  We study source localization from high dimensional M/EEG data by extending a
multiscale method based on Entropic inference devised to increase the spatial
resolution of inverse problems. This method is used to construct informative
prior distributions in a manner inspired in the context of fMRI (Amaral et al
2004). We construct a set of renormalized lattices that approximate the cortex
region where the source activity is located and address the related problem of
defining the relevant variables in a coarser scale representation of the
cortex. The priors can be used in conjunction with other Bayesian methods such
as the Variational Bayes method (VB, Sato et al 2004). The central point of the
algorithm is that it uses a posterior obtained at a coarse scale to induce a
prior at the next finer scale stage of the problem. We present results which
suggest, on simulated data, that this way of including prior information is a
useful aid for the source location problem. This is judged by the rate and
magnitude of errors in source localization. Better convergence times are also
achieved. We also present results on public data collected during a face
recognition task.
