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

Mads Dyrholm, S. Makeig, Lars Kai Hansen

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    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
    Volume19
    Issue number4
    Pages (from-to)934-955
    ISSN0899-7667
    DOIs
    Publication statusPublished - 2007

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