11 research outputs found
A systematic study of head tissue inhomogeneity and anisotropy on EEG forward problem computing
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,KarhunenâLoeve expansion and stochastic
Galerkin methods. We apply Volume and Wangâs 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