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DAGM
2006
Springer

Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity

11 years 5 months ago
Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity
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.
Peter Breun, Moritz Grosse-Wentrup, Wolfgang Utsch
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where DAGM
Authors Peter Breun, Moritz Grosse-Wentrup, Wolfgang Utschick, Martin Buss
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