| The research of EEG is of great significance and clinical importance in the study of the neural activity and cognitive function of a brain. The calculation of EEG forward problem based on Finite Element Method (FEM) is an important tool in the research of EEG. The prerequisite of using FEM is the acquisition of the model that can accurately depict the configuration of a human head. However, the current methods are either at a pretty low level of automation and difficult to repeat, or require large approximations and smoothing process to the head tissues. Considering the accuracy requirement of clinical application, the FEM modeling of realistic human head is still a challenging topic.According to the above reasons. a new modeling procedure that based on multi-source data was developed in this thesis by improving several medical image process technologies. The follows are the main achievements of the thesis:1 . By improving several image segmentation methods, the scalp, skull, cerebrospinal fluid (CSF). white matter and gray matter are obtained automatically from CT and MRI images.2. The image co-registration method based on contour comparing and the image co-registration method based on the principle of mechanics decomposition are developed. By using these methods, the CT and MRI images are co-registered. As a result, the advantages of both CT and MRI images are fused into data to construct the model.3. Two kinds of finite element mesh generation methods that target the volume containing complex tissues are developed. By using these methods, the finite element model of realistic human head, which contains live tissues, is obtained.4. The finite element model obtained by the proposed modeling method is applied to solve EEG forward problem. The feasibility of the method is proved by the simulations for several sets of data. And the reliability of the model is also validated according to the experiments of the sphere model and the realistic head model.
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