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Neural Computation

April 2007, Vol. 19, No. 4, Pages 934-955
(doi: 10.1162/neco.2007.19.4.934)
© 2007 Massachusetts Institute of Technology
Model Selection for Convolutive ICA with an Application to Spatiotemporal Analysis of EEG
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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.