Cluster-permutation analysis was performed on 3D reconstructed fNIRS data to identify significant spatio-temporal HR clusters. Standard (GLM with fixed HRF and cluster-permutation EEG-fMRI analyses were performed for comparison purposes. Results: fNIRS detected HR to IEDs for 8/9 patients. It mainly consisted oxy-hemoglobin increases (7 patients, followed by oxy-hemoglobin decreases (6 patients. HR was lateralized in 6 patients and lasted from 8.5 to 30s. Standard EEG-fMRI analysis detected an HR in 4/9 patients (4/9 without enough IEDs, 1/9 unreliable result. The cluster-permutation EEG-fMRI analysis restricted to the region investigated by fNIRS showed additional strong and non-canonical BOLD responses starting earlier than the IEDs and lasting up to 30s. Conclusions: i EEG-fNIRS is suitable to detect the HR to IEDs and can outperform EEG-fMRI because of prolonged recordings and greater chance to detect IEDs; ii cluster-permutation analysis unveils additional HR features underestimated when imposing a canonical HR function iii the HR is often bilateral and lasts up to 30s.

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

Science.gov (United States)

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

DEFF Research Database (Denmark)

Hansen, Sofie Therese; Hansen, Lars Kai

2016-01-01

the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging......Electroencephalography (EEG) can capture brain dynamics in high temporal resolution. By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding...

ERPLAB: An Open-Source Toolbox for the Analysis of Event-Related Potentials

Directory of Open Access Journals (Sweden)

Javier eLopez-Calderon

2014-04-01

Full Text Available ERPLAB Toolbox is a freely available, open-source toolbox for processing and analyzing event-related potential (ERP data in the MATLAB environment. ERPLAB is closely integrated with EEGLAB, a popular open-source toolbox that provides many EEG preprocessing steps and an excellent user interface design. ERPLAB adds to EEGLAB’s EEG processing functions, providing additional tools for filtering, artifact detection, re-referencing, and sorting of events, among others. ERPLAB also provides robust tools for averaging EEG segments together to create averaged ERPs, for creating difference waves and other recombinations of ERP waveforms through algebraic expressions, for filtering and re-referencing the averaged ERPs, for plotting ERP waveforms and scalp maps, and for quantifying several types of amplitudes and latencies. ERPLAB’s tools can be accessed either from an easy-to-learn graphical user interface or from MATLAB scripts, and a command history function makes it easy for users with no programming experience to write scripts. Consequently, ERPLAB provides both ease of use and virtually unlimited power and flexibility, making it appropriate for the analysis of both simple and complex ERP experiments. Several forms of documentation are available, including a detailed user’s guide, a step-by-step tutorial, a scripting guide, and a set of video-based demonstrations.

Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications

Science.gov (United States)

2013-01-01

Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109

A New Approach to Eliminate High Amplitude Artifacts in EEG Signals

Directory of Open Access Journals (Sweden)

Ana Rita Teixeira

2016-09-01

Full Text Available High amplitude artifacts represent a problem during EEG recordings in neuroscience research. Taking this into account, this paper proposes a method to identify high amplitude artifacts with no requirement for visual inspection, electrooscillogram (EOG reference channel or user assigned parameters. A potential solution to the high amplitude artifacts (HAA elimination is presented based on blind source separation methods. The assumption underlying the selection of components is that HAA are independent of the EEG signal and different HAA can be generated during the EEG recordings. Therefore, the number of components related to HAA is variable and depends on the processed signal, which means that the method is adaptable to the input signal. The results show, when removing the HAA artifacts, the delta band is distorted but all the other frequency bands are preserved. A case study with EEG signals recorded while participants performed on the Halstead Category Test (HCT is presented. After HAA removal, data analysis revealed, as expected, an error-related frontal ERP wave: the feedback-related negativity (FRN in response to feedback stimuli.

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Science.gov (United States)

Wang, Deng; Miao, Duoqian; Blohm, Gunnar

2012-01-01

Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications. PMID:23087607

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

Directory of Open Access Journals (Sweden)

Chung-Yeon Lee

2012-10-01

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

The Realistic Versus the Spherical Head Model in EEG Dipole Source Analysis in the Presence of Noise

National Research Council Canada - National Science Library

Vanrumste, Bart

2001-01-01

.... For 27 electrodes, an EEG epoch of one time sample and spatially white Gaussian noise we found that the importance of the realistic head model over the spherical head model reduces by increasing the noise level.

The five percent electrode system for high-resolution EEG and ERP measurements.

NARCIS (Netherlands)

Oostenveld, R.; Praamstra, P.

2001-01-01

OBJECTIVE: A system for electrode placement is described. It is designed for studies on topography and source analysis of spontaneous and evoked EEG activity. METHOD: The proposed system is based on the extended International 10-20 system which contains 74 electrodes, and extends this system up to

The effects of the German renewable energy sources Act (EEG) on market, technical and industrial development

International Nuclear Information System (INIS)

Stryi-Hipp, G.

2004-01-01

Since 1999, the German photovoltaic market has been growing rapidly. In 2003, more than 130 MWp PV systems were installed in Germany; in 2004, more than 200 MWp are expected to be installed additionally. The main reason for this impressive market success has been the market stimulation policy of the German government. In January 1999, the 100.000 Roofs Programme for photovoltaic systems had started, offering interest-reduced loans for PV systems. In April 2000, it was backed by the Renewable Energy Sources Act (EEG), which considerably increased the feed-in tariff to 99 Pfennig (0.51 euros) per kWh. Since then, the PV market has boomed. In 2003, the 100,000 Roofs Programme (HTRP) ended successfully, after loans for more than 300 MWp PV systems had been granted. In order to fill the gap created by the termination of the programme, the government decided to increase the feed-in tariff provided by the EEG. Since January 2004, grid-connected PV systems on roofs and facades receive a tariff between 0,540 euros and 0,624 euros per kWh over 20 years. This improvement of the EEG further increased the interest in photovoltaics. The growth rate of the photovoltaic market, exceeding 50% in 2004, has led to the demand being higher than the available supply of PV systems. (author)

EEG. Renewables Act. Comment. 4. new rev. and enl. ed.

International Nuclear Information System (INIS)

Frenz, Walter; Cosack, Tilman

2015-01-01

Unlike any other Act, the Renewable Energy Sources Act (EEG) changes continuously. Recently it has been fundamentally transformed with the amendment 2014. Comprehensive, readable and practice-oriented. The proven Berliner comment EEG is your reliable companion through the new regulatory regime. All provisions of the EEG 2014 thorough and easy to understand commented by experts of the matter. 2. The EEG Amending Act of 29.6.2015 has already been considered. A detailed introduction and contributions to the relevant European law and the antitrust aspects of the renewable energy sources to guarantee you a broad understanding of the rules. Valuable background information you provide, the digressions of the most important renewable energy technologies, will explain the pictures thanks to numerous the scientific and technical foundations. Moreover you the construction law aspects in the construction of photovoltaic and wind turbines are explained clearly. [de

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

Science.gov (United States)

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

Determination of head conductivity frequency response in vivo with optimized EIT-EEG.

Science.gov (United States)

Dabek, Juhani; Kalogianni, Konstantina; Rotgans, Edwin; van der Helm, Frans C T; Kwakkel, Gert; van Wegen, Erwin E H; Daffertshofer, Andreas; de Munck, Jan C

2016-02-15

Electroencephalography (EEG) benefits from accurate head models. Dipole source modelling errors can be reduced from over 1cm to a few millimetres by replacing generic head geometry and conductivity with tailored ones. When adequate head geometry is available, electrical impedance tomography (EIT) can be used to infer the conductivities of head tissues. In this study, the boundary element method (BEM) is applied with three-compartment (scalp, skull and brain) subject-specific head models. The optimal injection of small currents to the head with a modular EIT current injector, and voltage measurement by an EEG amplifier is first sought by simulations. The measurement with a 64-electrode EEG layout is studied with respect to three noise sources affecting EIT: background EEG, deviations from the fitting assumption of equal scalp and brain conductivities, and smooth model geometry deviations from the true head geometry. The noise source effects were investigated depending on the positioning of the injection and extraction electrode and the number of their combinations used sequentially. The deviation from equal scalp and brain conductivities produces rather deterministic errors in the three conductivities irrespective of the current injection locations. With a realistic measurement of around 2 min and around 8 distant distinct current injection pairs, the error from the other noise sources is reduced to around 10% or less in the skull conductivity. The analysis of subsequent real measurements, however, suggests that there could be subject-specific local thinnings in the skull, which could amplify the conductivity fitting errors. With proper analysis of multiplexed sinusoidal EIT current injections, the measurements on average yielded conductivities of 340 mS/m (scalp and brain) and 6.6 mS/m (skull) at 2 Hz. From 11 to 127 Hz, the conductivities increased by 1.6% (scalp and brain) and 6.7% (skull) on the average. The proper analysis was ensured by using recombination of

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

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 (pEEG has potential utility as a biomarker in ALS.

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

Science.gov (United States)

Ying, Xiaoguo; Lin, Han; Hui, Guohua

2015-01-01

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

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

Science.gov (United States)

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

2017-10-01

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

Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation

Directory of Open Access Journals (Sweden)

Danny Eytan

2016-01-01

Full Text Available Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory, and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage

German legislation for promotion of renewable energies 2014. Act on feed-in and guaranteed pricing of electricity from renewable energy sources (EEG). Commentary. 7. new rev. ed.

International Nuclear Information System (INIS)

Salje, Peter

2015-01-01

The EEG is continuously subjected to changes and the permanent center of political discussions. Therefore now already the 7th edition of the EEG comment of the science and practice well recognized and highly recognized author Prof. Dr. Dr. Peter Salje appear. The revision includes, inter alia, further evaluation of the law of electric power produced from renewable energy sources from the EEG of 2012 and the regulations made there under (including Biomass and AusglMechV). The changes associated with the so-called Photovoltaic amendment dated August 2012 and the recent case law since the publication of the previous edition handed down are considered. In particular, the incorporation of the full basic EEG reform in 2014 guarantees the highest topicality. [de

Altered resting state EEG in chronic pancreatitis patients: toward a marker for chronic pain

NARCIS (Netherlands)

Vries, M. de; Wilder-Smith, O.H.G.; Jongsma, M.L.A.; Broeke, E.N. van den; Arns, M.W.; Goor, H. van; Rijn, C.M. van

2013-01-01

OBJECTIVES: Electroencephalography (EEG) may be a promising source of physiological biomarkers accompanying chronic pain. Several studies in patients with chronic neuropathic pain have reported alterations in central pain processing, manifested as slowed EEG rhythmicity and increased EEG power in

Altered resting state EEG in chronic pancreatitis patients: toward a marker for chronic pain

NARCIS (Netherlands)

Vries, M. de; Wilder-Smith, O.H.G.; Jongsma, M.L.A.; Broeke, E.N. van den; Arns, M.W.; Goor, H. van; Rijn, C.M. van

2013-01-01

Objectives: Electroencephalography (EEG) may be a promising source of physiological biomarkers accompanying chronic pain. Several studies in patients with chronic neuropathic pain have reported alterations in central pain processing, manifested as slowed EEG rhythmicity and increased EEG power in

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

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Xu Lei

Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

Combined analysis of cortical (EEG) and nerve stump signals improves robotic hand control.

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Tombini, Mario; Rigosa, Jacopo; Zappasodi, Filippo; Porcaro, Camillo; Citi, Luca; Carpaneto, Jacopo; Rossini, Paolo Maria; Micera, Silvestro

2012-01-01

Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (α/β band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored α band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and

Involvement of the anterior cingulate cortex in time-based prospective memory task monitoring: An EEG analysis of brain sources using Independent Component and Measure Projection Analysis.

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Gabriela Cruz

Full Text Available Time-based prospective memory (PM, remembering to do something at a particular moment in the future, is considered to depend upon self-initiated strategic monitoring, involving a retrieval mode (sustained maintenance of the intention plus target checking (intermittent time checks. The present experiment was designed to explore what brain regions and brain activity are associated with these components of strategic monitoring in time-based PM tasks.24 participants were asked to reset a clock every four minutes, while performing a foreground ongoing word categorisation task. EEG activity was recorded and data were decomposed into source-resolved activity using Independent Component Analysis. Common brain regions across participants, associated with retrieval mode and target checking, were found using Measure Projection Analysis.Participants decreased their performance on the ongoing task when concurrently performed with the time-based PM task, reflecting an active retrieval mode that relied on withdrawal of limited resources from the ongoing task. Brain activity, with its source in or near the anterior cingulate cortex (ACC, showed changes associated with an active retrieval mode including greater negative ERP deflections, decreased theta synchronization, and increased alpha suppression for events locked to the ongoing task while maintaining a time-based intention. Activity in the ACC was also associated with time-checks and found consistently across participants; however, we did not find an association with time perception processing per se.The involvement of the ACC in both aspects of time-based PM monitoring may be related to different functions that have been attributed to it: strategic control of attention during the retrieval mode (distributing attentional resources between the ongoing task and the time-based task and anticipatory/decision making processing associated with clock-checks.

Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog

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2013-01-01

Background Matching pursuit algorithm (MP), especially with recent multivariate extensions, offers unique advantages in analysis of EEG and MEG. Methods We propose a novel construction of an optimal Gabor dictionary, based upon the metrics introduced in this paper. We implement this construction in a freely available software for MP decomposition of multivariate time series, with a user friendly interface via the Svarog package (Signal Viewer, Analyzer and Recorder On GPL, http://braintech.pl/svarog), and provide a hands-on introduction to its application to EEG. Finally, we describe numerical and mathematical optimizations used in this implementation. Results Optimal Gabor dictionaries, based on the metric introduced in this paper, for the first time allowed for a priori assessment of maximum one-step error of the MP algorithm. Variants of multivariate MP, implemented in the accompanying software, are organized according to the mathematical properties of the algorithms, relevant in the light of EEG/MEG analysis. Some of these variants have been successfully applied to both multichannel and multitrial EEG and MEG in previous studies, improving preprocessing for EEG/MEG inverse solutions and parameterization of evoked potentials in single trials; we mention also ongoing work and possible novel applications. Conclusions Mathematical results presented in this paper improve our understanding of the basics of the MP algorithm. Simple introduction of its properties and advantages, together with the accompanying stable and user-friendly Open Source software package, pave the way for a widespread and reproducible analysis of multivariate EEG and MEG time series and novel applications, while retaining a high degree of compatibility with the traditional, visual analysis of EEG. PMID:24059247

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.

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

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

2014-10-01

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

Analysis of tractable distortion metrics for EEG compression applications

International Nuclear Information System (INIS)

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

2012-01-01

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

Time frequency analysis of olfactory induced EEG-power change.

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Valentin Alexander Schriever

Full Text Available The objective of the present study was to investigate the usefulness of time-frequency analysis (TFA of olfactory-induced EEG change with a low-cost, portable olfactometer in the clinical investigation of smell function.A total of 78 volunteers participated. The study was composed of three parts where olfactory stimuli were presented using a custom-built olfactometer. Part I was designed to optimize the stimulus as well as the recording conditions. In part II EEG-power changes after olfactory/trigeminal stimulation were compared between healthy participants and patients with olfactory impairment. In Part III the test-retest reliability of the method was evaluated in healthy subjects.Part I indicated that the most effective paradigm for stimulus presentation was cued stimulus, with an interstimulus interval of 18-20s at a stimulus duration of 1000ms with each stimulus quality presented 60 times in blocks of 20 stimuli each. In Part II we found that central processing of olfactory stimuli analyzed by TFA differed significantly between healthy controls and patients even when controlling for age. It was possible to reliably distinguish patients with olfactory impairment from healthy individuals at a high degree of accuracy (healthy controls vs anosmic patients: sensitivity 75%; specificity 89%. In addition we could show a good test-retest reliability of TFA of chemosensory induced EEG-power changes in Part III.Central processing of olfactory stimuli analyzed by TFA reliably distinguishes patients with olfactory impairment from healthy individuals at a high degree of accuracy. Importantly this can be achieved with a simple olfactometer.

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

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Tuter, N V; Gnezditskiĭ, V V

2008-01-01

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

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

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

2017-08-15

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

Standardized Computer-based Organized Reporting of EEG: SCORE

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

2013-01-01

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

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

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

2018-03-01

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

Solving of L0 norm constrained EEG inverse problem.

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

2009-01-01

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

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

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

2017-03-01

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

Investigating social cognition in infants and adults using dense array electroencephalography ((d)EEG).

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Akano, Adekemi J; Haley, David W; Dudek, Joanna

2011-06-27

Dense array electroencephalography ((d)EEG), which provides a non-invasive window for measuring brain activity and a temporal resolution unsurpassed by any other current brain imaging technology¹, ² is being used increasingly in the study of social cognitive functioning in infants and adults. While (d)EEG is enabling researchers to examine brain activity patterns with unprecedented levels of sensitivity, conventional EEG recording systems continue to face certain limitations, including 1) poor spatial resolution and source localization³,⁴2) the physical discomfort for test subjects of enduring the individual application of numerous electrodes to the surface of the scalp, and 3) the complexity for researchers of learning to use multiple software packages to collect and process data. Here we present an overview of an established methodology that represents a significant improvement on conventional methodologies for studying EEG in infants and adults. Although several analytical software techniques can be used to establish indirect indices of source localization to improve the spatial resolution of (d)EEG, the HydroCel Geodesic Sensor Net (HCGSN) by Electrical Geodesics, Inc. (EGI), a dense sensory array that maintains equal distances among adjacent recording electrodes on all surfaces of the scalp, further enhances spatial resolution⁴,⁵(,)⁶ compared to standard (d)EEG systems. The sponge-based HCGSN can be applied rapidly and without scalp abrasion, making it ideal for use with adults⁷,⁸ children⁹,¹⁰, ¹¹,¹² and infants¹², in both research and clinical ⁴,⁵,⁶,¹³,¹⁴,¹⁵settings. This feature allows for considerable cost and time savings by decreasing the average net application time compared to other (d)EEG systems. Moreover, the HCGSN includes unified, seamless software applications for all phases of data, greatly simplifying the collection, processing, and analysis of (d)EEG data. The HCGSN features a low-profile electrode

Analysis of Small Muscle Movement Effects on EEG Signals

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2016-12-22

different conditions are recorded in this experiment. These conditions are the resting state, left finger keyboard press, right finger keyboard...51 4.3.2. Right and Left Finger Keyboard Press Conditions ..................................... 57 4.4. Detection of Hand...solving Gamma 30 Hz and higher Blending of multiple brain functions ; Muscle related artifacts 2.2. EEG Artifacts EEG recordings are intended to

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

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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

Artifact removal from EEG signals using adaptive filters in cascade

Science.gov (United States)

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

2007-11-01

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

Artifact removal from EEG signals using adaptive filters in cascade

International Nuclear Information System (INIS)

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

2007-01-01

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

Artifact removal from EEG signals using adaptive filters in cascade

Energy Technology Data Exchange (ETDEWEB)

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

2007-11-15

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

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

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

2015-01-22

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

On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.

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Schindler, Kaspar; Gast, Heidemarie; Goodfellow, Marc; Rummel, Christian

2012-09-01

identified seizure-onset zone. When compared to the bit strings derived from other EEG signals, their M was relatively smaller. These findings are consistent with a strong but transient occurrence of information-poor, that is, redundant electrical brain activity on a smaller spatial scale, which is particularly pronounced in the seizure-onset zone. On a larger spatial scale, a progressively more collective state emerges, as revealed by increasing amounts of mutual information. Information theoretical analysis of bit patterns derived from EEG signals helps to characterize periictal brain activity on different spatial scales in a quantitative and efficient way and may provide clinically relevant results. Wiley Periodicals, Inc. © 2012 International League Against Epilepsy.

Higher-Order Spectrum in Understanding Nonlinearity in EEG Rhythms

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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.

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

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

2017-05-15

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

Seizure-Onset Mapping Based on Time-Variant Multivariate Functional Connectivity Analysis of High-Dimensional Intracranial EEG: A Kalman Filter Approach.

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Lie, Octavian V; van Mierlo, Pieter

2017-01-01

The visual interpretation of intracranial EEG (iEEG) is the standard method used in complex epilepsy surgery cases to map the regions of seizure onset targeted for resection. Still, visual iEEG analysis is labor-intensive and biased due to interpreter dependency. Multivariate parametric functional connectivity measures using adaptive autoregressive (AR) modeling of the iEEG signals based on the Kalman filter algorithm have been used successfully to localize the electrographic seizure onsets. Due to their high computational cost, these methods have been applied to a limited number of iEEG time-series (Kalman filter implementations, a well-known multivariate adaptive AR model (Arnold et al. 1998) and a simplified, computationally efficient derivation of it, for their potential application to connectivity analysis of high-dimensional (up to 192 channels) iEEG data. When used on simulated seizures together with a multivariate connectivity estimator, the partial directed coherence, the two AR models were compared for their ability to reconstitute the designed seizure signal connections from noisy data. Next, focal seizures from iEEG recordings (73-113 channels) in three patients rendered seizure-free after surgery were mapped with the outdegree, a graph-theory index of outward directed connectivity. Simulation results indicated high levels of mapping accuracy for the two models in the presence of low-to-moderate noise cross-correlation. Accordingly, both AR models correctly mapped the real seizure onset to the resection volume. This study supports the possibility of conducting fully data-driven multivariate connectivity estimations on high-dimensional iEEG datasets using the Kalman filter approach.

Brain order disorder 2nd group report of f-EEG

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

2014-05-01

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

Volume conduction effects on wavelet cross-bicoherence analysis

International Nuclear Information System (INIS)

Memon, I.A.; Channa, C.

2013-01-01

Cross-bicoherence analysis is one of the important nonlinear signal processing tools which is used to measure quadratic phase coupling between frequencies of two different time series. It is frequently used in the diagnosis of various cognitive and neurological disorders in EEG (Electroencephalography) analysis. Volume conduction effects of various uncorrelated sources present in the brain can produce biased estimates into the estimated values of cross-bicoherence function. Previous studies have discussed volume conduction effects on coherence function which is used to measure linear relationship between EEG signals in terms of their phase and amplitude. However, volume conduction effect on cross-bicoherence analysis which is quite a different technique has not been investigated up to now to the best of our knowledge. This study is divided into two major parts, the first part deals with the investigation of VCUS (Volume Conduction effects due to Uncorrelated Sources) characteristics on EEG-cross-bicoherence analysis. The simulated EEG data due to uncorrelated sources present in the brain was used in this part of study. The next part of study is based upon investigating the effects of VCUS on the statistical analysis of results of EEG-based cross-bicoherence analysis. The study provides an important clinical application because most of studies based on EEG cross-bicoherence analysis have avoided the issue of VCUS. The cross-bicoherence analysis was performed by detecting the change in MSCB (Magnitude Square Cross-Bicoherence Function) between EEG activities of change detection and no-change detection trials. The real EEG signals were used. (author)

Effect of Sertraline on Current-Source Distribution of the High Beta Frequency Band: Analysis of Electroencephalography under Audiovisual Erotic Stimuli in Healthy, Right-Handed Males.

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Lee, Seung Hyun; Hyun, Jae Seog; Kwon, Oh-Young

2010-08-01

The purpose of this study was to examine the cerebral changes in high beta frequency oscillations (22-30 Hz) induced by sertraline and by audiovisual erotic stimuli in healthy adult males. Scalp electroencephalographies (EEGs) were conducted twice in 11 healthy, right-handed males, once before sertraline intake and again 4 hours thereafter. The EEGs included four sessions recorded sequentially while the subjects were resting, watching a music video, resting, and watching an erotic video for 3 minutes, 5 minutes, 3 minutes, and 5 minutes, respectively. We performed frequency-domain analysis using the EEGs with a distributed model of current-source analysis. The statistical nonparametric maps were obtained from the sessions of watching erotic and music videos (perotic stimuli decreased the current-source density of the high beta frequency band in the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in the baseline EEGs taken before sertraline intake (perotic stimuli did not induce any changes in current-source distribution of the brain 4 hours after sertraline intake. It is speculated that erotic stimuli may decrease the function of the middle frontal gyrus, the precentral gyrus, the postcentral gyrus, and the supramarginal gyrus of the left cerebral hemisphere in healthy adult males. This change may debase the inhibitory control of the brain against erotic stimuli. Sertraline may reduce the decrement in inhibitory control.

Assessing a novel polymer-wick based electrode for EEG neurophysiological research.

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Pasion, Rita; Paiva, Tiago O; Pedrosa, Paulo; Gaspar, Hugo; Vasconcelos, Beatriz; Martins, Ana C; Amaral, Maria H; Nóbrega, João M; Páscoa, Ricardo; Fonseca, Carlos; Barbosa, Fernando

2016-07-15

The EEG technique has decades of valid applications in clinical and experimental neurophysiology. EEG equipment and data analysis methods have been characterized by remarkable developments, but the skin-to-electrode signal transfer remains a challenge for EEG recording. A novel quasi-dry system - the polymer wick-based electrode - was developed to overcome the limitations of conventional dry and wet silver/silver-chloride (Ag/AgCl) electrodes for EEG recording. Nine participants completed an auditory oddball protocol with simultaneous EEG acquisition using both the conventional Ag/AgCl and the wick electrodes. Wick system successfully recorded the expected P300 modulation. Standard ERP analysis, residual random noise analysis, and single-trial analysis of the P300 wave were performed in order to compare signal acquired by both electrodes. It was found that the novel wick electrode performed similarly to the conventional Ag/AgCl electrodes. The developed wick electrode appears to be a reliable alternative for EEG research, representing a promising halfway alternative between wet and dry electrodes. Copyright © 2016 Elsevier B.V. All rights reserved.

Deep Neural Architectures for Mapping Scalp to Intracranial EEG.

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

2018-03-19

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

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

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

2014-08-01

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

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

OpenAIRE

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

2014-01-01

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

Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.

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Jalili, Mahdi; Lavoie, Suzie; Deppen, Patricia; Meuli, Reto; Do, Kim Q; Cuénod, Michel; Hasler, Martin; De Feo, Oscar; Knyazeva, Maria G

2007-10-24

The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals. To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels) EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity. The new method of

Dysconnection topography in schizophrenia revealed with state-space analysis of EEG.

Directory of Open Access Journals (Sweden)

Mahdi Jalili

2007-10-01

Full Text Available The dysconnection hypothesis has been proposed to account for pathophysiological mechanisms underlying schizophrenia. Widespread structural changes suggesting abnormal connectivity in schizophrenia have been imaged. A functional counterpart of the structural maps would be the EEG synchronization maps. However, due to the limits of currently used bivariate methods, functional correlates of dysconnection are limited to the isolated measurements of synchronization between preselected pairs of EEG signals.To reveal a whole-head synchronization topography in schizophrenia, we applied a new method of multivariate synchronization analysis called S-estimator to the resting dense-array (128 channels EEG obtained from 14 patients and 14 controls. This method determines synchronization from the embedding dimension in a state-space domain based on the theoretical consequence of the cooperative behavior of simultaneous time series-the shrinking of the state-space embedding dimension. The S-estimator imaging revealed a specific synchronization landscape in schizophrenia patients. Its main features included bilaterally increased synchronization over temporal brain regions and decreased synchronization over the postcentral/parietal region neighboring the midline. The synchronization topography was stable over the course of several months and correlated with the severity of schizophrenia symptoms. In particular, direct correlations linked positive, negative, and general psychopathological symptoms to the hyper-synchronized temporal clusters over both hemispheres. Along with these correlations, general psychopathological symptoms inversely correlated within the hypo-synchronized postcentral midline region. While being similar to the structural maps of cortical changes in schizophrenia, the S-maps go beyond the topography limits, demonstrating a novel aspect of the abnormalities of functional cooperation: namely, regionally reduced or enhanced connectivity.The new

A distributed microcomputer-controlled system for data acquisition and power spectral analysis of EEG.

Science.gov (United States)

Vo, T D; Dwyer, G; Szeto, H H

1986-04-01

A relatively powerful and inexpensive microcomputer-based system for the spectral analysis of the EEG is presented. High resolution and speed is achieved with the use of recently available large-scale integrated circuit technology with enhanced functionality (INTEL Math co-processors 8087) which can perform transcendental functions rapidly. The versatility of the system is achieved with a hardware organization that has distributed data acquisition capability performed by the use of a microprocessor-based analog to digital converter with large resident memory (Cyborg ISAAC-2000). Compiled BASIC programs and assembly language subroutines perform on-line or off-line the fast Fourier transform and spectral analysis of the EEG which is stored as soft as well as hard copy. Some results obtained from test application of the entire system in animal studies are presented.

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

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Bin-bin SUN

2015-03-01

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

Transfer function between EEG and BOLD signals of epileptic activity

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Marco eLeite

2013-01-01

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

The standardized EEG electrode array of the IFCN.

Science.gov (United States)

Seeck, Margitta; Koessler, Laurent; Bast, Thomas; Leijten, Frans; Michel, Christoph; Baumgartner, Christoph; He, Bin; Beniczky, Sándor

2017-10-01

Standardized EEG electrode positions are essential for both clinical applications and research. The aim of this guideline is to update and expand the unifying nomenclature and standardized positioning for EEG scalp electrodes. Electrode positions were based on 20% and 10% of standardized measurements from anatomical landmarks on the skull. However, standard recordings do not cover the anterior and basal temporal lobes, which is the most frequent source of epileptogenic activity. Here, we propose a basic array of 25 electrodes including the inferior temporal chain, which should be used for all standard clinical recordings. The nomenclature in the basic array is consistent with the 10-10-system. High-density scalp EEG arrays (64-256 electrodes) allow source imaging with even sub-lobar precision. This supplementary exam should be requested whenever necessary, e.g. search for epileptogenic activity in negative standard EEG or for presurgical evaluation. In the near future, nomenclature for high density electrodes arrays beyond the 10-10 system needs to be defined, to allow comparison and standardized recordings across centers. Contrary to the established belief that smaller heads needs less electrodes, in young children at least as many electrodes as in adults should be applied due to smaller skull thickness and the risk of spatial aliasing. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Increased intra-participant variability in children with autistic spectrum disorder: Evidence from single trial analyses of evoked EEG.

Directory of Open Access Journals (Sweden)

Elizabeth eMilne

2011-03-01

Full Text Available Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial variability can be reliably measured from EEG, and that intra-participant EEG variability is significantly greater in those with ASD than in neuro-typical matched controls. EEG recorded at the scalp is a linear mixture of activity arising from muscle artifacts and numerous concurrent brain processes. To minimise these additional sources of variability, EEG data were subjected to two different methods of spatial filtering. (i The data were decomposed using infomax Independent Component Analysis (ICA, a method of blind source separation which un-mixes the EEG signal into components with maximally independent time-courses, and (ii a surface Laplacian transform was performed (Current Source Density interpolation in order to reduce the effects of volume conduction. Data are presented from thirteen high functioning adolescents with ASD without co-morbid ADHD, and twelve neuro-typical age- IQ- and gender-matched controls. Comparison of variability between the ASD and neuro-typical groups indicated that intra-participant variability of P1 latency and P1 amplitude was greater in the participants with ASD, and inter-trial α-band phase coherence was lower in the participants with ASD. These data support the suggestion that individuals with ASD are less able to synchronise the activity of stimulus-related cell assemblies than neuro-typical individuals, and provide empirical evidence in support of theories of increased neural noise in ASD.

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...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

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Lin, Lung-Chang; Chen, Sharon Chia-Ju; Chiang, Ching-Tai; Wu, Hui-Chuan; Yang, Rei-Cheng; Ouyang, Chen-Sen

2017-03-01

The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction involves the classification of the preictal and interictal stages. This study aimed to develop an efficient, automatic, quantitative, and individualized approach for preictal/interictal stage identification. Five epileptic children, who had experienced at least 2 episodes of seizures during a 24-hour video EEG recording, were included. Artifact-free preictal and interictal EEG epochs were acquired, respectively, and characterized with 216 global feature descriptors. The best subset of 5 discriminative descriptors was identified. The best subsets showed differences among the patients. Statistical analysis revealed most of the 5 descriptors in each subset were significantly different between the preictal and interictal stages for each patient. The proposed approach yielded weighted averages of 97.50% correctness, 96.92% sensitivity, 97.78% specificity, and 95.45% precision on classifying test epochs. Although the case number was limited, this study successfully integrated a new EEG analytical method to classify preictal and interictal EEG segments and might be used further in predicting the occurrence of seizures.

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

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François-B. Vialatte

2011-01-01

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

Saccadic spike potentials in gamma-band EEG: characterization, detection and suppression.

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Keren, Alon S; Yuval-Greenberg, Shlomit; Deouell, Leon Y

2010-02-01

Analysis of high-frequency (gamma-band) neural activity by means of non-invasive EEG is gaining increasing interest. However, we have recently shown that a saccade-related spike potential (SP) seriously confounds the analysis of EEG induced gamma-band responses (iGBR), as the SP eludes traditional EEG artifact rejection methods. Here we provide a comprehensive profile of the SP and evaluate methods for its detection and suppression, aiming to unveil true cerebral gamma-band activity. The SP appears consistently as a sharp biphasic deflection of about 22 ms starting at the saccade onset, with a frequency band of approximately 20-90 Hz. On the average, larger saccades elicit higher SP amplitudes. The SP amplitude gradually changes from the extra-ocular channels towards posterior sites with the steepest gradients around the eyes, indicating its ocular source. Although the amplitude and the sign of the SP depend on the choice of reference channel, the potential gradients remain the same and non-zero for all references. The scalp topography is modulated almost exclusively by the direction of saccades, with steeper gradients ipsilateral to the saccade target. We discuss how the above characteristics impede attempts to remove these SPs from the EEG by common temporal filtering, choice of different references, or rejection of contaminated trials. We examine the extent to which SPs can be reliably detected without an eye tracker, assess the degree to which scalp current density derivation attenuates the effect of the SP, and propose a tailored ICA procedure for minimizing the effect of the SP. Copyright (c) 2009 Elsevier Inc. All rights reserved.

EEG Oscillations Are Modulated in Different Behavior-Related Networks during Rhythmic Finger Movements.

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Seeber, Martin; Scherer, Reinhold; Müller-Putz, Gernot R

2016-11-16

Sequencing and timing of body movements are essential to perform motoric tasks. In this study, we investigate the temporal relation between cortical oscillations and human motor behavior (i.e., rhythmic finger movements). High-density EEG recordings were used for source imaging based on individual anatomy. We separated sustained and movement phase-related EEG source amplitudes based on the actual finger movements recorded by a data glove. Sustained amplitude modulations in the contralateral hand area show decrease for α (10-12 Hz) and β (18-24 Hz), but increase for high γ (60-80 Hz) frequencies during the entire movement period. Additionally, we found movement phase-related amplitudes, which resembled the flexion and extension sequence of the fingers. Especially for faster movement cadences, movement phase-related amplitudes included high β (24-30 Hz) frequencies in prefrontal areas. Interestingly, the spectral profiles and source patterns of movement phase-related amplitudes differed from sustained activities, suggesting that they represent different frequency-specific large-scale networks. First, networks were signified by the sustained element, which statically modulate their synchrony levels during continuous movements. These networks may upregulate neuronal excitability in brain regions specific to the limb, in this study the right hand area. Second, movement phase-related networks, which modulate their synchrony in relation to the movement sequence. We suggest that these frequency-specific networks are associated with distinct functions, including top-down control, sensorimotor prediction, and integration. The separation of different large-scale networks, we applied in this work, improves the interpretation of EEG sources in relation to human motor behavior. EEG recordings provide high temporal resolution suitable to relate cortical oscillations to actual movements. Investigating EEG sources during rhythmic finger movements, we distinguish sustained from

Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software.

Science.gov (United States)

Rogasch, Nigel C; Sullivan, Caley; Thomson, Richard H; Rose, Nathan S; Bailey, Neil W; Fitzgerald, Paul B; Farzan, Faranak; Hernandez-Pavon, Julio C

2017-02-15

The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS-EEG) is growing in popularity as a method for assessing various cortical properties such as excitability, oscillations and connectivity. However, this combination of methods is technically challenging, resulting in artifacts both during recording and following typical EEG analysis methods, which can distort the underlying neural signal. In this article, we review the causes of artifacts in EEG recordings resulting from TMS, as well as artifacts introduced during analysis (e.g. as the result of filtering over high-frequency, large amplitude artifacts). We then discuss methods for removing artifacts, and ways of designing pipelines to minimise analysis-related artifacts. Finally, we introduce the TMS-EEG signal analyser (TESA), an open-source extension for EEGLAB, which includes functions that are specific for TMS-EEG analysis, such as removing and interpolating the TMS pulse artifact, removing and minimising TMS-evoked muscle activity, and analysing TMS-evoked potentials. The aims of TESA are to provide users with easy access to current TMS-EEG analysis methods and to encourage direct comparisons of these methods and pipelines. It is hoped that providing open-source functions will aid in both improving and standardising analysis across the field of TMS-EEG research. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Comparison of quantitative EEG characteristics of quiet and active sleep in newborns.

Science.gov (United States)

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

2003-11-01

The aim of the present study was to verify whether the proposed method of computer-supported EEG analysis is able to differentiate the EEG activity in quiet sleep (QS) from that in active sleep (AS) in newborns. A quantitative description of the neonatal EEG may contribute to a more exact evaluation of the functional state of the brain, as well as to a refinement of diagnostics of brain dysfunction manifesting itself frequently as 'dysrhythmia' or 'dysmaturity'. Twenty-one healthy newborns (10 full-term and 11 pre-term) were examined polygraphically (EEG-eight channels, respiration, ECG, EOG and EMG) in the course of sleep. From each EEG record, two 5-min samples (one from QS and one from AS) were subject to an off-line computerized analysis. The obtained data were averaged with respect to the sleep state and to the conceptional age. The number of variables was reduced by means of factor analysis. All factors identified by factor analysis were highly significantly influenced by sleep states in both developmental periods. Likewise, a comparison of the measured variables between QS and AS revealed many statistically significant differences. The variables describing (a) the number and length of quasi-stationary segments, (b) voltage and (c) power in delta and theta bands contributed to the greatest degree to the differentiation of EEGs between both sleep states. The presented method of the computerized EEG analysis which has good discriminative potential is adequately sensitive and describes the neonatal EEG with convenient accuracy.

Disentangling Tinnitus Distress and Tinnitus Presence by Means of EEG Power Analysis

Directory of Open Access Journals (Sweden)

Martin Meyer

2014-01-01

Full Text Available The present study investigated 24 individuals suffering from chronic tinnitus (TI and 24 nonaffected controls (CO. We recorded resting-state EEG and collected psychometric data to obtain information about how chronic tinnitus experience affects the cognitive and emotional state of TI. The study was meant to disentangle TI with high distress from those who suffer less from persistent tinnitus based on both neurophysiological and behavioral data. A principal component analysis of psychometric data uncovers two distinct independent dimensions characterizing the individual tinnitus experience. These independent states are distress and presence, the latter is described as the perceived intensity of sound experience that increases with tinnitus duration devoid of any considerable emotional burden. Neuroplastic changes correlate with the two independent components. TI with high distress display increased EEG activity in the oscillatory range around 25 Hz (upper β-band that agglomerates over frontal recording sites. TI with high presence show enhanced EEG signal strength in the δ-, α-, and lower γ-bands (30–40 Hz over bilateral temporal and left perisylvian electrodes. Based on these differential patterns we suggest that the two dimensions, namely, distress and presence, should be considered as independent dimensions of chronic subjective tinnitus.

Standardized computer-based organized reporting of EEG:SCORE

DEFF Research Database (Denmark)

Beniczky, Sandor; H, Aurlien,; JC, Brøgger,

2013-01-01

process, organized by the European Chapter of the International Federation of Clinical Neurophysiology. The Standardised Computer-based Organised Reporting of EEG (SCORE) software was constructed based on the terms and features of the consensus statement and it was tested in the clinical practice...... in free-text format. The purpose of our endeavor was to create a computer-based system for EEG assessment and reporting, where the physicians would construct the reports by choosing from predefined elements for each relevant EEG feature, as well as the clinical phenomena (for video-EEG recordings....... SCORE can potentially improve the quality of EEG assessment and reporting; it will help incorporate the results of computer-assisted analysis into the report, it will make possible the build-up of a multinational database, and it will help in training young neurophysiologists....

EEG-Annotate: Automated identification and labeling of events in continuous signals with applications to EEG.

Science.gov (United States)

Su, Kyung-Min; Hairston, W David; Robbins, Kay

2018-01-01

In controlled laboratory EEG experiments, researchers carefully mark events and analyze subject responses time-locked to these events. Unfortunately, such markers may not be available or may come with poor timing resolution for experiments conducted in less-controlled naturalistic environments. We present an integrated event-identification method for identifying particular responses that occur in unlabeled continuously recorded EEG signals based on information from recordings of other subjects potentially performing related tasks. We introduce the idea of timing slack and timing-tolerant performance measures to deal with jitter inherent in such non-time-locked systems. We have developed an implementation available as an open-source MATLAB toolbox (http://github.com/VisLab/EEG-Annotate) and have made test data available in a separate data note. We applied the method to identify visual presentation events (both target and non-target) in data from an unlabeled subject using labeled data from other subjects with good sensitivity and specificity. The method also identified actual visual presentation events in the data that were not previously marked in the experiment. Although the method uses traditional classifiers for initial stages, the problem of identifying events based on the presence of stereotypical EEG responses is the converse of the traditional stimulus-response paradigm and has not been addressed in its current form. In addition to identifying potential events in unlabeled or incompletely labeled EEG, these methods also allow researchers to investigate whether particular stereotypical neural responses are present in other circumstances. Timing-tolerance has the added benefit of accommodating inter- and intra- subject timing variations. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

EEG simulation by 2D interconnected chaotic oscillators

Energy Technology Data Exchange (ETDEWEB)

Kubany, Adam, E-mail: adamku@bgu.ac.i [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel); Mhabary, Ziv; Gontar, Vladimir [Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, P.O. Box 653, Beer-Sheva 84105 (Israel)

2011-01-15

Research highlights: ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. An inverse problem solution (PRCGA) is proposed. Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

EEG simulation by 2D interconnected chaotic oscillators

International Nuclear Information System (INIS)

Kubany, Adam; Mhabary, Ziv; Gontar, Vladimir

2011-01-01

Research highlights: → ANN of 2D interconnected chaotic oscillators is explored for EEG simulation. → An inverse problem solution (PRCGA) is proposed. → Good matching between the simulated and experimental EEG signals has been achieved. - Abstract: An artificial neuronal network composed by 2D interconnected chaotic oscillators is explored for brain waves (EEG) simulation. For the inverse problem solution a parallel real-coded genetic algorithm (PRCGA) is proposed. In order to conduct thorough comparison between the simulated and target signal characteristics, a spectrum analysis of the signals is undertaken. A good matching between the theoretical and experimental EEG signals has been achieved. Numerical results of calculations are presented and discussed.

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-11-01

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

Physiological artifacts in scalp EEG and ear-EEG.

Science.gov (United States)

Kappel, Simon L; Looney, David; Mandic, Danilo P; Kidmose, Preben

2017-08-11

A problem inherent to recording EEG is the interference arising from noise and artifacts. While in a laboratory environment, artifacts and interference can, to a large extent, be avoided or controlled, in real-life scenarios this is a challenge. Ear-EEG is a concept where EEG is acquired from electrodes in the ear. We present a characterization of physiological artifacts generated in a controlled environment for nine subjects. The influence of the artifacts was quantified in terms of the signal-to-noise ratio (SNR) deterioration of the auditory steady-state response. Alpha band modulation was also studied in an open/closed eyes paradigm. Artifacts related to jaw muscle contractions were present all over the scalp and in the ear, with the highest SNR deteriorations in the gamma band. The SNR deterioration for jaw artifacts were in general higher in the ear compared to the scalp. Whereas eye-blinking did not influence the SNR in the ear, it was significant for all groups of scalps electrodes in the delta and theta bands. Eye movements resulted in statistical significant SNR deterioration in both frontal, temporal and ear electrodes. Recordings of alpha band modulation showed increased power and coherence of the EEG for ear and scalp electrodes in the closed-eyes periods. Ear-EEG is a method developed for unobtrusive and discreet recording over long periods of time and in real-life environments. This study investigated the influence of the most important types of physiological artifacts, and demonstrated that spontaneous activity, in terms of alpha band oscillations, could be recorded from the ear-EEG platform. In its present form ear-EEG was more prone to jaw related artifacts and less prone to eye-blinking artifacts compared to state-of-the-art scalp based systems.

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

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

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

EEG oscillatory power dissociates between distress- and depression-related psychopathology in subjective tinnitus.

Science.gov (United States)

Meyer, Martin; Neff, Patrick; Grest, Angelina; Hemsley, Colette; Weidt, Steffi; Kleinjung, Tobias

2017-05-15

Recent research has used source estimation approaches to identify spatially distinct neural configurations in individuals with chronic, subjective tinnitus (TI). The results of these studies are often heterogeneous, a fact which may be partly explained by an inherent heterogeneity in the TI population and partly by the applied EEG data analysis procedure and EEG hardware. Hence this study was performed to re-enact a formerly published study (Joos et al., 2012) to better understand the reason for differences and overlap between studies from different labs. We re-investigated the relationship between neural oscillations and behavioral measurements of affective states in TI, namely depression and tinnitus-related distress by recruiting 45 TI who underwent resting-state EEG. Comprehensive psychopathological (depression and tinnitus-related distress scores) and psychometric data (including other tinnitus characteristics) were gathered. A principal component analysis (PCA) was performed to unveil independent factors that predict distinct aspects of tinnitus-related pathology. Furthermore, we correlated EEG power changes in the standard frequency bands with the behavioral scores for both the whole-brain level and, as a post hoc approach, for selected regions of interest (ROI) based on sLORETA. Behavioral data revealed significant relationships between measurements of depression and tinnitus-related distress. Notably, no significant results were observed for the depressive scores and modulations of the EEG signal. However, akin to the former study we evidenced a significant relationship between a power increase in the β-bands and tinnitus-related distress. In conclusion, it has emerged that depression and tinnitus-related distress, even though they are assumed not to be completely independent, manifest in distinct neural configurations. Copyright © 2017 Elsevier B.V. All rights reserved.

EEG datasets for motor imagery brain-computer interface.

Science.gov (United States)

Cho, Hohyun; Ahn, Minkyu; Ahn, Sangtae; Kwon, Moonyoung; Jun, Sung Chan

2017-07-01

Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. © The Authors 2017. Published by Oxford University Press.

Bayesian Correlated Component Analysis for inference of joint EEG activation

DEFF Research Database (Denmark)

Poulsen, Andreas Trier; Kamronn, Simon Due; Parra, Lucas

2014-01-01

We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset.......We propose a probabilistic generative multi-view model to test the representational universality of human information processing. The model is tested in simulated data and in a well-established benchmark EEG dataset....

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.

Source and sink nodes in absence seizures.

Science.gov (United States)

Rodrigues, Abner C; Machado, Birajara S; Caboclo, Luis Otavio S F; Fujita, Andre; Baccala, Luiz A; Sameshima, Koichi

2016-08-01

As opposed to focal epilepsy, absence seizures do not exhibit a clear seizure onset zone or focus since its ictal activity rapidly engages both brain hemispheres. Yet recent graph theoretical analysis applied to absence seizures EEG suggests the cortical focal presence, an unexpected feature for this type of epilepsy. In this study, we explore the characteristics of absence seizure by classifying the nodes as to their source/sink natures via weighted directed graph analysis based on connectivity direction and strength estimation using information partial directed coherence (iPDC). By segmenting the EEG signals into relatively short 5-sec-long time windows we studied the evolution of coupling strengths from both sink and source nodes, and the network dynamics of absence seizures in eight patients.

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.

Nonlinear analysis of EEGs of patients with major depression during different emotional states.

Science.gov (United States)

Akdemir Akar, Saime; Kara, Sadık; Agambayev, Sümeyra; Bilgiç, Vedat

2015-12-01

Although patients with major depressive disorder (MDD) have dysfunctions in cognitive behaviors and the regulation of emotions, the underlying brain dynamics of the pathophysiology are unclear. Therefore, nonlinear techniques can be used to understand the dynamic behavior of the EEG signals of MDD patients. To investigate and clarify the dynamics of MDD patients׳ brains during different emotional states, EEG recordings were analyzed using nonlinear techniques. The purpose of the present study was to assess whether there are different EEG complexities that discriminate between MDD patients and healthy controls during emotional processing. Therefore, nonlinear parameters, such as Katz fractal dimension (KFD), Higuchi fractal dimension (HFD), Shannon entropy (ShEn), Lempel-Ziv complexity (LZC) and Kolmogorov complexity (KC), were computed from the EEG signals of two groups under different experimental states: noise (negative emotional content) and music (positive emotional content) periods. First, higher complexity values were generated by MDD patients relative to controls. Significant differences were obtained in the frontal and parietal scalp locations using KFD (pemotional bias was demonstrated by their higher brain complexities during the noise period than the music stimulus. Additionally, we found that the KFD, HFD and LZC values were more sensitive in discriminating between patients and controls than the ShEn and KC measures, according to the results of ANOVA and ROC calculations. It can be concluded that the nonlinear analysis may be a useful and discriminative tool in investigating the neuro-dynamic properties of the brain in patients with MDD during emotional stimulation. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spectral analysis of the EEG during halothane anaesthesia: Input-output relations

NARCIS (Netherlands)

Silva, F.H. Lopes da; Smith, N. Ty; Zwart, Aart; Nichols, W.W.

1. 1. The “Halothane-brain compartment” system was investigated in dogs. The input was the inspired concentration of Halothane. The output was the intensity of EEG spectral components. The EEG was analysed by a hybrid system (analogue filters and digital integration in a small computer). For the

Patient Specific Characteristic of Brain Dynamic in Interpretation of Long Term EEG Analysis

Czech Academy of Sciences Publication Activity Database

Komárek, V.; Paluš, Milan; Hrnčíř, Z.

2004-01-01

Roč. 45, Suppl. 3 (2004), s. 51 ISSN 0013-9580. [European Congress on Epileptology /6./. 30.05.2004-03.06.2004, Vienna] R&D Projects: GA MŠk ME 701 Institutional research plan: CEZ:AV0Z1030915 Keywords : brain dynamic * long term EEG analysis Subject RIV: FH - Neurology

Increased theta band EEG power in sickle cell disease patients

Directory of Open Access Journals (Sweden)

Case M

2017-12-01

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

Synchronization of EEG activity in patients with bipolar disorder

Science.gov (United States)

Panischev, O. Yu; Demin, S. A.; Muhametshin, I. G.; Demina, N. Yu

2015-12-01

In paper we apply the method based on the Flicker-Noise Spectroscopy (FNS) to determine the differences in frequency-phase synchronization of the cortical electroencephalographic (EEG) activities in patients with bipolar disorder (BD). We found that for healthy subjects the frequency-phase synchronization of EEGs from long-range electrodes was significantly better for BD patients. In BD patients a high synchronization of EEGs was observed only for short-range electrodes. Thus, the FNS is a simple graphical method for qualitative analysis can be applied to identify the synchronization effects in EEG activity and, probably, may be used for the diagnosis of this syndrome.

Corrected Four-Sphere Head Model for EEG Signals.

Science.gov (United States)

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

2017-01-01

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

Corrected Four-Sphere Head Model for EEG Signals

Directory of Open Access Journals (Sweden)

Solveig Næss

2017-10-01

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

Rational manipulation of digital EEG: pearls and pitfalls.

Science.gov (United States)

Seneviratne, Udaya

2014-12-01

The advent of digital EEG has provided greater flexibility and more opportunities in data analysis to optimize the diagnostic yield. Changing the filter settings, sensitivity, montages, and time-base are possible rational manipulations to achieve this goal. The options to use polygraphy, video, and quantification are additional useful features. Aliasing and loss of data are potential pitfalls in the use of digital EEG. This review illustrates some common clinical scenarios where rational manipulations can enhance the diagnostic EEG yield and potential pitfalls in the process.

Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction.

Science.gov (United States)

de Munck, Jan C; van Houdt, Petra J; Gonçalves, Sónia I; van Wegen, Erwin; Ossenblok, Pauly P W

2013-01-01

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with

Wireless and wearable EEG system for evaluating driver vigilance.

Science.gov (United States)

Lin, Chin-Teng; Chuang, Chun-Hsiang; Huang, Chih-Sheng; Tsai, Shu-Fang; Lu, Shao-Wei; Chen, Yen-Hsuan; Ko, Li-Wei

2014-04-01

Brain activity associated with attention sustained on the task of safe driving has received considerable attention recently in many neurophysiological studies. Those investigations have also accurately estimated shifts in drivers' levels of arousal, fatigue, and vigilance, as evidenced by variations in their task performance, by evaluating electroencephalographic (EEG) changes. However, monitoring the neurophysiological activities of automobile drivers poses a major measurement challenge when using a laboratory-oriented biosensor technology. This work presents a novel dry EEG sensor based mobile wireless EEG system (referred to herein as Mindo) to monitor in real time a driver's vigilance status in order to link the fluctuation of driving performance with changes in brain activities. The proposed Mindo system incorporates the use of a wireless and wearable EEG device to record EEG signals from hairy regions of the driver conveniently. Additionally, the proposed system can process EEG recordings and translate them into the vigilance level. The study compares the system performance between different regression models. Moreover, the proposed system is implemented using JAVA programming language as a mobile application for online analysis. A case study involving 15 study participants assigned a 90 min sustained-attention driving task in an immersive virtual driving environment demonstrates the reliability of the proposed system. Consistent with previous studies, power spectral analysis results confirm that the EEG activities correlate well with the variations in vigilance. Furthermore, the proposed system demonstrated the feasibility of predicting the driver's vigilance in real time.

EEG, job motor or job killer? Symposium for a more rational discussion; EEG - Jobmotor oder Jobkiller? Symposium fuehrt zur Versachlichung der Diskussion

Energy Technology Data Exchange (ETDEWEB)

Haeder, M. [Fachhochschule Bochum (Germany); Schulz, E. [Verband der Elektrizitaetswirtschaft - VDEW - e.V., Berlin (Germany)

2005-12-12

The authors investigated the job effects of the Renewables Act (EEG) discussed at the VDEW symposium of October 2004. There are many divergent factors which necessitated the use of differentiated econometric models. The conclusion was that the EEG should not be judged on the basis of job effects but from environmental and energy aspects. Positive job effects may be expected from more efficient production of electric power from renewable energy sources, which may be achieved by funding on the basis of a EU funding model. (orig.)

FACET - a "Flexible Artifact Correction and Evaluation Toolbox" for concurrently recorded EEG/fMRI data.

Science.gov (United States)

Glaser, Johann; Beisteiner, Roland; Bauer, Herbert; Fischmeister, Florian Ph S

2013-11-09

In concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an Analysis, a Correction and an Evaluation framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings. FACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230-239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720-737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices. The FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches.

EEG signatures of arm isometric exertions in preparation, planning and execution.

Science.gov (United States)

Nasseroleslami, Bahman; Lakany, Heba; Conway, Bernard A

2014-04-15

The electroencephalographic (EEG) activity patterns in humans during motor behaviour provide insight into normal motor control processes and for diagnostic and rehabilitation applications. While the patterns preceding brisk voluntary movements, and especially movement execution, are well described, there are few EEG studies that address the cortical activation patterns seen in isometric exertions and their planning. In this paper, we report on time and time-frequency EEG signatures in experiments in normal subjects (n=8), using multichannel EEG during motor preparation, planning and execution of directional centre-out arm isometric exertions performed at the wrist in the horizontal plane, in response to instruction-delay visual cues. Our observations suggest that isometric force exertions are accompanied by transient and sustained event-related potentials (ERP) and event-related (de-)synchronisations (ERD/ERS), comparable to those of a movement task. Furthermore, the ERPs and ERD/ERS are also observed during preparation and planning of the isometric task. Comparison of ear-lobe-referenced and surface Laplacian ERPs indicates the contribution of superficial sources in supplementary and pre-motor (FC(z)), parietal (CP(z)) and primary motor cortical areas (C₁ and FC₁) to ERPs (primarily negative peaks in frontal and positive peaks in parietal areas), but contribution of deep sources to sustained time-domain potentials (negativity in planning and positivity in execution). Transient and sustained ERD patterns in μ and β frequency bands of ear-lobe-referenced and surface Laplacian EEG indicate the contribution of both superficial and deep sources to ERD/ERS. As no physical displacement happens during the task, we can infer that the underlying mechanisms of motor-related ERPs and ERD/ERS patterns do not only depend on change in limb coordinate or muscle-length-dependent ascending sensory information and are primary generated by motor preparation, direction

Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.

Science.gov (United States)

Giacometti, Paolo; Perdue, Katherine L; Diamond, Solomon G

2014-05-30

Interpretation and analysis of electroencephalography (EEG) measurements relies on the correspondence of electrode scalp coordinates to structural and functional regions of the brain. An algorithm is introduced for automatic calculation of the International 10-20, 10-10, and 10-5 scalp coordinates of EEG electrodes on a boundary element mesh of a human head. The EEG electrode positions are then used to generate parcellation regions of the cerebral cortex based on proximity to the EEG electrodes. The scalp electrode calculation method presented in this study effectively and efficiently identifies EEG locations without prior digitization of coordinates. The average of electrode proximity parcellations of the cortex were tabulated with respect to structural and functional regions of the brain in a population of 20 adult subjects. Parcellations based on electrode proximity and EEG sensitivity were compared. The parcellation regions based on sensitivity and proximity were found to have 44.0 ± 11.3% agreement when demarcated by the International 10-20, 32.4 ± 12.6% by the 10-10, and 24.7 ± 16.3% by the 10-5 electrode positioning system. The EEG positioning algorithm is a fast and easy method of locating EEG scalp coordinates without the need for digitized electrode positions. The parcellation method presented summarizes the EEG scalp locations with respect to brain regions without computation of a full EEG forward model solution. The reference table of electrode proximity versus cortical regions may be used by experimenters to select electrodes that correspond to anatomical and functional regions of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

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

Directory of Open Access Journals (Sweden)

Jonas Duun-Henriksen

2015-01-01

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

Analysis of brain activity and response to colour stimuli during learning tasks: an EEG study

Science.gov (United States)

Folgieri, Raffaella; Lucchiari, Claudio; Marini, Daniele

2013-02-01

The research project intends to demonstrate how EEG detection through BCI device can improve the analysis and the interpretation of colours-driven cognitive processes through the combined approach of cognitive science and information technology methods. To this end, firstly it was decided to design an experiment based on comparing the results of the traditional (qualitative and quantitative) cognitive analysis approach with the EEG signal analysis of the evoked potentials. In our case, the sensorial stimulus is represented by the colours, while the cognitive task consists in remembering the words appearing on the screen, with different combination of foreground (words) and background colours. In this work we analysed data collected from a sample of students involved in a learning process during which they received visual stimuli based on colour variation. The stimuli concerned both the background of the text to learn and the colour of the characters. The experiment indicated some interesting results concerning the use of primary (RGB) and complementary (CMY) colours.

EEG feature selection method based on decision tree.

Science.gov (United States)

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

2015-01-01

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

Measurement and modification of the EEG and related behavior

Science.gov (United States)

Sterman, M. B.

1991-01-01

Electrophysiological changes in the sensorimotor pathways were found to accompany the effect of rhythmic EEG patterns in the sensorimotor cortex. Additionally, several striking behavioral changes were seen, including in particular an enhancement of sleep and an elevation of seizure threshold to epileptogenic agents. This raised the possibility that human seizure disorders might be influenced therapeutically by similar training. Our objective in human EEG feedback training became not only the facilitation of normal rhythmic patterns, but also the suppression of abnormal activity, thus requiring complex contingencies directed to the normalization of the sensorimotor EEG. To achieve this, a multicomponent frequency analysis was developed to extract and separate normal and abnormal elements of the EEG signal. Each of these elements was transduced to a specific component of a visual display system, and these were combined through logic circuits to present the subject with a symbolic display. Variable criteria provided for the gradual shaping of EEG elements towards the desired normal pattern. Some 50-70% of patients with poorly controlled seizure disorders experienced therapeutic benefits from this approach in our laboratory, and subsequently in many others. A more recent application of this approach to the modification of human brain function in our lab has been directed to the dichotomous problems of task overload and underload in the contemporary aviation environment. At least 70% of all aviation accidents have been attributed to the impact of these kinds of problems on crew performance. The use of EEG in this context has required many technical innovations and the application of the latest advances in EEG signal analysis. Our first goal has been the identification of relevant EEG characteristics. Additionally, we have developed a portable recording and analysis system for application in this context. Findings from laboratory and in-flight studies suggest that we

A multi-dimensional functional principal components analysis of EEG data.

Science.gov (United States)

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Discovering EEG resting state alterations of semantic dementia.

Science.gov (United States)

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

2016-05-01

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

Random matrix analysis of human EEG data

Czech Academy of Sciences Publication Activity Database

Šeba, Petr

2003-01-01

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

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

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Vangelis Sakkalis

2008-01-01

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

Synchronization of EEG activity in patients with bipolar disorder

International Nuclear Information System (INIS)

Panischev, O Yu; Demin, S A; Muhametshin, I G; Yu Demina, N

2015-01-01

In paper we apply the method based on the Flicker-Noise Spectroscopy (FNS) to determine the differences in frequency-phase synchronization of the cortical electroencephalographic (EEG) activities in patients with bipolar disorder (BD). We found that for healthy subjects the frequency-phase synchronization of EEGs from long-range electrodes was significantly better for BD patients. In BD patients a high synchronization of EEGs was observed only for short-range electrodes. Thus, the FNS is a simple graphical method for qualitative analysis can be applied to identify the synchronization effects in EEG activity and, probably, may be used for the diagnosis of this syndrome. (paper)

An EEG should not be obtained routinely after first unprovoked seizure in childhood.

Science.gov (United States)

Gilbert, D L; Buncher, C R

2000-02-08

To quantify and analyze the value of expected information from an EEG after first unprovoked seizure in childhood. An EEG is often recommended as part of the standard diagnostic evaluation after first seizure. A MEDLINE search from 1980 to 1998 was performed. From eligible studies, data on EEG results and seizure recurrence risk in children were abstracted, and sensitivity, specificity, and positive and negative predictive values of EEG in predicting recurrence were calculated. Linear information theory was used to quantify and compare the expected information from the EEG in all studies. Standard test-treat decision analysis with a treatment threshold at 80% recurrence risk was used to determine the range of pretest recurrence probabilities over which testing affects treatment decisions. Four studies involving 831 children were eligible for analysis. At best, the EEG had a sensitivity of 61%, a specificity of 71%, and an expected information of 0.16 out of a possible 0.50. The pretest probability of recurrence was less than the lower limit of the range for rational testing in all studies. In this analysis, the quantity of expected information from the EEG was too low to affect treatment recommendations in most patients. EEG should be ordered selectively, not routinely, after first unprovoked seizure in childhood.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

Science.gov (United States)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Sources of EEG activity most relevant to performance of brain-computer interface based on motor imagery

Czech Academy of Sciences Publication Activity Database

Frolov, A.; Húsek, Dušan; Bobrov, P.; Korshakov, A.V.; Chernikova, L.; Konovalov, R.; Mokienko, O.

2012-01-01

Roč. 22, č. 1 (2012), s. 21-37 ISSN 1210-0552 R&D Projects: GA ČR GAP202/10/0262 Grant - others:GA MŠk(CZ) ED1.1.00/02.0070 Program:ED Institutional research plan: CEZ:AV0Z10300504 Keywords : brain-computer interface * independent component analysis * pattern classification * motor imagery * inverse problem * fMRI * EEG Subject RIV: IN - Informatics, Computer Science Impact factor: 0.362, year: 2012

EEG Controlled Wheelchair

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Swee Sim Kok

2016-01-01

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

Correlation between intra- and extracranial background EEG

DEFF Research Database (Denmark)

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

2012-01-01

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

EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing.

Science.gov (United States)

Cohen, Michael X; Ridderinkhof, K Richard

2013-01-01

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus) conflict modulation in stimulus-contralateral parietal gamma (30-50 Hz), followed by a later alpha-band (8-12 Hz) conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4-8 Hz), alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials) had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions.

EEG source reconstruction reveals frontal-parietal dynamics of spatial conflict processing.

Directory of Open Access Journals (Sweden)

Michael X Cohen

Full Text Available Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus conflict modulation in stimulus-contralateral parietal gamma (30-50 Hz, followed by a later alpha-band (8-12 Hz conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4-8 Hz, alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions.

EEG Source Reconstruction Reveals Frontal-Parietal Dynamics of Spatial Conflict Processing

Science.gov (United States)

Cohen, Michael X; Ridderinkhof, K. Richard

2013-01-01

Cognitive control requires the suppression of distracting information in order to focus on task-relevant information. We applied EEG source reconstruction via time-frequency linear constrained minimum variance beamforming to help elucidate the neural mechanisms involved in spatial conflict processing. Human subjects performed a Simon task, in which conflict was induced by incongruence between spatial location and response hand. We found an early (∼200 ms post-stimulus) conflict modulation in stimulus-contralateral parietal gamma (30–50 Hz), followed by a later alpha-band (8–12 Hz) conflict modulation, suggesting an early detection of spatial conflict and inhibition of spatial location processing. Inter-regional connectivity analyses assessed via cross-frequency coupling of theta (4–8 Hz), alpha, and gamma power revealed conflict-induced shifts in cortical network interactions: Congruent trials (relative to incongruent trials) had stronger coupling between frontal theta and stimulus-contrahemifield parietal alpha/gamma power, whereas incongruent trials had increased theta coupling between medial frontal and lateral frontal regions. These findings shed new light into the large-scale network dynamics of spatial conflict processing, and how those networks are shaped by oscillatory interactions. PMID:23451201

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.

Source localization of intermittent rhythmic delta activity in a patient with acute confusional migraine: cross-spectral analysis using standardized low-resolution brain electromagnetic tomography (sLORETA).

Science.gov (United States)

Kim, Dae-Eun; Shin, Jung-Hyun; Kim, Young-Hoon; Eom, Tae-Hoon; Kim, Sung-Hun; Kim, Jung-Min

2016-01-01

Acute confusional migraine (ACM) shows typical electroencephalography (EEG) patterns of diffuse delta slowing and frontal intermittent rhythmic delta activity (FIRDA). The pathophysiology of ACM is still unclear but these patterns suggest neuronal dysfunction in specific brain areas. We performed source localization analysis of IRDA (in the frequency band of 1-3.5 Hz) to better understand the ACM mechanism. Typical IRDA EEG patterns were recorded in a patient with ACM during the acute stage. A second EEG was obtained after recovery from ACM. To identify source localization of IRDA, statistical non-parametric mapping using standardized low-resolution brain electromagnetic tomography was performed for the delta frequency band comparisons between ACM attack and non-attack periods. A difference in the current density maximum was found in the dorsal anterior cingulated cortex (ACC). The significant differences were widely distributed over the frontal, parietal, temporal and limbic lobe, paracentral lobule and insula and were predominant in the left hemisphere. Dorsal ACC dysfunction was demonstrated for the first time in a patient with ACM in this source localization analysis of IRDA. The ACC plays an important role in the frontal attentional control system and acute confusion. This dysfunction of the dorsal ACC might represent an important ACM pathophysiology.

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

NARCIS (Netherlands)

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

2017-01-01

Background: We recently showed that electroencephalography (EEG) patterns within the first 24 hours robustly contribute to multimodal prediction of poor or good neurological outcome of comatose patients after cardiac arrest. Here, we confirm these results and present a cost-minimization analysis.

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

NARCIS (Netherlands)

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

1989-01-01

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

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

Science.gov (United States)

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Brain oscillations in sport: toward EEG biomakers of performance

Directory of Open Access Journals (Sweden)

Guy eCheron

2016-02-01

Full Text Available Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The noninvasive nature of high-density electroencephalography (EEG recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

Science.gov (United States)

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

BLINKER: Automated Extraction of Ocular Indices from EEG Enabling Large-Scale Analysis.

Science.gov (United States)

Kleifges, Kelly; Bigdely-Shamlo, Nima; Kerick, Scott E; Robbins, Kay A

2017-01-01

Electroencephalography (EEG) offers a platform for studying the relationships between behavioral measures, such as blink rate and duration, with neural correlates of fatigue and attention, such as theta and alpha band power. Further, the existence of EEG studies covering a variety of subjects and tasks provides opportunities for the community to better characterize variability of these measures across tasks and subjects. We have implemented an automated pipeline (BLINKER) for extracting ocular indices such as blink rate, blink duration, and blink velocity-amplitude ratios from EEG channels, EOG channels, and/or independent components (ICs). To illustrate the use of our approach, we have applied the pipeline to a large corpus of EEG data (comprising more than 2000 datasets acquired at eight different laboratories) in order to characterize variability of certain ocular indicators across subjects. We also investigate dependence of ocular indices on task in a shooter study. We have implemented our algorithms in a freely available MATLAB toolbox called BLINKER. The toolbox, which is easy to use and can be applied to collections of data without user intervention, can automatically discover which channels or ICs capture blinks. The tools extract blinks, calculate common ocular indices, generate a report for each dataset, dump labeled images of the individual blinks, and provide summary statistics across collections. Users can run BLINKER as a script or as a plugin for EEGLAB. The toolbox is available at https://github.com/VisLab/EEG-Blinks. User documentation and examples appear at http://vislab.github.io/EEG-Blinks/.

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...

Induction and separation of motion artifacts in EEG data using a mobile phantom head device.

Science.gov (United States)

Oliveira, Anderson S; Schlink, Bryan R; Hairston, W David; König, Peter; Ferris, Daniel P

2016-06-01

Electroencephalography (EEG) can assess brain activity during whole-body motion in humans but head motion can induce artifacts that obfuscate electrocortical signals. Definitive solutions for removing motion artifact from EEG have yet to be found, so creating methods to assess signal processing routines for removing motion artifact are needed. We present a novel method for investigating the influence of head motion on EEG recordings as well as for assessing the efficacy of signal processing approaches intended to remove motion artifact. We used a phantom head device to mimic electrical properties of the human head with three controlled dipolar sources of electrical activity embedded in the phantom. We induced sinusoidal vertical motions on the phantom head using a custom-built platform and recorded EEG signals with three different acquisition systems while the head was both stationary and in varied motion conditions. Recordings showed up to 80% reductions in signal-to-noise ratio (SNR) and up to 3600% increases in the power spectrum as a function of motion amplitude and frequency. Independent component analysis (ICA) successfully isolated the three dipolar sources across all conditions and systems. There was a high correlation (r > 0.85) and marginal increase in the independent components' (ICs) power spectrum (∼15%) when comparing stationary and motion parameters. The SNR of the IC activation was 400%-700% higher in comparison to the channel data SNR, attenuating the effects of motion on SNR. Our results suggest that the phantom head and motion platform can be used to assess motion artifact removal algorithms and compare different EEG systems for motion artifact sensitivity. In addition, ICA is effective in isolating target electrocortical events and marginally improving SNR in relation to stationary recordings.

SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

Science.gov (United States)

Graichen, Uwe; Eichardt, Roland; Fiedler, Patrique; Strohmeier, Daniel; Zanow, Frank; Haueisen, Jens

2015-01-01

Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

Prevalence and etiology of false normal aEEG recordings in neonatal hypoxic-ischaemic encephalopathy

OpenAIRE

Marics, Gábor; Csekő, Anna; Vásárhelyi, Barna; Zakariás, Dávid; Schuster, György; Szabó, Miklós

2013-01-01

Background Amplitude-integrated electroencephalography (aEEG) is a useful tool to determine the severity of neonatal hypoxic-ischemic encephalopathy (HIE). Our aim was to assess the prevalence and study the origin of false normal aEEG recordings based on 85 aEEG recordings registered before six hours of age. Methods Raw EEG recordings were reevaluated retrospectively with Fourier analysis to identify and describe the frequency patterns of the raw EEG signal, in cases with inconsistent aEEG re...

SVM detection of epileptiform activity in routine EEG.

LENUS (Irish Health Repository)

Kelleher, Daniel

2010-01-01

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

ECG contamination of EEG signals: effect on entropy.

Science.gov (United States)

Chakrabarti, Dhritiman; Bansal, Sonia

2016-02-01

Entropy™ is a proprietary algorithm which uses spectral entropy analysis of electroencephalographic (EEG) signals to produce indices which are used as a measure of depth of hypnosis. We describe a report of electrocardiographic (ECG) contamination of EEG signals leading to fluctuating erroneous Entropy values. An explanation is provided for mechanism behind this observation by describing the spread of ECG signals in head and neck and its influence on EEG/Entropy by correlating the observation with the published Entropy algorithm. While the Entropy algorithm has been well conceived, there are still instances in which it can produce erroneous values. Such erroneous values and their cause may be identified by close scrutiny of the EEG waveform if Entropy values seem out of sync with that expected at given anaesthetic levels.

EEG resting state functional connectivity analysis in children with benign epilepsy with centrotemporal spikes

Directory of Open Access Journals (Sweden)

Azeez eAdebimpe

2016-03-01

Full Text Available In this study, we investigated changes in functional connectivity of the brain networks in patients with benign epilepsy with centrotemporal spikes compared to healthy controls using high-density EEG data collected under eyes-closed resting state condition. EEG source reconstruction was performed with exact Low Resolution Electromagnetic Tomography (eLORETA. We investigated functional connectivity (FC between 84 Brodmann areas using lagged phase synchronization (LPS in four frequency bands (δ, θ, α, and β. We further computed the network degree, clustering coefficient and efficiency. Compared to controls, patients displayed higher θ and α and lower β lagged phase synchronization values. In these frequency bands, patients were also characterized by less well ordered brain networks exhibiting higher global degrees and efficiencies and lower clustering coefficients. In the beta band, patients exhibited reduced functional segregation and integration due to loss of both local and long-distance functional connections. These findings suggest that benign epileptic brain networks might be functionally disrupted due to their altered functional organization especially in the α and β frequency bands.

Quantitative Analysis of the Effects of Slow Wave Sleep Deprivation During the First 3 h of Sleep on Subsequent EEG Power Density

NARCIS (Netherlands)

Dijk, Derk Jan; Beersma, Domien G.M.; Daan, Serge; Bloem, Gerda M.; Hoofdakker, Rutger H. van den

1987-01-01

The relation between EEG power density during slow wave sleep (SWS) deprivation and power density during subsequent sleep was investigated. Nine young male adults slept in the laboratory for 3 consecutive nights. Spectral analysis of the EEG on the 2nd (baseline) night revealed an exponential

Quantitative analysis of sleep EEG microstructure in the time-frequency domain.

Science.gov (United States)

De Carli, Fabrizio; Nobili, Lino; Beelke, Manolo; Watanabe, Tsuyoshi; Smerieri, Arianna; Parrino, Liborio; Terzano, Mario Giovanni; Ferrillo, Franco

2004-06-30

A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time-frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time-frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.

Quantitative change of EEG and respiration signals during mindfulness meditation

Science.gov (United States)

2014-01-01

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

Widespread EEG changes precede focal seizures.

Directory of Open Access Journals (Sweden)

Piero Perucca

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

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

Localization of epileptogenic zones in Lennox–Gastaut syndrome using frequency domain source imaging of intracranial electroencephalography: a preliminary investigation

International Nuclear Information System (INIS)

Cho, Jae-Hyun; Jung, Young-Jin; Kim, Jeong-Youn; Im, Chang-Hwan; Kang, Hoon-Chul; Kim, Heung Dong; Yoon, Dae Sung; Lee, Yong-Ho

2013-01-01

Although intracranial electroencephalography (iEEG) has been widely used to localize epileptogenic zones in epilepsy, visual inspection of iEEG recordings does not always result in a favorable surgical outcome, especially in secondary generalized epilepsy such as Lennox–Gastaut syndrome (LGS). Various computational iEEG analysis methods have recently been introduced to confirm the visual inspection results. Of these methods, high gamma oscillation in iEEG has attracted interest because a series of studies have reported a close relationship between epileptogenic zones and cortical areas with high gamma oscillation. Meanwhile, frequency domain source imaging of EEG and MEG oscillations has proven to be a useful auxiliary tool for identifying rough locations of epileptogenic zones. To the best of our knowledge, however, frequency domain source imaging of high gamma iEEG oscillations has not been studied. In this study, we investigated whether the iEEG-based frequency domain source imaging of high gamma oscillation (60–100 Hz) would be a useful supplementary tool for identifying epileptogenic zones in patients with secondary generalized epilepsy. The method was applied to three successfully operated on LGS patients, whose iEEG contained some ictal events with distinct high gamma oscillations before seizure onset. The resultant cortical source distributions were compared with surgical resection areas and with high gamma spectral power distributions on the intracranial sensor plane. While the results of the sensor-level analyses contained many spurious activities, the results of frequency domain source imaging coincided better with the surgical resection areas, suggesting that the frequency domain source imaging of iEEG high gamma oscillations might help enhance the accuracy of pre-surgical evaluations of patients with secondary generalized epilepsy. (paper)

Automated approach to detecting behavioral states using EEG-DABS

Directory of Open Access Journals (Sweden)

Zachary B. Loris

2017-07-01

Full Text Available Electrocorticographic (ECoG signals represent cortical electrical dipoles generated by synchronous local field potentials that result from simultaneous firing of neurons at distinct frequencies (brain waves. Since different brain waves correlate to different behavioral states, ECoG signals presents a novel strategy to detect complex behaviors. We developed a program, EEG Detection Analysis for Behavioral States (EEG-DABS that advances Fast Fourier Transforms through ECoG signals time series, separating it into (user defined frequency bands and normalizes them to reduce variability. EEG-DABS determines events if segments of an experimental ECoG record have significantly different power bands than a selected control pattern of EEG. Events are identified at every epoch and frequency band and then are displayed as output graphs by the program. Certain patterns of events correspond to specific behaviors. Once a predetermined pattern was selected for a behavioral state, EEG-DABS correctly identified the desired behavioral event. The selection of frequency band combinations for detection of the behavior affects accuracy of the method. All instances of certain behaviors, such as freezing, were correctly identified from the event patterns generated with EEG-DABS. Detecting behaviors is typically achieved by visually discerning unique animal phenotypes, a process that is time consuming, unreliable, and subjective. EEG-DABS removes variability by using defined parameters of EEG/ECoG for a desired behavior over chronic recordings. EEG-DABS presents a simple and automated approach to quantify different behavioral states from ECoG signals.

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

DEFF Research Database (Denmark)

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

1996-01-01

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

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Science.gov (United States)

Bridwell, David A.; Cavanagh, James F.; Collins, Anne G. E.; Nunez, Michael D.; Srinivasan, Ramesh; Stober, Sebastian; Calhoun, Vince D.

2018-01-01

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function. PMID:29632480

Hidden pattern discovery on epileptic EEG with 1-D local binary patterns and epileptic seizures detection by grey relational analysis.

Science.gov (United States)

Kaya, Yılmaz

2015-09-01

This paper proposes a novel approach to detect epilepsy seizures by using Electroencephalography (EEG), which is one of the most common methods for the diagnosis of epilepsy, based on 1-Dimension Local Binary Pattern (1D-LBP) and grey relational analysis (GRA) methods. The main aim of this paper is to evaluate and validate a novel approach, which is a computer-based quantitative EEG analyzing method and based on grey systems, aimed to help decision-maker. In this study, 1D-LBP, which utilizes all data points, was employed for extracting features in raw EEG signals, Fisher score (FS) was employed to select the representative features, which can also be determined as hidden patterns. Additionally, GRA is performed to classify EEG signals through these Fisher scored features. The experimental results of the proposed approach, which was employed in a public dataset for validation, showed that it has a high accuracy in identifying epileptic EEG signals. For various combinations of epileptic EEG, such as A-E, B-E, C-E, D-E, and A-D clusters, 100, 96, 100, 99.00 and 100% were achieved, respectively. Also, this work presents an attempt to develop a new general-purpose hidden pattern determination scheme, which can be utilized for different categories of time-varying signals.

[EEG-markers of vertical postural organization in healthy persons].

Science.gov (United States)

Zhavoronkova, L A; Zharikova, A V; Kushnir, E M; Mikhalkova, A A

2012-01-01

In 10 healthy persons (22.8 +/- 0.67 years) spectral-coherence parameters of EEG were analyzed in different steps of verticalizations--from gorizontal position to seat and stand one. Maximal changes of all EEG parameters were observed in state with absence of visual control. We observed an increase of power for fast spectral bands of EEG (beta- and gamma-bands) in all conditions and additional increase of these EEG parameters was observed at situation of complication of conditions of vertical pose supporting. Results of EEG coherent analysis in conditions of human verticalization showed specific increase of coherence for the majority of rhythm ranges in the right hemisphere especially in the central-frontal and in occipital-parietal areas and for interhemispheric pairs for these leads. This fact can reflect participation of cortical as well as subcortical structures in these processes. In conditions of complicate conditions of vertical pose supporting the additional increase of EEG coherence in fast bands (beta-rhythm) was observed at the frontal areas. This fact can testify about increasing of executive functions in this conditions.

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

International Nuclear Information System (INIS)

Krueger, J.

1983-01-01

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

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.

Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction

NARCIS (Netherlands)

de Munck, J.C.; van Houdt, P.J.; Goncalves, S.I.; van Wegen, E.E.H.; Ossenblok, P.P.W.

2013-01-01

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of

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

Science.gov (United States)

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

2013-11-01

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

How to make an EEG allocation. The EEG allocation is not decided by solar growth rates but by politicians; Wir basteln uns eine EEG-Umlage. Wie hoch die EEG-Umlage wird, haengt weniger am Solarzubau, sondern ist eine politische Entscheidung

Energy Technology Data Exchange (ETDEWEB)

Welter, Philippe

2012-09-15

In political discussions, the EEG allocation is still viewed as a reference point for the cost of financial incentives for renewable energy sources. However, there are many contributing factors of which the cost of renewables is only one. The most important aspects are privileges for industrial consumers, liquidity reserves, and stock exchange electricity prices. The contribution goes into detail.

FACET – a “Flexible Artifact Correction and Evaluation Toolbox” for concurrently recorded EEG/fMRI data

Science.gov (United States)

2013-01-01

Background In concurrent EEG/fMRI recordings, EEG data are impaired by the fMRI gradient artifacts which exceed the EEG signal by several orders of magnitude. While several algorithms exist to correct the EEG data, these algorithms lack the flexibility to either leave out or add new steps. The here presented open-source MATLAB toolbox FACET is a modular toolbox for the fast and flexible correction and evaluation of imaging artifacts from concurrently recorded EEG datasets. It consists of an Analysis, a Correction and an Evaluation framework allowing the user to choose from different artifact correction methods with various pre- and post-processing steps to form flexible combinations. The quality of the chosen correction approach can then be evaluated and compared to different settings. Results FACET was evaluated on a dataset provided with the FMRIB plugin for EEGLAB using two different correction approaches: Averaged Artifact Subtraction (AAS, Allen et al., NeuroImage 12(2):230–239, 2000) and the FMRI Artifact Slice Template Removal (FASTR, Niazy et al., NeuroImage 28(3):720–737, 2005). Evaluation of the obtained results were compared to the FASTR algorithm implemented in the EEGLAB plugin FMRIB. No differences were found between the FACET implementation of FASTR and the original algorithm across all gradient artifact relevant performance indices. Conclusion The FACET toolbox not only provides facilities for all three modalities: data analysis, artifact correction as well as evaluation and documentation of the results but it also offers an easily extendable framework for development and evaluation of new approaches. PMID:24206927

The quantity does it? The EEG 2017 and the consequences for the German energy transition; Menge macht's? Das EEG 2017 und die Folgen fuer die deutsche Energiewende

Energy Technology Data Exchange (ETDEWEB)

Meya, Jasper N.; Neetzow, Paul [Oldenburg Univ. (Germany); Humboldt Univ., Berlin (Germany); Neubauer, Linda; Pechan, Anna [Oldenburg Univ. (Germany)

2016-11-15

The Renewable Energy Sources Act (EEG) has so far been the driving force behind German energy transition and has found many imitators internationally as an effective funding instrument. With the reform of the EEG, which was approved by the Federal Government, the promotion of renewable energies (EE) was shifted from the current price to a quantity control. The present analysis of the impact of the switch to cost-efficiency, the diversity of actors, the achievement of the EE production targets and network consistency shows that the efficiency advantages of tendering may be reduced by higher transaction costs and regional expansion ambitions dampened. The tendered quantities are not sufficient for the long term to increase EE share to the targeted level. It is also doubtful whether a grid-compatible EE expansion is achieved. [German] Das Erneuerbare-Energien-Gesetz (EEG) war bislang der Motor der deutschen Energiewende und hat als effektives Foerderinstrument international viele Nachahmer gefunden. Mit der von der Bundesregierung beschlossenen Reform des EEG wird die Foerderung der erneuerbaren Energien (EE) von der bisherigen Preis- auf eine Mengensteuerung umgestellt. Die vorliegende Analyse der Auswirkungen der Umstellung auf Kosteneffizienz, Akteursvielfalt, Erreichen der EE-Erzeugungsziele und Netzkonformitaet zeigt, dass die Effizienzvorteile der Ausschreibungen ggf. durch hoehere Transaktionskosten verringert und regionale Ausbauambitionen gedaempft werden. Die ausgeschriebenen Mengen reichen langfristig nicht aus, um den EE-Anteil zielkonform zu erhoehen. Es ist zudem fraglich, ob ein netzkonformer EE-Ausbau erreicht wird.

How about a Bayesian M/EEG imaging method correcting for incomplete spatio-temporal priors

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai T.; Sekihara, Kensuke

2013-01-01

previous spatio-temporal inverse M/EEG models, the proposed model benefits of consisting of two source terms, namely, a spatio-temporal pattern term limiting the source configuration to a spatio-temporal subspace and a source correcting term to pick up source activity not covered by the spatio......-temporal prior belief. We have tested the model on both artificial data and real EEG data in order to demonstrate the efficacy of the model. The model was tested at different SNRs (-10.0,-5.2, -3.0, -1.0, 0, 0.8, 3.0 dB) using white noise. At all SNRs the sAquavit performs best in AUC measure, e.g. at SNR=0d...

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

Science.gov (United States)

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

2016-03-01

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

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

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

2017-04-01

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

Mixed-norm estimates for the M/EEG inverse problem using accelerated gradient methods.

Science.gov (United States)

Gramfort, Alexandre; Kowalski, Matthieu; Hämäläinen, Matti

2012-04-07

Magneto- and electroencephalography (M/EEG) measure the electromagnetic fields produced by the neural electrical currents. Given a conductor model for the head, and the distribution of source currents in the brain, Maxwell's equations allow one to compute the ensuing M/EEG signals. Given the actual M/EEG measurements and the solution of this forward problem, one can localize, in space and in time, the brain regions that have produced the recorded data. However, due to the physics of the problem, the limited number of sensors compared to the number of possible source locations, and measurement noise, this inverse problem is ill-posed. Consequently, additional constraints are needed. Classical inverse solvers, often called minimum norm estimates (MNE), promote source estimates with a small ℓ₂ norm. Here, we consider a more general class of priors based on mixed norms. Such norms have the ability to structure the prior in order to incorporate some additional assumptions about the sources. We refer to such solvers as mixed-norm estimates (MxNE). In the context of M/EEG, MxNE can promote spatially focal sources with smooth temporal estimates with a two-level ℓ₁/ℓ₂ mixed-norm, while a three-level mixed-norm can be used to promote spatially non-overlapping sources between different experimental conditions. In order to efficiently solve the optimization problems of MxNE, we introduce fast first-order iterative schemes that for the ℓ₁/ℓ₂ norm give solutions in a few seconds making such a prior as convenient as the simple MNE. Furthermore, thanks to the convexity of the optimization problem, we can provide optimality conditions that guarantee global convergence. The utility of the methods is demonstrated both with simulations and experimental MEG data.

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,

EEG in connection with coma.

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Wilson, John A; Nordal, Helge J

2013-01-08

Coma is a dynamic condition that may have various causes. Important changes may take place rapidly, often with consequences for treatment. The purpose of this article is to provide a brief overview of EEG patterns in comas with various causes, and indicate how EEG contributes in an assessment of the prognosis for coma patients. The article is based on many years of clinical and research-based experience of EEG used for patients in coma. A self-built reference database was supplemented by searches for relevant articles in PubMed. EEG reveals immediate changes in coma, and can provide early information on cause and prognosis. It is the only diagnostic tool for detecting a non-convulsive epileptic status. Locked-in- syndrome may be overseen without EEG. Repeated EEG scans increase diagnostic certainty and make it possible to monitor the development of coma. EEG reflects brain function continuously and therefore holds a key place in the assessment and treatment of coma.

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

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

2018-02-01

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

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

OpenAIRE

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

2014-01-01

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

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

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

2018-04-01

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

Video-EEG epilepsian diagnostiikassa - milloin ja miksi?

OpenAIRE

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

2009-01-01

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

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

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

2002-01-30

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

Tele-transmission of EEG recordings.

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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.

Brain Network Analysis from High-Resolution EEG Signals

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de Vico Fallani, Fabrizio; Babiloni, Fabio

lattice and a random structure. Such a model has been designated as "small-world" network in analogy with the concept of the small-world phenomenon observed more than 30 years ago in social systems. In a similar way, many types of functional brain networks have been analyzed according to this mathematical approach. In particular, several studies based on different imaging techniques (fMRI, MEG and EEG) have found that the estimated functional networks showed small-world characteristics. In the functional brain connectivity context, these properties have been demonstrated to reflect an optimal architecture for the information processing and propagation among the involved cerebral structures. However, the performance of cognitive and motor tasks as well as the presence of neural diseases has been demonstrated to affect such a small-world topology, as revealed by the significant changes of L and C. Moreover, some functional brain networks have been mostly found to be very unlike the random graphs in their degree-distribution, which gives information about the allocation of the functional links within the connectivity pattern. It was demonstrated that the degree distributions of these networks follow a power-law trend. For this reason those networks are called "scale-free". They still exhibit the small-world phenomenon but tend to contain few nodes that act as highly connected "hubs". Scale-free networks are known to show resistance to failure, facility of synchronization and fast signal processing. Hence, it would be important to see whether the scaling properties of the functional brain networks are altered under various pathologies or experimental tasks. The present Chapter proposes a theoretical graph approach in order to evaluate the functional connectivity patterns obtained from high-resolution EEG signals. In this way, the "Brain Network Analysis" (in analogy with the Social Network Analysis that has emerged as a key technique in modern sociology) represents an

Correlation of EEG with neuropsychological status in children with epilepsy.

Science.gov (United States)

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

2016-02-01

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

Electroencephalogy (EEG) Feedback in Decision-Making

Science.gov (United States)

2015-08-26

Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful

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

NARCIS (Netherlands)

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

2017-01-01

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

All-in-one interictal presurgical imaging in patients with epilepsy: single-session EEG/PET/(f)MRI

Energy Technology Data Exchange (ETDEWEB)

Grouiller, Frederic; Delattre, Benedicte M.A.; Lazeyras, Francois; Ratib, Osman; Vargas, Maria I.; Garibotto, Valentina [Geneva University Hospital, Department of Radiology and Medical Informatics, Geneva 14 (Switzerland); Pittau, Francesca; Spinelli, Laurent; Seeck, Margitta; Vulliemoz, Serge [Geneva University Hospital, EEG and Epilepsy Unit, Department of Neurology, Geneva 14 (Switzerland); Heinzer, Susanne [Philips AG Healthcare, Zuerich (Switzerland); Iannotti, Giannina R. [Geneva University Hospital, Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Geneva 14 (Switzerland)

2015-04-17

In patients with pharmacoresistant focal epilepsy, resection of the epileptic focus can lead to freedom from seizures or significant improvement in well-selected candidates. Localization of the epileptic focus with multimodal concordance is crucial for a good postoperative outcome. Beyond the detection of epileptogenic lesions on structural MRI and focal hypometabolism on FDG PET, EEG-based Electric Source Imaging (ESI) and simultaneous EEG and functional MRI (EEG-fMRI) are increasingly applied for mapping epileptic activity. We here report presurgical multimodal interictal imaging using a hybrid PET/MR scanner for single-session FDG PET, MRI, EEG-fMRI and ESI. This quadrimodal imaging procedure was performed in a single session in 12 patients using a high-density (256 electrodes) MR-compatible EEG system and a hybrid PET/MR scanner. EEG was used to exclude subclinical seizures during uptake of the PET tracer, to compute ESI on interictal epileptiform discharges and to guide fMRI analysis for mapping haemodynamic changes correlated with interictal epileptiform activity. The whole multimodal recording was performed in less than 2 hours with good patient comfort and data quality. Clinically contributory examinations with at least two modalities were obtained in nine patients and with all modalities in five patients. This single-session quadrimodal imaging procedure provided reliable and contributory interictal clinical data. This procedure avoids multiple scanning sessions and is associated with less radiation exposure than PET-CT. Moreover, it guarantees the same medication level and medical condition for all modalities. The procedure improves workflow and could reduce the duration and cost of presurgical epilepsy evaluations. (orig.)

All-in-one interictal presurgical imaging in patients with epilepsy: single-session EEG/PET/(f)MRI

International Nuclear Information System (INIS)

Grouiller, Frederic; Delattre, Benedicte M.A.; Lazeyras, Francois; Ratib, Osman; Vargas, Maria I.; Garibotto, Valentina; Pittau, Francesca; Spinelli, Laurent; Seeck, Margitta; Vulliemoz, Serge; Heinzer, Susanne; Iannotti, Giannina R.

2015-01-01

In patients with pharmacoresistant focal epilepsy, resection of the epileptic focus can lead to freedom from seizures or significant improvement in well-selected candidates. Localization of the epileptic focus with multimodal concordance is crucial for a good postoperative outcome. Beyond the detection of epileptogenic lesions on structural MRI and focal hypometabolism on FDG PET, EEG-based Electric Source Imaging (ESI) and simultaneous EEG and functional MRI (EEG-fMRI) are increasingly applied for mapping epileptic activity. We here report presurgical multimodal interictal imaging using a hybrid PET/MR scanner for single-session FDG PET, MRI, EEG-fMRI and ESI. This quadrimodal imaging procedure was performed in a single session in 12 patients using a high-density (256 electrodes) MR-compatible EEG system and a hybrid PET/MR scanner. EEG was used to exclude subclinical seizures during uptake of the PET tracer, to compute ESI on interictal epileptiform discharges and to guide fMRI analysis for mapping haemodynamic changes correlated with interictal epileptiform activity. The whole multimodal recording was performed in less than 2 hours with good patient comfort and data quality. Clinically contributory examinations with at least two modalities were obtained in nine patients and with all modalities in five patients. This single-session quadrimodal imaging procedure provided reliable and contributory interictal clinical data. This procedure avoids multiple scanning sessions and is associated with less radiation exposure than PET-CT. Moreover, it guarantees the same medication level and medical condition for all modalities. The procedure improves workflow and could reduce the duration and cost of presurgical epilepsy evaluations. (orig.)

A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

Directory of Open Access Journals (Sweden)

Iman Veisi

2010-03-01

Full Text Available Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is contaminated with noise is investigated and compared with some traditional chaos-based measures. Materials and Methods: In the proposed method, the phase space of the time series is reconstructed and then partitioned using ordinal patterns. The partitions can be labeled using a set of symbols. Therefore, the state trajectory is converted to a symbol sequence. A finite state machine is then constructed to model the sequence. A new complexity measure is proposed to detect dynamical changes using the state transition matrix of the state machine. The proposed complexity measure was applied to detect epilepsy in short and noisy EEG signals and the results were compared with some chaotic measures. Results: The results indicate that this complexity measure can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 75% for highly noised EEG signals. Discussion and Conclusion: The complexity measure can be computed in a very fast and easy way and, unlike traditional chaotic measures, is robust with respect to noise corrupting the data. This measure is also capable of dynamical change detection in short time series data.

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

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Flasbeck, Vera; Popkirov, Stoyan; Brüne, Martin

2017-01-01

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

A Spiking Neural Network Methodology and System for Learning and Comparative Analysis of EEG Data From Healthy Versus Addiction Treated Versus Addiction Not Treated Subjects.

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Doborjeh, Maryam Gholami; Wang, Grace Y; Kasabov, Nikola K; Kydd, Robert; Russell, Bruce

2016-09-01

This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain data. As a case study, the method was applied to electroencephalography (EEG) data collected during a GO/NOGO cognitive task performed by untreated opiate addicts, those undergoing methadone maintenance treatment (MMT) for opiate dependence and a healthy control group. the method is based on an SNN architecture called NeuCube, trained on spatiotemporal EEG data. NeuCube was used to classify EEG data across subject groups and across GO versus NOGO trials, but also facilitated a deeper comparative analysis of the dynamic brain processes. This analysis results in a better understanding of human brain functioning across subject groups when performing a cognitive task. In terms of the EEG data classification, a NeuCube model obtained better results (the maximum obtained accuracy: 90.91%) when compared with traditional statistical and artificial intelligence methods (the maximum obtained accuracy: 50.55%). more importantly, new information about the effects of MMT on cognitive brain functions is revealed through the analysis of the SNN model connectivity and its dynamics. this paper presented a new method for EEG data modeling and revealed new knowledge on brain functions associated with mental activity which is different from the brain activity observed in a resting state of the same subjects.

Study on Brain Dynamics by Non Linear Analysis of Music Induced EEG Signals

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Banerjee, Archi; Sanyal, Shankha; Patranabis, Anirban; Banerjee, Kaushik; Guhathakurta, Tarit; Sengupta, Ranjan; Ghosh, Dipak; Ghose, Partha

2016-02-01

Music has been proven to be a valuable tool for the understanding of human cognition, human emotion, and their underlying brain mechanisms. The objective of this study is to analyze the effect of Hindustani music on brain activity during normal relaxing conditions using electroencephalography (EEG). Ten male healthy subjects without special musical education participated in the study. EEG signals were acquired at the frontal (F3/F4) lobes of the brain while listening to music at three experimental conditions (rest, with music and without music). Frequency analysis was done for the alpha, theta and gamma brain rhythms. The finding shows that arousal based activities were enhanced while listening to Hindustani music of contrasting emotions (romantic/sorrow) for all the subjects in case of alpha frequency bands while no significant changes were observed in gamma and theta frequency ranges. It has been observed that when the music stimulus is removed, arousal activities as evident from alpha brain rhythms remain for some time, showing residual arousal. This is analogous to the conventional 'Hysteresis' loop where the system retains some 'memory' of the former state. This is corroborated in the non linear analysis (Detrended Fluctuation Analysis) of the alpha rhythms as manifested in values of fractal dimension. After an input of music conveying contrast emotions, withdrawal of music shows more retention as evidenced by the values of fractal dimension.

Removal of the ballistocardiographic artifact from EEG-fMRI data: a canonical correlation approach

International Nuclear Information System (INIS)

Assecondi, Sara; Hallez, Hans; Staelens, Steven; Lemahieu, Ignace; Bianchi, Anna M; Huiskamp, Geertjan M

2009-01-01

The simultaneous recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) can give new insights into how the brain functions. However, the strong electromagnetic field of the MR scanner generates artifacts that obscure the EEG and diminish its readability. Among them, the ballistocardiographic artifact (BCGa) that appears on the EEG is believed to be related to blood flow in scalp arteries leading to electrode movements. Average artifact subtraction (AAS) techniques, used to remove the BCGa, assume a deterministic nature of the artifact. This assumption may be too strong, considering the blood flow related nature of the phenomenon. In this work we propose a new method, based on canonical correlation analysis (CCA) and blind source separation (BSS) techniques, to reduce the BCGa from simultaneously recorded EEG-fMRI. We optimized the method to reduce the user's interaction to a minimum. When tested on six subjects, recorded in 1.5 T or 3 T, the average artifact extracted with BSS-CCA and AAS did not show significant differences, proving the absence of systematic errors. On the other hand, when compared on the basis of intra-subject variability, we found significant differences and better performance of the proposed method with respect to AAS. We demonstrated that our method deals with the intrinsic subject variability specific to the artifact that may cause averaging techniques to fail.

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

Science.gov (United States)

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

2017-01-01

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

EEG current source density and the phenomenology of the default network.

Science.gov (United States)

Cannon, Rex L; Baldwin, Debora R

2012-10-01

In recent years, there has been an increasing line of research dedicated to the investigation of the default mode network (DMN) of the brain and resting state networks. However, the mental activity of the DMN has not been rigorously assessed to date. The specific aims of the current study were 2-fold: First, we sought to determine whether the current source density (CSD) levels in the DMN would correspond to other neuroimaging techniques. Second, we sought to understand the subjective mental activity of the DMN during baseline recordings. This study was conducted with 63 nonclinical participants, 34 female and 29 males with a mean age of 19.2 years (standard deviation = 2.0). The participants were recorded in 8 conditions. First, 4-minute eyes-closed baseline (ECB) and eyes-opened baseline (EOB) were obtained. The participants then completed 3 assessment instruments and 3 image conditions while the electroencephalography (EEG) was continuously recorded. Participants completed subjective reports for baselines and image conditions. These were rated by 3 independent raters and compared for reliability using a random effects model with an absolute agreement definition. The mean CSD between all conditions differed significantly, in many but not all regions of interest in the DMN. Interestingly, as suggested by other studies, the DMN appears preferential to self-relevant, self-specific, or self-perceptive processes. The reliability analyses show α for interrater agreement for ECB at .95 and EOB at .96. The subjective reports obtained from the participants regarding the mental activities employed during baseline recordings correspond to attentional and self-regulatory processes, which may also implicate the resting state or DMN as playing a direct role in the maintenance of a complex behavior (eg, being still, attending, and self-regulating). Thus, attention and self-regulation constitute the phenomenology of the resting state (DMN) in this study. The results also

EEG activity in Muslim prayer: A pilot study

Directory of Open Access Journals (Sweden)

Haider H. Alwasiti

2010-12-01

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

Educational simulation of the electroencephalogram (EEG)

NARCIS (Netherlands)

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

2001-01-01

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

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

Science.gov (United States)

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

2014-10-15

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

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

DEFF Research Database (Denmark)

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

2016-01-01

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

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

Directory of Open Access Journals (Sweden)

Galina V. Portnova

2018-01-01

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

Only low frequency event-related EEG activity is compromised in multiple sclerosis: insights from an independent component clustering analysis.

Directory of Open Access Journals (Sweden)

Hanni Kiiski

Full Text Available Cognitive impairment (CI, often examined with neuropsychological tests such as the Paced Auditory Serial Addition Test (PASAT, affects approximately 65% of multiple sclerosis (MS patients. The P3b event-related potential (ERP, evoked when an infrequent target stimulus is presented, indexes cognitive function and is typically compared across subjects' scalp electroencephalography (EEG data. However, the clustering of independent components (ICs is superior to scalp-based EEG methods because it can accommodate the spatiotemporal overlap inherent in scalp EEG data. Event-related spectral perturbations (ERSPs; event-related mean power spectral changes and inter-trial coherence (ITCs; event-related consistency of spectral phase reveal a more comprehensive overview of EEG activity. Ninety-five subjects (56 MS patients, 39 controls completed visual and auditory two-stimulus P3b event-related potential tasks and the PASAT. MS patients were also divided into CI and non-CI groups (n = 18 in each based on PASAT scores. Data were recorded from 128-scalp EEG channels and 4 IC clusters in the visual, and 5 IC clusters in the auditory, modality were identified. In general, MS patients had significantly reduced ERSP theta power versus controls, and a similar pattern was observed for CI vs. non-CI MS patients. The ITC measures were also significantly different in the theta band for some clusters. The finding that MS patients had reduced P3b task-related theta power in both modalities is a reflection of compromised connectivity, likely due to demyelination, that may have disrupted early processes essential to P3b generation, such as orientating and signal detection. However, for posterior sources, MS patients had a greater decrease in alpha power, normally associated with enhanced cognitive function, which may reflect a compensatory mechanism in response to the compromised early cognitive processing.

The New York Head-A precise standardized volume conductor model for EEG source localization and tES targeting.

Science.gov (United States)

Huang, Yu; Parra, Lucas C; Haufe, Stefan

2016-10-15

In source localization of electroencephalograpic (EEG) signals, as well as in targeted transcranial electric current stimulation (tES), a volume conductor model is required to describe the flow of electric currents in the head. Boundary element models (BEM) can be readily computed to represent major tissue compartments, but cannot encode detailed anatomical information within compartments. Finite element models (FEM) can capture more tissue types and intricate anatomical structures, but with the higher precision also comes the need for semi-automated segmentation, and a higher computational cost. In either case, adjusting to the individual human anatomy requires costly magnetic resonance imaging (MRI), and thus head modeling is often based on the anatomy of an 'arbitrary' individual (e.g. Colin27). Additionally, existing reference models for the human head often do not include the cerebro-spinal fluid (CSF), and their field of view excludes portions of the head and neck-two factors that demonstrably affect current-flow patterns. Here we present a highly detailed FEM, which we call ICBM-NY, or "New York Head". It is based on the ICBM152 anatomical template (a non-linear average of the MRI of 152 adult human brains) defined in MNI coordinates, for which we extended the field of view to the neck and performed a detailed segmentation of six tissue types (scalp, skull, CSF, gray matter, white matter, air cavities) at 0.5mm(3) resolution. The model was solved for 231 electrode locations. To evaluate its performance, additional FEMs and BEMs were constructed for four individual subjects. Each of the four individual FEMs (regarded as the 'ground truth') is compared to its BEM counterpart, the ICBM-NY, a BEM of the ICBM anatomy, an 'individualized' BEM of the ICBM anatomy warped to the individual head surface, and FEMs of the other individuals. Performance is measured in terms of EEG source localization and tES targeting errors. Results show that the ICBM-NY outperforms

The quantity does it? The EEG 2017 and the consequences for the German energy transition

International Nuclear Information System (INIS)

Meya, Jasper N.; Neetzow, Paul; Neubauer, Linda; Pechan, Anna

2016-01-01

The Renewable Energy Sources Act (EEG) has so far been the driving force behind German energy transition and has found many imitators internationally as an effective funding instrument. With the reform of the EEG, which was approved by the Federal Government, the promotion of renewable energies (EE) was shifted from the current price to a quantity control. The present analysis of the impact of the switch to cost-efficiency, the diversity of actors, the achievement of the EE production targets and network consistency shows that the efficiency advantages of tendering may be reduced by higher transaction costs and regional expansion ambitions dampened. The tendered quantities are not sufficient for the long term to increase EE share to the targeted level. It is also doubtful whether a grid-compatible EE expansion is achieved. [de

An EEGLAB plugin to analyze individual EEG alpha rhythms using the "channel reactivity-based method".

Science.gov (United States)

Goljahani, A; Bisiacchi, P; Sparacino, G

2014-03-01

A recent paper [1] proposed a new technique, termed the channel reactivity-based method (CRB), for characterizing EEG alpha rhythms using individual (IAFs) and channel (CAFs) alpha frequencies. These frequencies were obtained by identifying the frequencies at which the power of the alpha rhythms decreases. In the present study, we present a graphical interactive toolbox that can be plugged into the popular open source environment EEGLAB, making it easy to use CRB. In particular, we illustrate the major functionalities of the software and discuss the advantages of this toolbox for common EEG investigations. The CRB analysis plugin, along with extended documentation and the sample dataset utilized in this study, is freely available on the web at http://bio.dei.unipd.it/crb/. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Presurgical EEG-fMRI in a complex clinical case with seizure recurrence after epilepsy surgery

Science.gov (United States)

Zhang, Jing; Liu, Qingzhu; Mei, Shanshan; Zhang, Xiaoming; Wang, Xiaofei; Liu, Weifang; Chen, Hui; Xia, Hong; Zhou, Zhen; Li, Yunlin

2013-01-01

Epilepsy surgery has improved over the last decade, but non-seizure-free outcome remains at 10%–40% in temporal lobe epilepsy (TLE) and 40%–60% in extratemporal lobe epilepsy (ETLE). This paper reports a complex multifocal case. With a normal magnetic resonance imaging (MRI) result and nonlocalizing electroencephalography (EEG) findings (bilateral TLE and ETLE, with more interictal epileptiform discharges [IEDs] in the right frontal and temporal regions), a presurgical EEG-functional MRI (fMRI) was performed before the intraoperative intracranial EEG (icEEG) monitoring (icEEG with right hemispheric coverage). Our previous EEG-fMRI analysis results (IEDs in the left hemisphere alone) were contradictory to the EEG and icEEG findings (IEDs in the right frontal and temporal regions). Thus, the EEG-fMRI data were reanalyzed with newly identified IED onsets and different fMRI model options. The reanalyzed EEG-fMRI findings were largely concordant with those of EEG and icEEG, and the failure of our previous EEG-fMRI analysis may lie in the inaccurate identification of IEDs and wrong usage of model options. The right frontal and temporal regions were resected in surgery, and dual pathology (hippocampus sclerosis and focal cortical dysplasia in the extrahippocampal region) was found. The patient became seizure-free for 3 months, but his seizures restarted after antiepileptic drugs (AEDs) were stopped. The seizures were not well controlled after resuming AEDs. Postsurgical EEGs indicated that ictal spikes in the right frontal and temporal regions reduced, while those in the left hemisphere became prominent. This case suggested that (1) EEG-fMRI is valuable in presurgical evaluation, but requires caution; and (2) the intact seizure focus in the remaining brain may cause the non-seizure-free outcome. PMID:23926432

An Evaluation of EEG Scanner’s Dependence on the Imaging Technique, Forward Model Computation Method, and Array Dimensionality

DEFF Research Database (Denmark)

Stahlhut, Carsten; Attias, Hagai Thomas; Stopczynski, Arkadiusz

2012-01-01

EEG source reconstruction involves solving an inverse problem that is highly ill-posed and dependent on a generally fixed forward propagation model. In this contribution we compare a low and high density EEG setup’s dependence on correct forward modeling. Specifically, we examine how different...... forward models affect the source estimates obtained using four inverse solvers Minimum-Norm, LORETA, Minimum-Variance Adaptive Beamformer, and Sparse Bayesian Learning....

Mobile real-time EEG imaging Bayesian inference with sparse, temporally smooth source priors

DEFF Research Database (Denmark)

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

2013-01-01

EEG based real-time imaging of human brain function has many potential applications including quality control, in-line experimental design, brain state decoding, and neuro-feedback. In mobile applications these possibilities are attractive as elements in systems for personal state monitoring...

Reliability of fully automated versus visually controlled pre- and post-processing of resting-state EEG.

Science.gov (United States)

Hatz, F; Hardmeier, M; Bousleiman, H; Rüegg, S; Schindler, C; Fuhr, P

2015-02-01

To compare the reliability of a newly developed Matlab® toolbox for the fully automated, pre- and post-processing of resting state EEG (automated analysis, AA) with the reliability of analysis involving visually controlled pre- and post-processing (VA). 34 healthy volunteers (age: median 38.2 (20-49), 82% female) had three consecutive 256-channel resting-state EEG at one year intervals. Results of frequency analysis of AA and VA were compared with Pearson correlation coefficients, and reliability over time was assessed with intraclass correlation coefficients (ICC). Mean correlation coefficient between AA and VA was 0.94±0.07, mean ICC for AA 0.83±0.05 and for VA 0.84±0.07. AA and VA yield very similar results for spectral EEG analysis and are equally reliable. AA is less time-consuming, completely standardized, and independent of raters and their training. Automated processing of EEG facilitates workflow in quantitative EEG analysis. Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination

Directory of Open Access Journals (Sweden)

Todd Zorick

2016-01-01

Full Text Available Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014, the concept of information equilibrium (IE in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally.

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

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Gilney Figueira Zebende

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

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.

Preictal dynamics of EEG complexity in intracranially recorded epileptic seizure: a case report.

Science.gov (United States)

Bob, Petr; Roman, Robert; Svetlak, Miroslav; Kukleta, Miloslav; Chladek, Jan; Brazdil, Milan

2014-11-01

Recent findings suggest that neural complexity reflecting a number of independent processes in the brain may characterize typical changes during epileptic seizures and may enable to describe preictal dynamics. With respect to previously reported findings suggesting specific changes in neural complexity during preictal period, we have used measure of pointwise correlation dimension (PD2) as a sensitive indicator of nonstationary changes in complexity of the electroencephalogram (EEG) signal. Although this measure of complexity in epileptic patients was previously reported by Feucht et al (Applications of correlation dimension and pointwise dimension for non-linear topographical analysis of focal onset seizures. Med Biol Comput. 1999;37:208-217), it was not used to study changes in preictal dynamics. With this aim to study preictal changes of EEG complexity, we have examined signals from 11 multicontact depth (intracerebral) EEG electrodes located in 108 cortical and subcortical brain sites, and from 3 scalp EEG electrodes in a patient with intractable epilepsy, who underwent preoperative evaluation before epilepsy surgery. From those 108 EEG contacts, records related to 44 electrode contacts implanted into lesional structures and white matter were not included into the experimental analysis.The results show that in comparison to interictal period (at about 8-6 minutes before seizure onset), there was a statistically significant decrease in PD2 complexity in the preictal period at about 2 minutes before seizure onset in all 64 intracranial channels localized in various brain sites that were included into the analysis and in 3 scalp EEG channels as well. Presented results suggest that using PD2 in EEG analysis may have significant implications for research of preictal dynamics and prediction of epileptic seizures.