title: Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG.
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.
authors.affiliation: Intelligent Signal Processing Group, Informatics and Mathematical Modelling, Technical University of Denmark, 2800 Lyngby, Denmark. mad@imm.dtu.dk
authors.forename: Mads
authors.initials: M
authors.lastname: Dyrholm
authors.forename: Scott
authors.initials: S
authors.lastname: Makeig
authors.forename: Lars Kai
authors.initials: LK
authors.lastname: Hansen
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D008959:Models, Neurological; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted