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  Forward modeling and tissue conductivities

Haueisen, J., & Knösche, T. R. (2019). Forward modeling and tissue conductivities. In S. Supek (Ed.), Magnetoencephalography: From signals to dynamic cortical networks (pp. 145-165). Cham: Springer. doi:10.1007/978-3-030-00087-5_4.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-3DE6-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-3E27-B
Genre: Book Chapter

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 Creators:
Haueisen, Jens1, Author
Knösche, Thomas R.2, Author              
Affiliations:
1Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Germany, ou_persistent22              
2Methods and Development Unit - MEG and Cortical Networks, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205650              

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Free keywords: Volume conduction; Field computation; EEG/MEG modeling; BEM; FEM
 Abstract: The neuroelectromagnetic forward model describes the prediction of measurements from known sources. It includes models for the sources and the sensors as well as an electromagnetic description of the head as a volume conductor, which are discussed in this chapter. First we give a general overview on the forward problem and discuss various simplifications and assumptions that lead to different analytical and numerical methods. Next, we introduce important analytical models which assume simple geometries of the head. Then we describe numerical models accounting for realistic geometries. The most important numerical methods for head modeling are the boundary element method (BEM) and the finite element method (FEM). The boundary element method describes the head by a small number of compartments, each with a homogeneous isotropic conductivity. In contrast, the finite element method discretizes the 3D distribution of the anisotropic conductivity tensor with the help of small-volume elements. Subsequently, we discuss in some detail how electrical conductivity information is measured and how it is used in forward modeling. Finally, we briefly introduce the lead field concept.

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Language(s): eng - English
 Dates: 2019-10-18
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1007/978-3-030-00087-5_4
 Degree: -

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Title: Magnetoencephalography: From signals to dynamic cortical networks
Source Genre: Book
 Creator(s):
Supek, Selma 1, Editor
Aine, Cheryl J. 1, Author
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Cham : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 145 - 165 Identifier: ISBN: 978-3-030-00086-8
DOI: 10.1007/978-3-030-00087-5