The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-304

“Recursively Applied MUSIC: A Framework for EEG and MEG Source Localization”

by John C. Mosher and Richard M. Leahy

December 1996

The multiple signal characterization (MUSIC) algorithm locates multiple asynchronous dipolar sources from electroencephalography (EEG) and magnetoencephalography (MEG) data. A signal subspace is estimated from the data, then the algorithm scans a single dipole model through a three-dimensional head volume and computes projections onto this subspace. To locate the sources, the user must search the head volume for local peaks in the projection metric. This task is time consuming and subjective. Here we describe an extension of this approach which we refer to as RAP (Recursively APplied) MUSIC. This new procedure automatically extracts the locations of the sources through a recursive use of subspace projections. The new method is also able to deal with synchronous sources through the use of a spatially independent topographies (SPIT) model. This model defines a source as one or more non-rotating dipoles with a single time course. Within this framework, we are able to locate fixed, rotating and synchronous dipoles. The recursive subspace projection procedure that we introduce here uses the metric of subspace angles as a multidimensional form of correlation analysis between the model subspace and the data subspace. By using subspace angle computations, we recursively build up a model for the sources that account for a given set of data. We demonstrate here how RAP-MUSIC can easily extract multiple asynchronous dipolar sources that are difficult to find using the original MUSIC scan. We then demonstrate RAP-MUSIC applied to the more general SPIT model and show results for combinations of fixed, rotating, and synchronous dipoles.

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