Overview of Bayesian Solvers in EEG Distributed Source Models: Prior Selection, Algorithmic Implementation, and Depth Bias Reduction

Abstract

Electroencephalography (EEG) source imaging aims to reconstruct the spatial distribution of neural activity within the brain from non-invasive scalp measurements. This inverse problem is severely ill-posed due to the low spatial resolution of EEG and the presence of measurement noise, necessitating robust regularization techniques. Bayesian approaches provide a principled framework for incorporating prior knowledge into the solution, where regularization naturally arises through prior distributions and their associated hyperparameters. In this work, we provide an overview of key Bayesian methods for EEG source imaging based on Gaussian, Laplace, and group Laplace priors, with particular emphasis on hierarchical models that promote sparsity. We analyze the connections between these hierarchical formulations and classical optimization techniques, and provide an analytical description of their implementation using expectation -maximization and alternating optimization algorithms. To address the issue of depth bias where deeper sources are systematically underestimated or mislocalized - we extend a statistical signal-to-noise ratio (SNR) framework to derive depth-weighted priors that account for differences in how strongly sources at different depths are reflected in the measurements. Finally, we illustrate the behaviour of the considered models through simulation studies involving sources at varying depths. The results highlight the impact of prior selection and depth weighting on reconstruction accuracy and demonstrate the importance of informed model design for depth-sensitive EEG source localization.