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

Conference · Mon Dec 30 23:00:00 EST 1996

OSTI ID:442227

Mosher, J C ^[1]; Leahy, R M ^[2]

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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., 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
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
BIOLOGICAL LOCALIZATION
BRAIN
COMPUTERIZED SIMULATION
ELECTRIC DIPOLE MOMENTS
ELECTRIC DIPOLES
ELECTRIC FIELDS
ELECTROENCEPHALOGRAPHY
HEAD
LEAST SQUARE FIT
MAGNETIC DIPOLE MOMENTS
MAGNETIC DIPOLES
MAGNETIC FIELDS
MATHEMATICAL MODELS
MATHEMATICAL SPACE
MATRICES
MAXWELL EQUATIONS
SPACE DEPENDENCE
TOPOLOGICAL MAPPING
VOLUME