A discriminant bispectrum feature for surface electromyogram signal classification.
This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies-Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%.
Chen
Xinpu
X
Institute of Robotics, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Shanghai 200240, China. chenxinpu_cn@yahoo.com.cn
Zhu
Xiangyang
X
Zhang
Dingguo
D
D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D006225:Hand / Q000502:physiology; D006801:Humans; D008297:Male; D009068:Movement; D015999:Multivariate Analysis; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D014953:Wrist / Q000502:physiology