Functions of nonlinear parameters, computed from electroencephalography (EEG) signals, in mental tasks classification were investigated, where the largest Lyapunov exponent, the mean period of trajectories and the average initial distance between neighboring trajectories were taken as the nonlinear parameters, and Fisher s linear discriminant was adopted as the classifier.
研究了非线性参数作为脑电(EEG)信号特征时对意识任务分类的作用,使用的3种非线性参数特征为最大Lyapunov指数、轨道平均周期和轨道平均初始距离,分类方法为Fisher线性判别式。