As opposed to the well-posed forward modeling and computation, the EEG/MEG inverse problem is more challenging. Such a problem is to estimate the source signals s(t) from EEG or MEG measurements x(t). As the number of EEG/MEG sensors is in general smaller than the number of sources within the brain, the EEG/MEG inverse solution is non-unique if no constraints are given. Over the past two decades, a number of efforts have been made to tackle this challenge. Evidence has increasingly indicated that reasonable EEG/MEG inverse solutions can be obtained if appropriate constraints, as derived from brain anatomy and physiology, are placed on the source distribution. For instance, one may specify the optimal solution as the most energy efficient one among those fitting equally well with the data. In a noise-free condition, this constraint leads to the linear least-squares source estimate. In noisy conditions, this constraint can be incorporated as a minimum norm side constraint, giving rise to the minimum norm estimate (MNE) [81]. Other variations of the MNE include the lead-field normalized weighted minimum norm (WMN) [82], low-resolution brain electromagnetic
