CutFEM forward modeling for EEG source analysis

TL;DR

This paper introduces CutFEM, an innovative finite element method for EEG forward modeling that improves accuracy, efficiency, and geometric flexibility over traditional FEM approaches, enabling better brain source analysis.

Contribution

The paper presents CutFEM, a novel unfitted finite element method that combines the advantages of hexahedral and tetrahedral meshes for EEG forward simulations.

Findings

CutFEM achieves higher numerical accuracy than existing FEM methods.

CutFEM reduces memory usage and computational time.

CutFEM can mesh complex geometries with touching compartments effectively.

Abstract

Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes thus offer better accuracy, but are more difficult to create. Methods: We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory evoked potentials. Results: CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption and computational speed while being able to mesh arbitrarily touching compartments. Conclusion: CutFEM balances numerical accuracy, computational efficiency and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.