基于非线性参数的意识任务分类

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．对4个实验对象共60个任务对进行了分类处理．使用3种参数在2s数据段上取得的平均分类精度分别为82．3％、90．7％和93．3％．在较短（1s）的数据段上，应用轨道平均初始距离参数进行分类取得了平均为90．8％的正确率，分类精度接近于Anderson取得的实验结果．轨道平均周期和轨道平均初始距离算法具有较小的运算量，能够应用于在线系统．

Mental Tasks Classification Based on Nonlinear Parameters

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. There were a total of 60 task pairs from 4 subjects for classification. The average classification accuracy obtained on 2-second EEG segments reached to 82.3%, 90.7%, and 93.3% for the above three parameters respectively. With the third parameter, the average accuracy of 90.8% was achieved on 1-second EEG segments, which approached favorably to the results of Anderson, et al. The methods of mean period and average initial distance of trajectories with computationally less demanding can be used for online analysis.