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Title: EEG and MEG source localization using recursively applied (RAP) MUSIC

Conference ·
OSTI ID:442227
 [1];  [2]
  1. Los Alamos National Lab., NM (United States)
  2. University of Southern California, Los Angeles, CA (United States). Signal and Image Processing Inst.

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. Here we describe a novel 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, which uses the metric of principal correlations as a multidimensional form of correlation analysis between the model subspace and the data subspace. The dipolar orientations, a form of `diverse polarization,` are easily extracted using the associated principal vectors.

Research Organization:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
National Insts. of Health, Bethesda, MD (United States)
DOE Contract Number:
W-7405-ENG-36
OSTI ID:
442227
Report Number(s):
LA-UR-96-3889; CONF-9611134-1; ON: DE97002358
Resource Relation:
Conference: 13. annual conference on signals, systems, and computers, Pacific Grove, CA (United States), Nov 1996; Other Information: PBD: [1996]
Country of Publication:
United States
Language:
English