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TWO PROBABILISTIC ALGORITHMS FOR MEG/EEG SOURCE RECONSTRUCTION
BibTeX
@MISC{Zumer_twoprobabilistic,
author = {Johanna M. Zumer and Hagai T. Attias and Kensuke Sekihara and Srikantan S. Nagarajan},
title = {TWO PROBABILISTIC ALGORITHMS FOR MEG/EEG SOURCE RECONSTRUCTION},
year = {}
}
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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 parameter-izes 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 im-age map is the likelihood of a dipole source at each voxel. Performance in simulations and real data demonstrate signifi-cant improvement over existing source localization methods. 1.
Keyphrases
temporal basis function estimate unknown linear mixture gaussian sensor noise unknown gaussian factor meg eeg data second algorithm parameter-izes activity unknown coefficient full-rank covariance matrix real data source im-age map first algorithm model activity unknown quantity source imaging known lead field source localization method particular voxel signifi-cant improvement dipole source
