基于支持向量机多分类的眼电辅助肌电的人机交互

Electrooculogram assisted electromyography human-machine interface system based on multi-class support vector machine

针对单一肌电信号在控制系统中正确识别率不高问题,设计并实现了一种基于支持向量机(SVM)多分类的眼电(EOG)辅助肌电(EMG)的人机交互(HCI)系统。该系统采用改进小波包算法和阈值法分别对EMG信号和EOG信号进行特征提取,并对特征向量融合;然后提取特征参数作为SVM的输入来识别EMG信号和EOG信号动作模式,根据分类结果生成控制命令。实验证明,该系统比单一肌电控制系统更便于操作,稳定性好,正确识别率高。

Concerning the low correct recognition rate of the Electromyography（ EMG） control system, a new HumanComputer Interaction（ HCI） system based on Electrooculogram（ EOG） assisted EMG was designed and implemented. The feature vectors of EOG and EMG were extracted by threshold method and improved wavelet transform separately, and the feature vectors were integrated together. Then the features were classified by multi-class Support Vector Machine（ SVM）, and the different control commands were generated according to the result of pattern recognition. The experimental results prove that, compared with the single EMG control system, the new system has better operability and stability with higher correct recognition rate.