From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

Studer, D.; Hoffmann, Ulrich; Koenig, Thomas

doi:10.1016/j.jneumeth.2005.11.006

Cited by 24 publications

(15 citation statements)

References 52 publications

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“…However, other researchers have criticized this approach, as the use of wavelets may alleviate but not eliminate non-stationarities in the data. Based on such criticisms, the methodology has been extended to include dictionary learning algorithms, which tailor the oscillatory elements used to decompose the data such that they optimally cover timefrequency ranges that are quasi-stationary (Studer et al, 2006).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

“…In this case, EEG is regarded as the superposition and sequence of synchronous oscillations of potentially widespread networks at specific frequencies. When such methods are augmented by dictionary-learning procedures, one may again obtain a discrete set of transiently active functional brain states, which are now defined in time, frequency, and phase Studer et al, 2006). Interestingly, a recent study based on combined EEG and fMRI measurements indicated that the activity of particular transient networks integrated by synchronous oscillations of cortical neurons was correlated with the BOLD signal in particular subregions of the thalamus in a frequency-specific manner (Schwab et al, 2015).…”

Section: Open Questions and Outlookmentioning

confidence: 99%

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EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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SummaryThe present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of largescale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales.The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

Section: Open Questions and Outlookmentioning

confidence: 99%

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

2018

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“…Averaging the energy in the TF plane reveals activity that is not strictly phase locked from one trial to the other. A similar approach has been performed for multichannel data (Studer et al, 2006), and a related study has proposed to maximize the average amplitude (Tropp et al, 2006):…”

Section: Induced Activity Matching Pursuit (Imp)mentioning

confidence: 99%

Consensus Matching Pursuit for multi-trial EEG signals

Bénar

Papadopoulo

Torrésani

et al. 2009

Journal of Neuroscience Methods

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“…Even the signals from a single intermittent source will simultaneously affect all the electrode recordings. Thus time delays between electrodes cannot be accounted for by a single intermittent source [93] [94]. If the single source activity was conducted through a distributed lead field its intermittent activation patterns would also be volume conducted to several of scalp electrodes.…”

Section: Investigating the Effect Of Volume Conduction On Synchrostatmentioning

confidence: 99%

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

Jamal

Das

Maharatna

et al. 2015

Physica A: Statistical Mechanics and its Applications

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Please cite this article as: W. Jamal, S. Das, K. Maharatna, I. Pan, D. Kuyucu, Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks, Physica A (2015), http://dx.doi.org/10.1016/j.physa. 2015.03.087 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract:Degree of phase synchronization between different Electroencephalogram (EEG) channels is known to be the manifestation of the underlying mechanism of information coupling between different brain regions. In this paper, we apply a continuous wavelet transform (CWT) based analysis technique on EEG data, captured during face perception tasks, to explore the temporal evolution of phase synchronization, from the onset of a stimulus. Our explorations show that there exists a small set (typically 3 -5) of unique synchronized patterns or synchrostates, each of which are stable of the order of milliseconds. Particularly, in the beta (β) band, which has been reported to be associated with visual processing task, the number of such stable states has been found to be three consistently. During processing of the stimulus, the switching between these states occurs abruptly but the switching characteristic follows a well-behaved and repeatable sequence. This is observed in a single subject analysis as well as a multiple-subject group-analysis in adults during face perception. We also show that although these patterns remain topographically similar for the general category of face perception task, the sequence of their occurrence and their temporal stability varies markedly between different face perception scenarios (stimuli) indicating towards different dynamical characteristics for information processing, which is stimulus-specific in nature. Subsequently, we translated these stable states into brain complex networks and derived some informative network measures for characterizing the degree of segregated processing and information integration in those synchrostates, leading to a new methodology for characterizing information processing in human brain. The proposed methodology of modelling the functional brain connectivity through these synchrostates may be viewed as a new way of quantitative characterization of the cognitive ability of the subject, stimuli and information integration/segregation capability.

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From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

Cited by 24 publications

References 52 publications

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

Consensus Matching Pursuit for multi-trial EEG signals

Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks

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