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Mathematical Modelling and Data Analysis

Department 8.4

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Title: Shifted factor analysis for the separation of evoked dependent MEG signals
Author(s): F. Kohl, G. Wübbeler, D. Kolossa, M. Bär, R. Orglmeister and C. Elster
Journal: Physics in medicine and biology
Year: 2010
Volume: 55
Issue: 15
Pages: 4219--30
IOP Publishing
DOI: 10.1088/0031-9155/55/15/002
ISSN: 1361-6560
Web URL: http://iopscience.iop.org/article/10.1088/0031-9155/55/15/002
Keywords: Evoked Potentials,Humans,Magnetoencephalography,Magnetoencephalography: methods,Models, Statistical,Neurons,Neurons: cytology,SingleTrial
Tags: 8.42, Gehirn, SingleTrial
Abstract: Decomposition of evoked magnetoencephalography (MEG) data into their underlying neuronal signals is an important step in the interpretation of these measurements. Often, independent component analysis (ICA) is employed for this purpose. However, ICA can fail as for evoked MEG data the neuronal signals may not be statistically independent. We therefore consider an alternative approach based on the recently proposed shifted factor analysis model, which does not assume statistical independence of the neuronal signals. We suggest the application of this model in the time domain and present an estimation procedure based on a Taylor series expansion. We show in terms of synthetic evoked MEG data that the proposed procedure can successfully separate evoked dependent neuronal signals while standard ICA fails. Latency estimation of neuronal signals is an inherent part of the proposed procedure and we demonstrate that resulting latency estimates are superior to those obtained by a maximum likelihood method.

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