Combined MEG and EEG source imaging by minimization of mutual information

Though very frequently assumed, the necessity to operate a joint processing of simultaneous magnetoencephalography (MEG) and electroencephalography (E EG) recordings for functional brain imaging has never been clearly demonstr ated. However, the very last generation of MEG instruments allows the simul taneous recording of brain magnetic fields and electrical potentials on the scalp. But the general fear regarding the fusion between MEG and EEG data is that the drawbacks from one modality will systematically spoil the perfo rmances of the other one without any consequent improvement. This is the ca se for instance for the estimation of deeper or radial sources with MEG. In this paper, we propose a method for a cooperative processing of MEG and EE G in a distributed source model. First, the evaluation of the respective pe rformances of each modality for the estimation of every dipole in the sourc e pattern is made using a conditional entropy criterion. Then, the algorith m operates a preprocessing of the MEG and EEG gain matrices which minimizes the mutual information between these two transfer functions, by a selectiv e weighting of the MEG and EEG lead fields. This new combined EEG/MEG modal ity brings major improvements to the localization:of active sources, togeth er with reduced sensitivity to perturbations on data.