Multiresolution imaging of neural currents from MEG data using an explicit piecewise image model

MEG source imaging is an underdetermined inverse problem that suffers from a large number of unknown source parameters in realistic source settings. While linear source estimators offer poor spatial resolution, most recent non-linear approaches still lead to untractable computational costs. We propose a new multiresolution approach using an explicit piecewise image model of the distribution of neural currents. At each spatial resolution, the ill-posedness of the iterative estimation procedure is reduced owing to a compact parametric current model for the mass neural activity of spatially-extended brain regions. This procedure can be considered as either an imaging approach per se, or a source-selection pre-processing step to more computationally-demanding estimators