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

Abstract

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.

EEG Analysis on Skull Conductivity Perturbations Using Realistic Head Model

“…Among the head tissue layers, skull shows the lowest conductivity due to its complicated bone structure. The electric potential originated from a current source inside the brain surrounding through the low conductive skull to the higher conductive scalp is known as forward computation [1]- [4]. Therefore, accurate representation and estimations of skull electrical conductivity are essential in developing appropriate EEG forward computation.…”