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Neuroimage. 2019 Jan 1;184:56-67. doi: 10.1016/j.neuroimage.2018.08.054. Epub 2018 Aug 28.

A realistic, accurate and fast source modeling approach for the EEG forward problem.

Author information

1
Laboratory of Mathematics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany, Malmedyweg 15, D-48149, Münster, Germany; Institute for Computational and Applied Mathematics, University of Münster, Germany, Einsteinstrasse 62, D-48149, Münster, Germany; Department of Applied Physics, University of Eastern Finland, P.O.Box 1627, FI-70211 Kuopio, Finland.
2
Laboratory of Mathematics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland. Electronic address: atena.rezaei@tut.fi.
3
Laboratory of Mathematics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland; Laboratory of Signal Processing, Tampere University of Technology, Tampere, Finland, P.O. Box 553, 33101, Tampere, Finland.
4
Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany, Malmedyweg 15, D-48149, Münster, Germany; Institute for Computational and Applied Mathematics, University of Münster, Germany, Einsteinstrasse 62, D-48149, Münster, Germany.
5
Institute for Computational and Applied Mathematics, University of Münster, Germany, Einsteinstrasse 62, D-48149, Münster, Germany.
6
Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany, Malmedyweg 15, D-48149, Münster, Germany.
7
Laboratory of Mathematics, Tampere University of Technology, P.O. Box 692, 33101, Tampere, Finland.

Abstract

The aim of this paper is to advance electroencephalography (EEG) source analysis using finite element method (FEM) head volume conductor models that go beyond the standard three compartment (skin, skull, brain) approach and take brain tissue inhomogeneity (gray and white matter and cerebrospinal fluid) into account. The new approach should enable accurate EEG forward modeling in the thin human cortical structures and, more specifically, in the especially thin cortices in children brain research or in pathological applications. The source model should thus be focal enough to be usable in the thin cortices, but should on the other side be more realistic than the current standard mathematical point dipole. Furthermore, it should be numerically accurate and computationally fast. We propose to achieve the best balance between these demands with a current preserving (divergence conforming) dipolar source model. We develop and investigate a varying number of current preserving source basis elements n (n=1,…,n=5). For validation, we conducted numerical experiments within a multi-layered spherical domain, where an analytical solution exists. We show that the accuracy increases along with the number of basis elements, while focality decreases. The results suggest that the best balance between accuracy and focality in thin cortices is achieved with n=4 (or in extreme cases even n=3) basis functions, while in thicker cortices n=5 is recommended to obtain the highest accuracy. We also compare the current preserving approach to two further FEM source modeling techniques, namely partial integration and St. Venant, and show that the best current preserving source model outperforms the competing methods with regard to overall balance. For all tested approaches, FEM transfer matrices enable high computational speed. We implemented the new EEG forward modeling approaches into the open source duneuro library for forward modeling in bioelectromagnetism to enable its broader use by the brain research community. This library is build upon the DUNE framework for parallel finite elements simulations and integrates with high-level toolboxes like FieldTrip. Additionally, an inversion test has been implemented using the realistic head model to demonstrate and compare the differences between the aforementioned source models.

KEYWORDS:

DUNE toolbox; Divergence conforming vector fields; Electroencephalography (EEG); Finite element method (FEM); Focal sources

[Indexed for MEDLINE]

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