Validation of EEG forward modeling approaches in the presence of anisotropy in the source space

TL;DR

This study evaluates FEM-based EEG forward modeling methods in anisotropic source spaces, highlighting the importance of modeling anisotropy for accurate EEG source analysis and recommending the Venant approach for its balance of accuracy and efficiency.

Contribution

It systematically compares three FEM approaches for modeling source space anisotropy in EEG, providing guidance on their accuracy, computational speed, and mesh requirements.

Findings

Anisotropy significantly affects electric potential calculations.

All three FEM methods can model anisotropy accurately with sufficient mesh refinement.

Venant approach offers the best compromise between accuracy and computational efficiency.

Abstract

The quality of the inverse approach in electroencephalography (EEG) source analysis is - among other things - depending on the accuracy of the forward modeling approach, i.e., the simulation of the electric potential for a known dipole source in the brain. Here, we use multilayer sphere modeling scenarios to investigate the performance of three different finite element method (FEM) based EEG forward approaches - subtraction, Venant and partial integration - in the presence of tissue conductivity anisotropy in the source space. In our studies, the effect of anisotropy on the potential is related to model errors when ignoring anisotropy and to numerical errors, convergence behavior and computational speed of the different FEM approaches. Three different source space anisotropy models that best represent adult, child and premature baby volume conduction scenarios, are used. Major findings of the study include (1) source space conductivity anisotropy has a significant effect on electric potential computation: The effect increases with increasing anisotropy ratio; (2) with numerical errors far below anisotropy effects, all three FEM approaches are able to model source space anisotropy accordingly, with the Venant approach offering the best compromise between accuracy and computational speed; (3) FE meshes have to be fine enough in the subdomain between the source and the sensors that capture its main activity. We conclude that, especially for the analysis of cortical development, but also for more general applications using EEG source analysis techniques, source space conductivity anisotropy should be modeled and the FEM Venant approach is an appropriate method.