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ISBI
2006
IEEE

Two probabilistic algorithms for MEG/EEG source reconstruction

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Two probabilistic algorithms for MEG/EEG source reconstruction
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 ExpectationMaximization (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.
Johanna M. Zumer, Hagai Attias, Kensuke Sekihara,
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2006
Where ISBI
Authors Johanna M. Zumer, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan
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