Finite-element model of the human head: scalp potentials due to dipole sources

Yan, Ying Dong; Nunez, Paul L.; Hart, Richard T.

doi:10.1007/bf02442317

Cited by 181 publications

(96 citation statements)

References 9 publications

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“…The Poisson equation in eq(2) and eq(3) are solved by means of FEM. For the modelling of the current source, we use 'single dipole', which has been introduced by Yan et al 20 . We use a standard variation procedure to transform the Poisson equation from the quasistatic Maxwell's equations into an algebraic system of linear equations.…”

Section: Forward Computationmentioning

confidence: 99%

Influence of white matter inhomogeneous anisotropy on EEG forward computing

Bashar

Wen

2008

Australas. Phys. Eng. Sci. Med.

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In this paper, we model the human head using the Volume and Wang's constraint methods, and study the inhomogeneous anisotropic conductivity for white matter (WM) using finite element method (FEM). To represent the WM accurately, the conductivity ratio approximation (CRA) and statistical conductivity approximation (SCA) techniques are applied to assign inhomogeneous anisotropic conductivity. This model is evaluated and compared with a homogeneous isotropic model and a homogeneous anisotropic model. The results show that the effects of inhomogeneous anisotropic conductivity of WM on the scalp EEG are significant.

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Section: Forward Computationmentioning

confidence: 99%

Influence of white matter inhomogeneous anisotropy on EEG forward computing

Bashar

Wen

2008

Australas. Phys. Eng. Sci. Med.

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“…As found in previous simulation studies, the results depend mainly on the qualitative property of poor skull conductivity in this range. The large uncertainty in estimates of skull conductivity in any head model whether concentric or realistic, is far more important than the errors introduced by approximating the geometry of the head, which have been estimated at 10-15 % in simulation studies that compare spherical models to realistic Finite Element models (Yan et al 1991).…”

Section: Spatial Filtering Of Scalp Potentialsmentioning

confidence: 99%

Source analysis of EEG oscillations using high-resolution EEG and MEG

Srinivasan

Winter

Nunez

2006

Progress in Brain Research

112

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We investigated spatial properties of the source distributions that generate scalp electroencephalographic (EEG) oscillations. The inherent complexity of the spatiotemporal dynamics of EEG oscillations indicates that conceptual models that view source activity as consisting of only of a few "equivalent dipoles" are inadequate. We present an approach that uses volume conduction models to characterize the distinct spatial filtering of cortical source activity by averagereference EEG, high-resolution EEG, and magnetoencephalography (MEG). By comparing these three measures, we can make inferences about the sources of EEG oscillations without having to make prior assumptions about the sources. We apply this approach to spontaneous EEG oscillations observed with eyes closed at rest. Both EEG and MEG recordings show robust alpha rhythms over posterior regions of the cortex; however, the dominant frequency of these rhythms varies between EEG and MEG recordings. Frontal alpha and theta rhythms are generated almost exclusively by superficial radial dipole layers that generate robust EEG signals but very little MEG signals; these sources are presumably mainly in the gyral crowns of frontal cortex. MEG and high-resolution EEG estimates of alpha rhythms provide evidence of local tangential and radial sources in the posterior cortex, lying mainly on sulcal and gyral surfaces. Despite the detailed information about local radial and tangential sources potentially afforded by high-resolution EEG and MEG, it is also evident that the alpha and theta rhythms receive contributions from non-local source activity, for instance large dipole layers distributed over lobeal or (potentially) even larger spatial scales.

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“…Three-dimensional finite elements, such as tetrahedrons or cubes, can be used in order to subdivide the relevant volumes for the purpose of integration. Although this method is one of the most successful methods for numerically solving potential differential equations, it was only recently used to solve volume conduction problems (Bertrand et al 1991;Yan 1991).…”

Section: Finite Element Methodsmentioning

confidence: 99%