Estimation of current density distributions from EEG/MEG data by maximizing sparseness of spatial difference

Separation of EEG (electroencephalography) or MEG (magnetoencephalography) data into activations of small dipoles or current density distribution is an ill-posed problem in which the number of parameters to estimate is larger than the dimension of the data. Several constraints have been proposed and used to avoid this problem, such as minimization of the L1-norm of the current distribution or minimization of Laplacian of the distribution. In this paper, we propose another constraint that the current density distribution changes at only a small number of areas and these changes can be large. By numerical experiments, we show that the proposed method estimates current distribution well from both data generated by strongly localized current distributions and data generated by currents broadly distributed