Convolutive ICA for Spatio-Temporal Analysis of EEG

Mads Dyrholm, Scott Makeig, Lars Kai Hansen

    Research output: Contribution to journalJournal articleResearchpeer-review

    290 Downloads (Pure)


    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
    Pages (from-to)934-955
    Publication statusPublished - 2007


    • Independent component analysis
    • ICA
    • EEG
    • signal processing

    Fingerprint Dive into the research topics of 'Convolutive ICA for Spatio-Temporal Analysis of EEG'. Together they form a unique fingerprint.

    Cite this