Sparse electromagnetic source imaging using EEG and MEG

The present study proposed the combined use of EEG and MEG data in a new sparse electromagnetic source imaging (ESI) technique, i.e., variation-based sparse cortical current density (VB-SCCD) method. Monte Carlo simulations were conducted to investigate the performance of the proposed approach in multiple extended brain activations (up to ten) that were randomly generated. Experimental EEG and MEG data from a face recognition task were further used to evaluate the performance of VB-SCCD. The present results indicate that the proposed approach can accurately reconstruct multiple brain activations and their spatial extents. The source imaging results from real data further demonstrate it is capable to recover networked brain activations involving multiple cortical regions, which are consistent with results from functional magnetic resonance imaging in same task paradigm. The present results further indicate the capability of the proposed approach in reconstructing deep brain sources and temporal dynamics of brain sources at millisecond resolutions. It thus suggests that sparse ESI using combined EEG and MEG is a promising technique probing detailed spatiotemporal brain activations.