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

TL;DR

This paper introduces a multiscale Bayesian approach using backward renormalization priors to improve source localization accuracy and convergence in high-dimensional EEG/MEG data analysis, demonstrated on simulated and real face recognition data.

Contribution

It develops a novel multiscale method that propagates coarse-scale posteriors to refine priors at finer scales for EEG/MEG source localization.

Findings

Improved accuracy in source localization on simulated data.

Faster convergence times in Bayesian inference.

Effective application to real face recognition EEG data.

Abstract

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.