基于肌肉协同的表面肌电通道选择方法

A Surface EMG Channel Selection Method Based on Muscle Synergy

将不同肌肉对运动任务的贡献度作为最优通道选择的优化准则,提出了基于肌肉协同(Muscle Synergy,MS)的通道选择方法。首先对原始肌电信号进行预处理,提取时域特征,然后使用非负矩阵分解(Non-Negative Matrix Factorization,NMF)算法分别对每个手势动作提取肌肉协同矩阵并进行转换;再将每个手势动作在各个肌电通道上的肌肉权重系数进行求和,得到所有肌电通道的重要性系数;最后通过支持向量机(SupportVectorMachines,SVM)、随机森林(Random Forest,RF)、K近邻分类器(K-Nearest Neighbor,KNN)进行分类。采用Ninapro数据库中DB5子数据库记录的表面肌电信号对该方法进行测试,测试结果表明,提取10个最优通道时,与以往研究中提出的顺序前向选择(Sequential Forward Selection,SFS)、马尔可夫随机场(Markov Random Field,MRF)和Relief-F通道选择方法相比,本文方法确定的肌电信号子集获得的识别精度与MRF和Relief-F方法相近,比SFS方法略低,但计算成本比它们均低。

In practical Electromyography(EMG)control,it is necessary to select appropriate number of surface EMG channels with ideal classification performance.In this paper,the contribution of different muscles to motor tasks is taken as the optimization criterion for optimal channel selection,and a channel selection method based on muscle synergy(MS)is proposed.Firstly,the raw EMG signal is preprocessed to extract EMG features,and then non-negative matrix factorization(NMF)algorithm is used to extract the muscle synergy matrix for each gesture and converted.Secondly,the muscle weight coefficients of each gesture on each EMG channel are summed to obtain the importance coefficients of all EMG channels.Finally,the classification is carried out by support vector machine(SVM),random forest(RF)and K-nearest neighbor(KNN)classifier.The method is tested by using surface EMG recorded in DB5 subdatabase of Ninapro database.The test results show that when extracting 10 optimal channels,compared with the sequential forward selection(SFS),Markov random field(MRF)and Relief-F channel selection methods proposed in previous studies,the recognition accuracy of the EMG subsets determined by this method is similar to that obtained by MRF and Relief-F method,and is slightly lower than that obtained by SFS method,but the computational cost is lower than that of SFS,MRF and Relief-F methods.