@ARTICLE\{IMM2007-04719,
    author       = "M. Dyrholm and S. Makeig and L. K. Hansen",
    title        = "Convolutive {ICA} for Spatio-Temporal Analysis of {EEG}",
    year         = "2007",
    keywords     = "Independent component analysis, {ICA,} {EEG,} signal processing",
    journal      = "Neural Computation",
    volume       = "",
    editor       = "",
    number       = "",
    publisher    = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4719-full.html",
    abstract     = "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."
}