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. |