Epilepsy Activities In The Detection Method And Applied Research

EEG and functional magnetic resonance imaging are two important non-invasive measurement techniques in detecting brain function, which is of great help for neural science, clinical diagnosis and treatment. The objective of the thesis is how to further expand the potential of these technologies, and effectively extract the information in the activities of brain function from EEG and functional magnetic resonance imaging data. It is of great significance in the research of the activities of brain function, and clinical diagnosis and treatment.This thesis is mainly concerned with several detection methods for epilepsy activities and its application as follows:The locating of epilepsy activity based on the independent component analysis (ICA). In this part data processing models about fMRI based on ICA are discussed. More specifically, models based on spatial ICA are utilized to deal with real epilepsy fMRI data, and it is successfully localized the activities of glioma which takes up the largest proportion in tumor. The results of our experiment are in accord with physiology and clinical diagnosis.Two different methods, ICA and wavelet, are proposed to deal with the real EEG data from epilepsy patients. They can effectively detect the spike, sharp and slow wave from EEG and are helpful in conducting the clinic diagnosis and treatment. Moreover, the result shows the Signal Noise Ratio (SNR) in wavelet is higher than that in ICA. So, wavelet have wider application prospect in detecting epilepsy.The vector autoregressive modeling and the Granger causality test are applied for investigating of effective connectivity between epileptic EEG focus. The Simulation and experiments on real data of epilepsy demonstrate the actual existence of Granger causality between epilepsy, as can favorably deepen the comprehension of dynamic operating way of our brains on a systematic level, and also could be a technical guidance for clinical diagnosis and therapy. Such an outcome from practical application of the vector autoregression and the Granger causality test confirms the effectiveness of the study of connectivity.In summary, based on the processing of EEG and functional magnetic resonance signals, this thesis applies several methods to localize the epilepsy activities and to investigate the connectivity. At last the results from our simulation and processing real data are compatible with that from physiology and pathology, and demonstrate the effect and research value of those methods.