Smoothness Constraint For the Estimation of Current Distribution From EEG/MEG Data

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 biologically plausible constraint, sparseness of spatial difference of the current distribution. 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