Neuroelectric current localization from combined EEG/MEG data

EEG/MEG devices record external signals which are generated by the neuronal electric activity of the brain. The localization of the neuronal sources requires the solution of the neuroelectromagnetic inverse problem which is highly ill-posed and ill-conditioned. We provide an iterative thresholding algorithm for recovering neuroeletric current densities within the brain through combined EEG/MEG data. We use a joint sparsity constraint to promote solutions localized in small brain area, assuming that the vector components of the current densities possess the same sparse spatial pattern. At each iteration step, the EEG/MEG forward problem is numerically solved by a Galerkin boundary element method. Some numerical experiments on the localization of current dipole sources are also given. The numerical results show that joint sparsity constraints outperform classical regularization methods based on quadratic constraints. © 2012 Springer-Verlag