Mind the noise covariance when localizing brain sources with M/EEG

Abstract : Magnetoencephalography (MEG) and electroencephalography (EEG) are imaging methods that measure neuronal dynamics non invasively with high temporal precision. It is often desired in MEG and EEG analysis to estimate the neu-ral sources of the signals. Strategies used for this purpose often take into account the covariance between sensors to yield more precise estimates of the sources. Here we investigate in greater detail how the quality of such covariance estimates conditions the estimation of MEG and EEG sources. We investigated three distinct source localization methods: dynamic Statistical Parametric Maps (dSPM), the linearly constrained minimum variance (LCMV) beamformer and Mixed-Norm Estimates (MxNE). We implemented and evaluated automated strategies for improving the quality of covariance estimates at different stages of data processing. Our results show that irrespective of the source localization method, accuracy can suffer from improper covariance estimation but can be improved by relying on automated regularization of covariance estimates.