| EEG signal is an important method in clinical disease diagnosed,neurophysiology,brain research,etc. Automatic detection of epileptic waves in EEG signal and EEG signal prediction is of great significance for clinical application,long-term monitoring (i.e. EEG Holter) and treatment and control of epilepsy.EEG signal is characterized by high-dimension chaotic. The prediction of chaotic signal is an importance area of chaos theory and its application. However,most existing research results are main about the prediction of the low-dimension chaotic signal,we investigate the prediction of high-dimension chaotic EEG signal in this paper. Artificial neural network has excellent performance in adapting ,nonlinear mapping etc.,and it is widely used to predict chaotic signal. The Volterra series expansion has altitudinal nonlinearly ,most nonlinear system can be expressed by the Volterra series expansion. So we adopt an Radial Basic Function neural network and a three-order Volterra series filter to construct two prediction model which are used to predict the EEG signal. In this paper,we improve the Radial Basic Function neural network and breach the two-order limited by using product-coupling to realize the three-order Volterra series filter.Wavelet transform is an analytical method that unites the time and frequency domain. It has such feathers as multi-resolution,constant relative bandwidth,and the ability to indicate the local features of signal in time and space. After wavelet transform by using proper wavelet basis functions,the epileptic waves can beseparated at different scale,then we can detect the epileptic waves by using the wavelet transform result at the proper threshold value.Simulation results show that:the two prediction model and the method to detached the epileptic waves by using the wavelet transform is effective. Especially,the prediction precision of EEG signal is higher than 10-3 using the three-order Volterra series filter.
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