Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG |
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Authors: | Dyrholm Mads Makeig Scott Hansen Lars Kai |
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Affiliation: | Intelligent Signal Processing Group, Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark. mad@imm.dtu.dk |
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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. |
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