On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics

Congedo, Marco; Gouy-Pailler, Cédric; Jutten, Christian

doi:10.1016/j.clinph.2008.09.007

Cited by 120 publications

(128 citation statements)

References 104 publications

(154 reference statements)

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“…Note that model (1) describes the instantaneous (in-phase) diffusion of current source over measurement sites, in fact describing the effect of direct current and volume conduction (Congedo et al 2008). Our source estimation is given by ˆ( )…”

Section: Group Blind Source Separationmentioning

confidence: 99%

“…In this work we use a method based on the approximate joint diagonalization (AJD) of Fourier cospectral matrices, which is a robust and computationally fast approach (Congedo et al 2008). We diagonalize grand-average cospectral matrices in the frequency range 0.5-30 Hz only, which is the range with highest signal-to-noise ratio.…”

Section: Group Blind Source Separationmentioning

confidence: 99%

“…This approach is analogous to the averaging group ICA approach described for fMRI by Schmithorst and Holland (2004). The weak assumption on the source process we need to solve the BSS problem with this approach is that sources are uncorrelated and have non-proportional power spectrum (Congedo et al 2008). …”

Section: Group Blind Source Separationmentioning

confidence: 99%

“…To solve the AJD problem we use an iterative algorithm previously developed (Pham and Congedo 2009), which is computationally fast. The reader is referred to (Congedo et al 2008) …”

Section: Group Blind Source Separationmentioning

confidence: 99%

“…Whereas ICA relies on high-order statistics, here we use another blind source separation (BSS) framework based on second-order statistics. We have previously argued that second-order BSS methods suits well EEG data (Congedo et al 2008). Importantly, it allows us to address the problem of possible dependence among the extracted regions of coherent synchronization.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Congedo

John

Ridder³

et al. 2009

Brain Topogr

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Abstract. The aim of this work is to study the coherence profile (dependence) of robust eyes-closed resting EEG sources isolated by group blind source separation (gBSS). We employ a test-retest strategy using two large sample normative databases (N=57 and N=84). Using a BSS method in the complex Fourier domain, we show that we can rigourously study the out-of-phase dependence of the exctracted components, albeit they are extracted so as to be in-phase independent (by BSS definition). Our focus on lagged communication between components effectively yields dependence measures unbiased by volume conduction effects, which is a major concern about the validity of any dependence measures issued by EEG measurements. We are able to show the organization of the extracted components in two networks. Within each network components oscillate coherently with multiple-frequency dynamics, whereas between networks they exchange information at non-random multiple time-lag rates.

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Section: Group Blind Source Separationmentioning

confidence: 99%

Section: Group Blind Source Separationmentioning

confidence: 99%

Section: Group Blind Source Separationmentioning

confidence: 99%

“…To solve the AJD problem we use an iterative algorithm previously developed (Pham and Congedo 2009), which is computationally fast. The reader is referred to (Congedo et al 2008) …”

Section: Group Blind Source Separationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Congedo

John

Ridder³

et al. 2009

Brain Topogr

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Dynamic changes of ICA‐derived EEG functional connectivity in the resting state

Chen¹,

Ros²,

Gruzelier³

2012

Human Brain Mapping

116

107

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An emerging issue in neuroscience is how to identify baseline state(s) and accompanying networks termed "resting state networks" (RSNs). Although independent component analysis (ICA) in fMRI studies has elucidated synchronous spatiotemporal patterns during cognitive tasks, less is known about the changes in EEG functional connectivity between eyes closed (EC) and eyes open (EO) states, two traditionally used baseline indices. Here we investigated healthy subjects (n = 27) in EC and EO employing a four-step analytic approach to the EEG: (1) group ICA to extract independent components (ICs), (2) standardized low-resolution tomography analysis (sLORETA) for cortical source localization of IC network nodes, followed by (3) graph theory for functional connectivity estimation of epochwise IC band-power, and (4) circumscribing IC similarity measures via hierarchical cluster analysis and multidimensional scaling (MDS). Our proof-of-concept results on alpha-band power demonstrate five statistically clustered groups with frontal, central, parietal, occipitotemporal, and occipital sources. Importantly, during EO compared with EC, graph analyses revealed two salient functional networks with frontoparietal connectivity: a more medial network with nodes in the mPFC/precuneus which overlaps with the "default-mode network" (DMN), and a more lateralized network comprising the middle frontal gyrus and inferior parietal lobule, coinciding with the "dorsal attention network" (DAN). Furthermore, a separate MDS analysis of ICs supported the emergence of a pattern of increased proximity (shared information) between frontal and parietal clusters specifically for the EO state. We propose that the disclosed component groups and their source-derived EEG functional connectivity maps may be a valuable method for elucidating direct neuronal (electrophysiological) RSNs in healthy people and those suffering from brain disorders.

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Single trial variability in brain–computer interfaces based on motor imagery: Learning in the presence of labeling noise

Gouy-Pailler¹,

Sebag²,

Larue³

et al. 2011

Int J Imaging Syst Tech

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This article addresses the issue of learning efficient linear spatial filters and a classification function to match noninvasive electroencephalographic (EEG) signals to motor imagery tasks voluntarily performed by the subjects. The new perspective used in this article consists in releasing the widely accepted hypothesis stating that motor tasks-related brain activities should have similar time course across trials. This work proposes a learning model that takes into account two previously unconsidered sources of variability. First, the time course of the subject's brain activity, while performing a motor imagery task, will be considered as a trial-dependent variable. This means that the optimal time, defined as an amount of time after the trial cue, chosen to determine the task performed by the subject might be different between distinct trials. The second released hypothesis deals with the spectral discriminative brain response. Although usual learning methods do not allow any dependency between the optimal discriminative time and frequency bands, our model takes into account this possible source of variability. Therefore, the brain response in distinct frequency bands, e.g., in the mu band or beta band, could be used by the decision function at distinct instants. Based on this underlying enhanced model, we propose a two-step procedure. In the first step, the algorithm carefully analyzes, using cross-validation techniques, the training data to identify previously mentioned sources of variability. In the second step, the enhanced frequency-dependent linear spatial filters and the classification function are determined. As by-products of this analysis, substantial piece of knowledge about motor imagery is provided. First, the method allows the identification and quantification of labeling noise in brain-computer interfaces (BCIs) based on motor imagery. Second, the algorithm gives a comparative estimation of the spectral time courses during motor imagery. This article makes an extensive use of the dataset I of BCI Competition IV, which took place in 2008. It consists of a training set and a test set of 59 EEG signals recording on four subjects while performing an asynchronous BCI experiment.The two-step procedure presented in this article is shown to significantly outperform a comparative naive approach.

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On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics

Cited by 120 publications

References 104 publications

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

On the “Dependence” of “Independent” Group EEG Sources; an EEG Study on Two Large Databases

Dynamic changes of ICA‐derived EEG functional connectivity in the resting state

Single trial variability in brain–computer interfaces based on motor imagery: Learning in the presence of labeling noise

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