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 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