Semi-blind identification of movement-related magnetoencephalogram components using a classification approach

Many biomedical signal processing applications involving the analysis of multi-channel electrophysiological recordings, such as the magnetoencephalogram (MEG) and electroencephalogram (EEG), increasingly employ blind source separation (BSS) techniques to estimate signal components reflecting artifacts and neurophysiological activity. While much research focuses on developing methods for automatic removal of artefact sources, comparatively little effort has been spent on trying to identify neurophysiological sources of interest, which is especially challenging in the absence of prior knowledge about their spatial or time-freqency characteristics. This work presents a method for identifying source signals exhibiting systematic and reliable time-frequency differences over clearly defined epochs associated with different 'system-states'. The proposed method uses annotated data and a classification approach to identify those sources which individually reflect significant differences between epochs (classes). Applied to segments of 275-channel MEG data from a visuo-motor task in which left, right or no finger movements occurred, the method selects only a small number of sources whose scalp topographies are consistent with primary sensorimotor cortical areas.