A spatio-temporal solution for the EEG/MEG inverse problem using group penalization methods

Abstract

The inverse problem encountered in electroencephalography (EEG) and magnetoencephalography (MEG) studies refers to estimating neural activity given limited scalp-recorded data. We propose a spatio-temporal solution using group penalization approaches. This proposed method is based on the assumption that the underlying sources of EEG/MEG measurements are smooth in the temporal domain, and focal in the spatial domain. It transforms the spatio-temporal problem to a high-dimensional linear regression problem with grouped predictors using a basis expansion. Then an iterative group elastic net algorithm is utilized to localize and estimate the source time courses. The proposed approach is shown to be effective on simulations and human MEG studies.