四类运动想象脑电信号特征提取与分类算法

针对脑机接口(BCI)系统中存在的信息传输速率较慢和脑电信号识别正确率较低的问题,对多通道四类运动想象脑电信号进行研究.通过对4种运动想象及休息状态脑电信号进行功率谱分析,合理确定预处理滤波器的最佳滤波频段,然后使用PW-CSP,Hilbert变换及归一化处理的方法,对四类运动想象脑电信号进行特征提取,分类算法分为特征信号算术求和与阈值比较的预分类过程及包含单个支持向量机(SVM)的细分类过程,算法复杂度明显比采用多个SVM组合的多类分类算法要低,为实现算法的在线应用打下基础.仿真结果表明,该算法分类正确率高,时间开销小,并且可以通过调节阈值,在正确率与算法复杂度之间获得平衡.

Feature extraction and classification of four-class motor imagery EEG data

Due to the low information transfer rate and low recognition accuracy in brain computer interface(BCI),feature extraction and classification of multi-channel four-class motor imagery for electroencephalogram(EEG)-based BCI was investigated.Optimum filtering band was obtained for power spectral analysis of four-class motor imagery and resting EEG.Then,the PW-CSP,Hilbert transformation and normalization were used to extract the feature of EEG data.Classification was divided into two steps,the first step was arithmetic summation and threshold comparison,Secondly a single support vector machine(SVM) was applied if the first step failed.The algorithm was simpler than combined SVM,which provided the foundation for on-line application.The experimental results show that the algorithm produces high classification accuracy and less time consumption,moreover,classification result can be further improved at the expense of algorithmic complexity by adjust the threshold.