The ill-posed nature of the MEG (or related EEG) source localization problem requires the
incorporation of prior assumptions when choosing an appropriate solution out of an infinite
set of candidates. Bayesian approaches are useful in this capacity because they allow these
assumptions to be explicitly quantified using postulated prior distributions. However, the
means by which these priors are chosen, as well as the estimation and inference
procedures that are subsequently adopted to affect localization, have led to a daunting array …

… However, it is not always transparent how these methods relate, nor how they can be
extended to handle more challenging problems, nor which ones should be expected to
perform best in various situations relevant to MEG/EEG source imaging.APtarting from a
hierarchical Bayesian model constructed using Gaussian scale mixtures with flexible
covariance components, we analyze and, where possible, extend three broad classes of
Bayesian inference methods: \(\gamma \)-MAP, which involves integrating out the unknown …