title: Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns.
abstract: BACKGROUND
  Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA).
  
  
  METHODS
  HD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features.
  
  
  RESULTS
  Compared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2).
  
  
  CONCLUSIONS
  The results demonstrated that the CSP features significantly improved the robustness against electrode shift for myoelectric control with respect to the commonly used features.
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. melzpan@sjtu.edu.cn.
authors.forename: Lizhi
authors.initials: L
authors.lastname: Pan
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. dgzhang@sjtu.edu.cn.
authors.forename: Dingguo
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Department of Systems Design Engineering, Center for Bioengineering & Biotechnology, University of Waterloo, Waterloo, Canada. ning.jiang@uwaterloo.ca.
authors.forename: Ning
authors.initials: N
authors.lastname: Jiang
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. xjsheng@sjtu.edu.cn.
authors.forename: Xinjun
authors.initials: X
authors.lastname: Sheng
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. mexyzhu@sjtu.edu.cn.
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000328:Adult; D000465:Algorithms; D004566:Electrodes; D004576:Electromyography / Q000379:methods*; D005260:Female; D005542:Forearm / Q000502:physiology; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*