Robust Bayesian algorithm for distributed source reconstructions from MEG/EEG data

One of the enduring challenges in MEG/EEG data analysis is the poor performance of source reconstruction algorithms under high noise and interference conditions, especially in case of distributed, correlated brain activity with complex spatial extent. In our previous work, we developed a source localization algorithm, Champagne, which is robust to the effects of noise, interference and highly correlated brain source activity. Champagne is ideally suited for reconstructions of sparse and highly clustered brain source activity rather than reconstruction of distributed source activity with larger spatial extents. Here, we introduce a novel Bayesian algorithm that enables reconstruction of distributed source activity. We build upon the robust performance features of the Champagne algorithm and refer to this algorithm as Smooth_Champagne. Simulations demonstrate excellent performance of Smooth_Champagne in determining the spatial extent of source activity. Smooth_Champagne also accurately reconstructs real MEG and EEG data.