ICA: A Potential Tool for BCI Systems

Abstract : Several studies dealing with independent component analysis (ICA)-based brain-computer interface (BCI) systems have been reported. Most of them have only explored a limited number of ICA methods, mainly FastICA and INFOMAX. The aim of this article is to help the BCI community researchers, especially those who are not familiar with ICA techniques, to choose an appropriate ICA method. For this purpose, the concept of ICA is reviewed and different measures of statistical independence are reported. Then, the application of these measures is illustrated through a brief description of the widely used algorithms in the ICA community, namely SOBI, COM2, JADE, ICAR, FastICA, and INFOMAX. The implementation of these techniques in the BCI field is also explained. Finally, a comparative study of these algorithms, conducted on simulated electroencephalography (EEG) data, shows that an appropriate selection of an ICA algorithm may significantly improve the capabilities of BCI systems.
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Article dans une revue
IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2008, 25 (1), pp.57-68. <10.1109/MSP.2008.4408442>
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Contributeur : Lotfi Senhadji <>
Soumis le : lundi 7 janvier 2008 - 18:38:48
Dernière modification le : vendredi 21 octobre 2016 - 01:36:38
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Amar Kachenoura, Laurent Albera, Lotfi Senhadji, Pierre Comon. ICA: A Potential Tool for BCI Systems. IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 2008, 25 (1), pp.57-68. <10.1109/MSP.2008.4408442>. <inserm-00202706>

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