Robust MEG source localization of event related potentials: identifying relevant sources by non-gaussianity

Authors:
Peter Breun;Moritz Grosse-Wentrup;Wolfgang Utschick;Martin Buss
Affiliations:
Institute for Circuit Theory and Signal Processing, Technische Universität München, München, Germany;Institute of Automatic Control Engineering (LSR), Technische Universität München, München, Germany;Institute for Circuit Theory and Signal Processing, Technische Universität München, München, Germany;Institute of Automatic Control Engineering (LSR), Technische Universität München, München, Germany
Venue:
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Year:
2006

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Abstract

Independent Component Analysis (ICA) is a frequently used preprocessing step in source localization of MEG and EEG data. By decomposing the measured data into maximally independent components (ICs), estimates of the time course and the topographies of neural sources are obtained. In this paper, we show that when using estimated source topographies for localization, correlations between neural sources introduce an error into the obtained source locations. This error can be avoided by reprojecting ICs onto the observation space, but requires the identification of relevant ICs. For Event Related Potentials (ERPs), we identify relevant ICs by estimating their non-Gaussianity. The efficacy of the approach is tested on auditory evoked potentials (AEPs) recorded by MEG. It is shown that ten trials are sufficient for reconstructing all important characteristics of the AEP, and source localization of the reconstructed ERP yields the same focus of activity as the average of 250 trials.