Sparse wavelet-based solutions for the M/EEG inverse problem

TL;DR

This paper introduces a wavelet-based sparse regularization method for solving the EEG and MEG inverse problem, improving source localization accuracy by promoting sparsity in the cortical source estimates.

Contribution

It proposes a novel approach combining spatial wavelet transforms with sparse regression techniques to enhance localization in neuro-electromagnetic inverse problems.

Findings

Effective localization on simulated data

Improved source sparsity and interpretability

Robust performance on real MEG data

Abstract

This paper is concerned with variational and Bayesian approaches to neuro-electromagnetic inverse problems (EEG and MEG). The strong indeterminacy of these problems is tackled by introducing sparsity inducing regularization/priors in a transformed domain, namely a spatial wavelet domain. Sparsity in the wavelet domain allows to reach ''data compression'' in the cortical sources domain. Spatial wavelets defined on the mesh graph of the triangulated cortical surface are used, in combination with sparse regression techniques, namely LASSO regression or sparse Bayesian learning, to provide localized and compressed estimates for brain activity from sensor data. Numerical results on simulated and real MEG data are provided, which outline the performances of the proposed approach in terms of localization.