Convolutive independent component analysis of EEG data

Independent component analysis is applied to EEG data. Conventionally EEG is dealt with on the assumption that the mixing process is instantaneous, but a close investigation shows that the process of generating EEG should be considered convolutive. In this paper a convolutive ICA algorithm that was proposed by one of the authors is applied to EEG data. The result shows that the convolutive ICA extracts independent components much more clearly than the instantaneous ICA. In the case of convolutive ICA, around 13 independent components have been indentified, which is much smaller than the number of channels.