FDM In 3D EEG Forward Problem

The research of Electroencephalogram (EEG) source analysis is of great significance and clinical importance in the study of cognitive function and neural activity of a brain. There are two key problems in the field of EEG source analysis, EEG forward problem and EEG inverse problem. EEG forward problem is the basis of the EEG inverse problem. Generally, EEG inverse problem depends on the accuracy and efficiency of the computational method of EEG forward problem, which is determined by the geometry and conductive properties of the tissues within the head. A realistic head model can be built by Finite Difference Method (FDM) which can cope with complex geometry, boundary conditions, multi homogeneous media and inhomogeneous media. This thesis concentrates on FDM for three-dimensional (3D) EEG forward problem with the following achievements:The FDM has been implemented to solve the 3D isotropic and anisotropic EEG forward problem. This method has been evaluated by computer simulations, by comparing with analytic solutions in a three-concentric-sphere head model. Finite Element Method (FEM) and Finite Difference Method (FDM) were compared for resolution of 3D isotropic EEG forward problem, from the point of view of computational accuracy, efficiency and complexity.The correspondence between MRI and FDM models was established and the reference model of homogeneous media has been obtained. The FDM was used to model the head volume conductivity with a hole of varying size. The effects of the holes on scalp EEG and ECoG were assessed by comparing the forward potential distributions with and without the hole.The white matter (WM) anisotropy electrical conductivity tensors were obtained from the DT-MRI data by volume constraints. The FDM was used to mode] the head volume conductivity with three kinds of anisotropy inhomogeneous WM. The effects of anisotropy inhomogeneous WM on EEG were assessed by comparing the forward potential distributions in homogeneous and inhomogeneous models.