Imaging brain extended sources from EEG/MEG based on variation sparsity using automatic relevance determination

Abstract Estimating the extents and localizations of extended sources from noninvasive EEG/MEG signals is challenging. In this paper, we have proposed a fully data driven source imaging method, namely Variation Sparse Source Imaging based on Automatic Relevance Determination (VSSI-ARD), to reconstruct extended cortical activities. VSSI-ARD explores the sparseness of current sources on the variation domain by employing ARD prior under empirical Bayesian framework. With convex analysis, the sources are efficiently obtained by solving a series of reweighting L21-norm regularization problems with ADMM. By virtue of the iterative reweighting process and sparse signal processing techniques, VSSI-ARD gets rid of the small amplitude dipoles that are more probably outside the extent of underlying sources. With the sparsity enforced on the edges using ARD prior, the estimations show clear boundaries between active and background regions without subjective thresholds. Validation with both simulated and human experimental data indicates that VSSI-ARD not only estimates the localizations of sources, but also provides relatively useful and accurate information about the extents of cortical activities.