Sparse EEG Source Localization Under the Variational Bayesian Framework

Abstract

In this paper we propose a new method for EEG source localization. Solving the above problem usually requires choosing an appropriate regularization term. Our method is based on the bayesian approach, hence, the regularization term is closely connected to the prior distribution. The proposed prior distribution has sparse properties favoring focal EEG sources. In order to obtain an efficient algorithm we use the Variational Bayesian framework which provides us a tractable iterative algorithm of closed form equations. We compare the proposed method with well-known approaches of EEG source localization and the results, using synthetic and real EEG data, have shown that our method presents state-of-the-art performance, especially in cases where we expect few activated brain regions.