Multiresolution framework to MEG/EEG source imaging

A new method based on a multiresolution approach for solving the ill-posed problem of brain electrical activity reconstruction from electroencephalora m (EEG)/magnetoencephalogram (MEG) signals is proposed in a distributed sou rce model. At each step of the algorithm, a regularized solution to the inv erse problem is used to constrain the source space on the cortical surface to be scanned at higher spatial resolution. We present the iterative proced ure together with an extension of the ST-maximurn a posteriori method [1] t hat integrates spatial and temporal a priori information in an estimator of the brain electrical activity. Results from EEG in a phantom head experime nt with a real human skull and from real MEG data on a healthy human subjec t are presented. The performances of the multiresolution method combined wi th a nonquadratic estimator are compared with commonly used dipolar methods , and to minimum-norm method with and without multiresolution. In all cases , the proposed approach proved to be more efficient both in terms of comput ational load and result quality, for the identification of sparse focal pat terns of cortical current density, than the fixed scale imaging approach.