A systematic study of head tissue inhomogeneity and anisotropy on EEG forward problem computing

Bashar, M. R. and Li, Y. and Wen, P. (2010) A systematic study of head tissue inhomogeneity and anisotropy on EEG forward problem computing. Australasian Physical and Engineering Sciences in Medicine, 33 (1). pp. 11-21. ISSN 0158-9938


In this study, we propose a stochastic method to
analyze the effects of inhomogeneous anisotropic tissue
conductivity on electroencephalogram (EEG) in forward
computation. We apply this method to an inhomogeneous
and anisotropic spherical human head model. We apply
stochastic finite element method based on Legendre polynomials,Karhunen–Loeve expansion and stochastic
Galerkin methods. We apply Volume and Wang’s constraints
to restrict the anisotropic conductivities for both the
white matter (WM) and the skull tissue compartments. The
EEGs resulting from deterministic and stochastic FEMs are
compared using statistical measurement techniques. Based
on these comparisons, we find that EEGs generated by
incorporating WM and skull inhomogeneous anisotropic
tissue properties individually result in an average of 56.5
and 57.5% relative errors, respectively. Incorporating these
tissue properties for both layers together generate 43.5%
average relative error. Inhomogeneous scalp tissue causes
27% average relative error and a full inhomogeneous
anisotropic model brings in an average of 45.5% relative
error. The study results demonstrate that the effects of
inhomogeneous anisotropic tissue conductivity are significant on EEG.

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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Author version not held.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 12 Dec 2010 05:35
Last Modified: 03 Jul 2013 00:04
Uncontrolled Keywords: EEG; forward computation; human head modelling; tissue conductivity; inhomogeneity; anisotropy and stochastic FEM
Fields of Research : 06 Biological Sciences > 0699 Other Biological Sciences > 069999 Biological Sciences not elsewhere classified
01 Mathematical Sciences > 0104 Statistics > 010406 Stochastic Analysis and Modelling
11 Medical and Health Sciences > 1109 Neurosciences > 110999 Neurosciences not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: 10.1007/s13246-010-0009-5
URI: http://eprints.usq.edu.au/id/eprint/8863

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