Journal of Cognitive Neuroscience
Second-Order separation of multidimensional sources with constrained mixing system
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Comparison of ICA algorithms for the isolation of biological artifacts in magnetoencephalography
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
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A multi-modal linear mixing model is suggested for simultaneously measured MEG and EEG data. On the basis of this model an ICA decomposition is calculated for a combined MEG and EEG signal vector using the TDSEP algorithm. A single modality demixing procedure is developed to classify ICA components to be multimodality sources detected by EEG and MEG simultaneously or to be single mode sources. Under this premise, data from 10 subjects are analysed and four exemplary types of sources are selected. We found that these sources represent physically meaningful multi-and single-mode signals: Alpha oscillations, heart activity, eye blinks, and slow signal drifts.