In:
Neural Computation, MIT Press, Vol. 19, No. 4 ( 2007-04), p. 934-955
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.
Type of Medium:
Online Resource
ISSN:
0899-7667
,
1530-888X
DOI:
10.1162/neco.2007.19.4.934
Language:
English
Publisher:
MIT Press
Publication Date:
2007
detail.hit.zdb_id:
1025692-1
detail.hit.zdb_id:
1498403-9
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