Liley模型的模拟EEG信号的非线性预测和分析

Nonlinear Prediction and Analysis of EEG in a Liley Model

分析Liley模型的模拟脑电(Electroencephalogram,EEG)信号的非线性预测和径向基函数(Radial Basis Functions,RBF)神经网络预测,利用相图分析和非线性正交预测(Nonlinear Cross-Prediction,NLCP)方法研究模拟EEG信号.结果发现:①RBF神经网络预测的效果要好于非线性预测;②NLCP方法对含有强周期分量的高维系统具有较好的适用性;③支持了EEG中存在混沌运动的观点.

Nonlinear prediction and RBF（Radial Basis Functions） neural network prediction of EEG（Electroencephalogram） signal in a Liley model are studied by phase graph and NLCP（Nonlinear Cross-Prediction）.It concluded that： 1） RBF neural network prediction is better than nonlinear prediction;2） NLCP method is adaptive to time series with strong periodic components;3） support the exist of chaos in EEG signals.