Sur l’exploitation des approches d’analyse en composantes indépendantes dans les interfaces cerveau machine

Senhadji, Lotfi; Kachenoura, Amar; Albera, Laurent; Comon, Pierre

doi:10.1016/j.irbm.2009.10.005

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Clustering techniques are then used to group similar components across subjects [6]. Some evidence of the possible interest in using ICA for effective Brain Computer Interface (BCI) design have been demonstrated [8][9][10][11][12]. However, BCI experiments usually involve repeated sessions recorded over several days from the same subjects.…”

Section: Introductionmentioning

confidence: 99%

Stability of ICA decomposition across within-subject EEG datasets

Grandchamp

Braboszcz

Makeig

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Independent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research.

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Section: Introductionmentioning

confidence: 99%

Stability of ICA decomposition across within-subject EEG datasets

Grandchamp

Braboszcz

Makeig

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A Comparative Study of Two Reference Estimation Methods in EEG Recording

Cao

Chen

et al. 2012

Advances in Brain Inspired Cognitive Systems

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In [1] we proposed two methods to identify the reference electrode signal under the key assumption that the reference signal is independent from EEG sources. This assumption is shown to be possibly true for intracranial EEG with a scalp reference. In this paper, we theoretically prove that the obtained reference signal by using the second method in [1] or the equivalent MPDR approach [2] outperforms the widely used average reference (AR) if the real reference is independent from EEG sources. The simulation results confirm the advantages over AR.

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On joint diagonalization of cumulant matrices for independent component analysis of MRS and EEG signals

Albera

Kachenoura

Wendling

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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An extension of the original implementation of JADE, named eJADE((1)) hereafter, was proposed in 2001 to perform independent component analysis for any combination of statistical orders greater than or equal to three. More precisely, eJADE((1)) relies on the joint diagonalization of a set of several cumulant matrices corresponding to different matrix slices of one or several higher order cumulant tensors. An efficient way, without lose of statistical information, of reducing the number of third and fourth order cumulant matrices to be jointly diagonalized is proposed in this paper. The resulting approach, named eJADE(3,4)((2)), can be interpreted as an improvement of the eJADE(3,4)((1)) method. A performance comparison with classical methods is conducted in the context of MRS and EEG signals showing the good behavior of our technique.

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Sur l’exploitation des approches d’analyse en composantes indépendantes dans les interfaces cerveau machine

Cited by 3 publications

References 31 publications

Stability of ICA decomposition across within-subject EEG datasets

Stability of ICA decomposition across within-subject EEG datasets

A Comparative Study of Two Reference Estimation Methods in EEG Recording

On joint diagonalization of cumulant matrices for independent component analysis of MRS and EEG signals

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