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ORIGINAL RESEARCH article

Front. Syst. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fnsys.2024.1327674

A Hybrid Boundary Element-Finite Element Approach for Solving the EEG Forward Problem in Brain Modeling Provisionally Accepted

  • 1Department of Biomedical Engineering, School of Electrical Engineering, K.N.Toosi University of Technology, Iran
  • 2School of Electrical Engineering, K.N.Toosi University of Technology, Iran

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This article presents a hybrid boundary element-finite element (BE-FE) method to solve the EEG forward problem and takes advantage of both the boundary element method (BEM) and finite element method (FEM). The Finite Element Method (FEM) offers the advantage of accurate modeling of complex and anisotropic tissue properties, making it suitable for EEG forward problems in realistic head geometries. On the other hand, the Boundary Element Method (BEM) excels in handling isotropic tissue regions and dipolar sources with a simplified mesh, leading to computational efficiency. This work utilizes both FEM and BEM strengths attained by dividing the regions into some homogeneous BE regions with sources and some heterogeneous and anisotropic FE regions. Furthermore, the BEM is applied for modeling the brain, including dipole sources, and the FEM for other head layers. To validate the proposed method, inhomogeneous isotropic/anisotropic three-and four-layer spherical head models are studied. Moreover, a four-layer MRI-based realistic head model is investigated. Results for six different dipole eccentricities and two different dipole orientations are computed using the BEM, FEM, and hybrid BE-FE method together with statistical analysis, and the related error criteria are compared. The proposed method is a promising new approach for solving realistic EEG forward problems paving the way for improved neuroimaging techniques and enhanced understanding of brain function.

Keywords: EEG forward problem, Finite element method, boundary element method, Hybrid BE-FE method, MRI-based realistic head model

Received: 25 Oct 2023; Accepted: 22 Feb 2024.

Copyright: © 2024 Dayarian and Khadem. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Ali Khadem, School of Electrical Engineering, K.N.Toosi University of Technology, Tehran, Iran