Neuroelectromagnetic imaging of correlated sources using a novel subspace penalized sparse learning

Localization of brain signal sources from EEG/MEG has been an active area of research [1]. Currently, there exists a variety of approaches such as MUSIC [2], M-SBL [3], and etc. These algorithms have been applied for various clinical examples and demonstrated excellent performances. However, when the unknown sources are highly correlated, the conventional algorithms often exhibit spurious reconstructions. To address the problem, this paper proposes a new algorithm that generalizes M-SBL by exploiting the fundamental subspace geometry in the multiple measurement problem (MMV). Experimental results using simulation and real phantom data show that the proposed algorithm outperforms the existing methods even under a highly correlated source condition.