A Scheme of sEMG Feature Extraction for Improving Myoelectric Pattern Recognition

This paper proposed a feature extraction scheme based on sparse representation considering the nonstationary property of surface electromyography（ sEMG）. Sparse Bayesian Learning（ SBL） algorithm was introduced to extract the feature with optimal class separability to improve recognition accuracies of multimovement patterns. The SBL algorithm exploited the compressibility（ or weak sparsity） of sEMG signal in some transformed domains. The proposed feature extracted by using the SBL algorithm was named SRC. The feature SRC represented time-varying characteristics of sEMG signal very effectively. We investigated the effect of the feature SRC by comparing with other fourteen individual features and eighteen multi-feature sets in offline recognition. The results demonstrated the feature SRC revealed the important dynamic information in the sEMG signals. And the multi-feature sets formed by the feature SRC and other single features yielded more superior performance on recognition accuracy. The best average recognition accuracy of 91. 67% was gained by using SVM classifier with the multi-feature set combining the feature SRC and the feature wavelength（ WL）. The proposed feature extraction scheme is promising for multi-movement recognition with high accuracy.