Convolutive ICA for Spatio-Temporal Analysis of EEG

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 introducing 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.

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