Bayesian Spatio-Temporal Decomposition For Electromagnetic Imaging Of Extended Sources Based On Destrieux Atlas

It is a long-standing challenge to accurately estimate the locations and extents of brain activities from electroencephalography and magnetoencephalography (E/MEG). In this work, a new fully data-driven source imaging method, Source Imaging based on Spatio-Temporal Decompositions (SI-STD), which is built upon a Bayesian framework, is proposed to address this issue. SI-STD decomposes the source matrix as a linear combination of several unknown temporal basis functions (TBFs). The prior of TBFs is set in the empirical Bayesian manner. Additionally, the spatial prior of sources is constructed using the Destrieux atlas and local smoothness constraint to model the globally sparse and locally smooth character of sources. Through variational Bayesian inference techniques, the TBFs and corresponding weight matrix are learned from E/MEG simultaneously. The results of numerical experiments with simulated and experimental E/MEG data show the superior performance of SI-STD in reconstructing extended sources compared with the L2-norm constrained methods. By virtue of the spatio-temporal decompositions of source matrix, SI-STD also produces better estimations of spatial and temporal profiles than the spatial-only constraint method.