Technical Program

Paper Detail

Paper:	SA-PM-PS3.6
Session:	Functional, Dynamic and Parametric Imaging
Time:	Saturday, April 8, 13:30 - 14:50
Presentation:	Poster
Title:	Two Probabilistic Algorithms for MEG/EEG Source Reconstruction
Authors:	Johanna Zumer; University of California, San Francisco
	Hagai Attias; Golden Metallic, Inc.
	Kensuke Sekihara; Toyko Metropolitan University
	Srikantan Nagarajan; University of California, San Francisco
Abstract:	We have developed two algorithms for source imaging from MEG/EEG data. Contribution to sensor data from a source at a particular voxel is expressed as the product of a known lead field and temporal basis functions with unknown coefficients. Temporal basis functions are in turn estimated from data. The first algorithm models activity outside the voxel of interest by a full-rank covariance matrix and estimates unknowns by maximizing the likelihood. The second algorithm parameterizes activity outside the voxel of interest as a linear mixture of a set of unknown Gaussian factors plus Gaussian sensor noise and estimates all unknown quantities using an Expectation-Maximization (EM) algorithm. In both cases, the source image map is the likelihood of a dipole source at each voxel. Performance in simulations and real data demonstrate significant improvement over existing source localization methods.

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