Multi-modal ICA exemplified on simultaneously measured MEG and EEG data

Abstract

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 multi-modality 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. © Springer-Verlag Berlin Heidelberg 2007.