Common spatial subspace decomposition applied to analysis of brain responses under multiple task conditions: a simulation study.

A method, called common spatial subspace decomposition, is presented which can extract signal components specific to one condition from multiple magnetoencephalography/electroencephalography data sets of multiple task conditions. Signal matrices or covariance matrices are decomposed using spatial factors common to multiple conditions. The spatial factors and corresponding spatial filters are then dissociated into specific and common parts, according to the common spatial subspace which exists among the data sets. Finally, the specific signal components are extracted using the corresponding spatial filters and spatial factors. The relationship between this decomposition and spatio-temporal source models is described in this paper. Computer simulations suggest that this method can facilitate the analysis of brain responses under multiple task conditions and merits further application.