Item

ITEM ACTIONS

This item is discarded!DetailsSummary

Discarded

Conference Paper

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

MPS-Authors

/persons/resource/persons83948

Grosse-Wentrup, M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Breun, P., Grosse-Wentrup, M., Utschick, W., & Buss, M. (2006). Robust MEG Source Localization of Event Related Potentials: Identifying Relevant Sources by Non-Gaussianity. Pattern Recognition: 28th DAGM Symposium, 394-403.

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.