HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Dyrholm, M. Articles by Hansen, L. K. Search for Related Content PubMed Articles by Dyrholm, M. Articles by Hansen, L. K.

Model Selection for Convolutive ICA with an Application to Spatiotemporal Analysis of EEG

mad{at}imm.dtu.dk Intelligent Signal Processing Group, Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark

Scott Makeig

smakeig{at}ucsd.edu Swartz Center for Computational Neuroscience, Institute for Neural Computation University of California San Diego, La Jolla CA 92093–0961, U.S.A.

Lars Kai Hansen

lkh{at}imm.dtu.dk Intelligent Signal Processing Group, Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark

We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model.