EEG source imaging based on spatial and temporal graph structures

Abstract

EEG serves as an essential tool for brain source localization due to its high temporal resolution. However, the inference of brain activities from the EEG data is, in general, a challenging ill-posed inverse problem. To better retrieve task related discriminative source patches from strong spontaneous background signals, we propose a novel EEG source imaging model based on spatial and temporal graph structures. In particular, graph fractional-order total variation (gFOTV) is used to enhance spatial smoothness, and the label information of brain state is enclosed in a temporal graph regularization term to guarantee intra-class consistency of estimated sources. The proposed model is efficiently solved by the alternating direction method of multipliers (ADMM). A two-stage algorithm is proposed as well to further improve the result. Numerical experiments have shown that our method localizes source extents more effectively than the benchmark methods.