Abstract:This paper introduces the usage of non-convex based regularizers to solve the underdetermined MEG inverse problem. The signal to be reconstructed is considered to have a structure which entails group-wise sparsity and within group sparsity among its covariates. We discuss the usage of l2 norm regularization and smoothed l0 (SL0) norm regularization to impose group-wise and within group sparsity respectively. In addition, we introduce a novel criterion which if satisfied, guarantees global optimality while solv… Show more
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