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Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 402341, 11 pages
Research Article

Localizing True Brain Interactions from EEG and MEG Data with Subspace Methods and Modified Beamformers

1IDA Group, Fraunhofer Institute FIRST, Kekuléstraße 7, 12489 Berlin, Germany
2Department of Computer Science, Faculty of Mathematics and Natural Sciences II, Humboldt-Universitaet zu Berlin, Rudower Chausee 25, 10099 Berlin, Germany
3Machine Learning Group, Berlin Institute of Technology, Franklinstr 28/29, 10587 Berlin, Germany
4NIRx Medizintechnik GmbH, Baumbachstraße 17, 13189 Berlin, Germany
5Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Martinistraße 52, 20246 Hamburg, Germany

Received 21 November 2011; Revised 17 February 2012; Accepted 10 May 2012

Academic Editor: Ralph G. Andrzejak

Copyright © 2012 Forooz Shahbazi Avarvand et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


To address the problem of mixing in EEG or MEG connectivity analysis we exploit that noninteracting brain sources do not contribute systematically to the imaginary part of the cross-spectrum. Firstly, we propose to apply the existing subspace method “RAP-MUSIC” to the subspace found from the dominant singular vectors of the imaginary part of the cross-spectrum rather than to the conventionally used covariance matrix. Secondly, to estimate the specific sources interacting with each other, we use a modified LCMV-beamformer approach in which the source direction for each voxel was determined by maximizing the imaginary coherence with respect to a given reference. These two methods are applicable in this form only if the number of interacting sources is even, because odd-dimensional subspaces collapse to even-dimensional ones. Simulations show that (a) RAP-MUSIC based on the imaginary part of the cross-spectrum accurately finds the correct source locations, that (b) conventional RAP-MUSIC fails to do so since it is highly influenced by noninteracting sources, and that (c) the second method correctly identifies those sources which are interacting with the reference. The methods are also applied to real data for a motor paradigm, resulting in the localization of four interacting sources presumably in sensory-motor areas.