Fast, variation-based methods for the analysis of extended brain sources.

Identifying the location and spatial extent of several highly correlated and simultaneously active brain sources from electroencephalographic (EEG) recordings and extracting the corresponding brain signals is a challenging problem. In a recent comparison of source imaging techniques, the VB-SCCD algorithm, which exploits the sparsity of the variational map of the sources, proved to be a promising approach. In this paper, we propose several ways to improve this method. In order to adjust the size of the estimated sources, we add a regularization term that imposes sparsity in the original source domain. Furthermore, we demonstrate the application of ADMM, which permits to efficiently solve the optimization problem. Finally, we also consider the exploitation of the temporal structure of the data by employing L-1,L-2-norm regularization. The performance of the resulting algorithm, called L-1,L-2-SVB-SCCD, is evaluated based on realistic simulations in comparison to VB-SCCD and several state-of-the-art techniques for extended source localization.