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

An important class of experiments in functional brain mapping involves coll ecting pairs of data Corresponding to separate "Task" and "Control'' condit ions. The data are then analyzed to determine what activity occurs during t he Task experiment but not in the Control, Here me describe a new method fo r processing paired magnetoencephalographic (MEG) data sets using our recur sively applied and projected multiple signal classification (RAP-MUSIC) alg orithm. In this method the signal subspace of the Task data is projected ag ainst the orthogonal complement of the Control data signal subspace to obta in a subspace which describes spatial activity unique to the Task, A RAP-MU SIC localization search is then performed on this projected data to localiz e the sources which are active in the Task but not in the Control data. in addition to dipolar sources, effective blocking of more complex sources, e. g., multiple synchronously activated dipoles or synchronously activated dis tributed source activity, is possible since these topographies are well-des cribed by the Control data signal subspace, Unlike previously published met hods, the proposed method is shown to be effective in situations where the time series associated with Control and Task activity possess significant c ross correlation. The method also allows for straightforward determination of the estimated time series of the localized target sources, A multiepoch MEG simulation and a phantom experiment are presented to demonstrate the ab ility of this method to successfully identify sources and their time series in the Task data.