Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG

Publication: Research - peer-reviewJournal article – Annual report year: 2007

View graph of relations

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.
Original languageEnglish
JournalNeural Computation
Publication date2007
Volume19
Issue4
Pages934-955
ISSN0899-7667
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 25
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 3833162