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We study source localization from high dimensional M/EEG data by extending amultiscale method based on Entropic inference devised to increase the spatialresolution of inverse problems. This method is used to construct informativeprior distributions in a manner inspired in the context of fMRI (Amaral et al2004). We construct a set of renormalized lattices that approximate the cortexregion where the source activity is located and address the related problem ofdefining the relevant variables in a coarser scale representation of thecortex. The priors can be used in conjunction with other Bayesian methods suchas the Variational Bayes method (VB, Sato et al 2004). The central point of thealgorithm is that it uses a posterior obtained at a coarse scale to induce aprior at the next finer scale stage of the problem. We present results whichsuggest, on simulated data, that this way of including prior information is auseful aid for the source location problem. This is judged by the rate andmagnitude of errors in source localization. Better convergence times are alsoachieved. We also present results on public data collected during a facerecognition task.

Backward Renormalization Priors and the Cortical Source Localization Problem with EEG or MEG