The Effects of Peeling on Finite Element Method -based EEG Source Reconstruction

TL;DR

This study investigates how removing potential source locations from brain layer surfaces influences EEG source localization accuracy using finite element models and inverse methods like sLORETA and Dipole Scan, especially under varying noise conditions.

Contribution

It demonstrates that source removal can slightly improve localization accuracy in finite element EEG models, particularly at lower noise levels, and discusses the impact of inverse algorithms and brain compartments.

Findings

Source removal slightly improves localization accuracy.

Lower noise levels benefit more from source removal.

Inverse method and brain compartment choice affect results.

Abstract

The problem of reconstructing brain activity from electric potential measurements performed on the surface of a human head is not an easy task: not just because the solution of the related inverse problem is fundamentally ill-posed (not unique), but because the methods utilized in constructing a synthetic forward solution themselves contain many inaccuracies. One of these is the fact that the usual method of modelling primary currents in the human head via dipoles brings about at least 2 modelling errors: one from the singularity introduced by the dipole, and one from placing such dipoles near conductivity discontinuities in the active brain layer boundaries. In this article we observe how the removal of possible source locations from the surfaces of active brain layers affects the localisation accuracy of two inverse methods, sLORETA and Dipole Scan, at different signal-to-noise ratios (SNR), when the H(div) source model is used. We also describe the finite element forward solver used to construct the synthetic EEG data, that was fed to the inverse methods as input, in addition to the meshes that were used as the domains of the forward and inverse solvers. Our results suggest that there is a slight general improvement in the localisation results, especially at lower noise levels. The applied inverse algorithm and brain compartment under observation also affect the accuracy.