Paired MEG data set source localization using recursively applied and projected (RAP) MUSIC

An important class of experiments in fractional brain mapping involves collecting pairs of data corresponding to separate "task" and "control" conditions. The data are then analyzed to determine what activity occurs during the task experiment but not in the control. Here we describe a new method for processing paired magnetoencephalographic (MEG) data sets using our recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this method the signal subspace of the task data is projected against the orthogonal complement of the control data signal subspace to obtain a subspace which describes activity unique to the task. A RAP-MUSIC localization search is then performed on this projected data to localize the sources which are active in the task but not in the control data.