Convolutive ICA for Spatio-Temporal Analysis of EEG

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

Documents

View graph of relations

We 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.
Original languageEnglish
JournalNeural Computation
Publication date2007
Volume19
Pages934-955
ISSN0899-7667
StatePublished

Keywords

  • Independent component analysis, ICA, EEG, signal processing
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

Download statistics

No data available

ID: 3940349