Convolutive ICA for Spatio-Temporal Analysis of EEG

Mads Dyrholm, Scott Makeig, Lars Kai Hansen

AbstractWe present a new algorithm for maximum likelihood convolutive
ICA (cICA) in which sources are unmixed using stable IIR filters determined
implicitly by estimating an FIR filter model of the mixing process. By intro-
ducing a FIR model for the sources we show how the order of the filters in the
convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.
KeywordsIndependent component analysis, ICA, EEG, signal processing
TypeJournal paper [Submitted]
JournalNeural Computation
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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