期刊
JOURNAL OF NEUROSCIENCE METHODS
卷 183, 期 1, 页码 72-76出版社
ELSEVIER
DOI: 10.1016/j.jneumeth.2009.07.006
关键词
EEG; MEG; Inverse methods; Connectivity; Decomposition; MOCA
资金
- Bundesministerium fur Bildung und Forschung [01GQ0415]
In many situations various methods to analyze EEG/MEG data result in subspaces of the sensor space spanned by potentials of a set of sources. We propose a general model free method to decompose such a subspace into contributions from distinct sources. This unique decomposition can be achieved by first finding the respective subspace in source space using a linear inverse method and then finding the linear transformation such that the source distributions are mutually orthogonal and have a minimum overlap. The corresponding algorithm is a generalization of the recently presented 'Minimum Overlap Component Analysis' (MOCA) to more than two sources. The computational cost is negligible and the algorithm is almost never trapped in local minima. The method is illustrated with results for alpha rhythm. (C) 2009 Elsevier B.V. All rights reserved.
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