New Strategy for Finite Element Mesh Generation for Accurate Solutions of Electroencephalography Forward Problems

Citations

WEB OF SCIENCE

1
Citations

SCOPUS

3

초록

The finite element method (FEM) is a numerical method that is often used for solving electroencephalography (EEG) forward problems involving realistic head models. In this study, FEM solutions obtained using three different mesh structures, namely coarse, densely refined, and adaptively refined meshes, are compared. The simulation results showed that the accuracy of FEM solutions could be significantly enhanced by adding a small number of elements around regions with large estimated errors. Moreover, it was demonstrated that the adaptively refined regions were always near the current dipole sources, suggesting that selectively generating additional elements around the cortical surface might be a new promising strategy for more efficient FEM-based EEG forward analysis.

키워드

ElectroencephalographyFinite element methodError estimationAdaptive mesh generationForward problemNEUROMAGNETIC FIELDSSOURCE LOCALIZATIONERROR ESTIMATIONHUMAN HEADEEGMODELCOMPUTATIONPOTENTIALSSOLVE
제목
New Strategy for Finite Element Mesh Generation for Accurate Solutions of Electroencephalography Forward Problems
저자
Lee, ChanyIm, Chang-Hwan
DOI
10.1007/s10548-018-0669-0
발행일
2019-05
유형
Article
저널명
Brain Topography
32
3
페이지
354 ~ 362