Estimation of M/EEG non-stationary brain activity using spatio-temporal sparse constraints

Abstract

Based on the assumption that brain activity appears in localized brain regions that can vary along time, yielding spatial and temporal non-stationary activity, we propose a constrained M/EEG inverse solution, based on the Fused Lasso penalty, that reconstructs brain activity as dynamic small and locally smooth spatial patches. Thus, our main contribution is to provide neural activity reconstruction tracking nonstationary dynamics. We validate the proposed approach in two different ways: i) using simulated MEG data when we have previous knowledge about spatial and temporal signal dynamics, and ii) using real MEG data, particularly we use a faces perception paradigm aimed to examine the M170 response. In the former case of validation, our approach outperforms conventional M/EEG-based imaging algorithms. Besides, there is a high correspondence between brain activities presented on the evaluated real MEG data and the time-varying solution obtained by our approach.