These methods have limitations in both technical and fundamental aspects. Technically, the fMRI weighting factor is most frequently up to users' subjective choice, although several empirical values have been suggested to be 10 [20], 3 [135] or 1.4 [134] based on results obtained in several simulation studies. This technical limitation may be solvable by using data-driven methods for choosing the fMRI weighting factor, such as the expectation maximization (EM) algorithm [137, 138, 140]. Moreover, we may bypass this uncertainty by using an alternative two-step estimation algorithm which avoids using the fMRI weighting factor [139]. In the first step, the EEG/MEG source space is strictly confined to the regions highlighted in fMRI, giving rise to an inverse solution firmly constrained by the fMRI. In the second step, the fMRI constraint is removed, and the solution obtained in the first step is re-entered as the initial solution to fit the EEG/MEG data again employing a so-called Twomey regularization [141].
