title: Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom.
abstract: OBJECTIVE
  Recent studies have shown the possibility of simultaneous and proportional control of electrically powered upper-limb prostheses, but there has been little investigation on optimal channel selection. The objective of this study is to find a robust channel selection method and the channel subsets most suitable for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom (DoFs).
  
  
  METHODS
  Ten able-bodied subjects and one person with congenital upper-limb deficiency took part in this study, and performed wrist movements with various combinations of two DoFs (flexion/extension and radial/ulnar deviation). During the experiment, high density electromyographic (EMG) signals and the actual wrist angles were recorded with an 8 × 24 electrode array and a motion tracking system, respectively. The wrist angles were estimated from EMG features with ridge regression using the subsets of channels chosen by three different channel selection methods: (1) least absolute shrinkage and selection operator (LASSO), (2) sequential feature selection (SFS), and (3) uniform selection (UNI).
  
  
  RESULTS
  SFS generally showed higher estimation accuracy than LASSO and UNI, but LASSO always outperformed SFS in terms of robustness, such as noise addition, channel shift and training data reduction. It was also confirmed that about 95% of the original performance obtained using all channels can be retained with only 12 bipolar channels individually selected by LASSO and SFS.
  
  
  CONCLUSIONS
  From the analysis results, it can be concluded that LASSO is a promising channel selection method for accurate simultaneous and proportional prosthesis control. We expect that our results will provide a useful guideline to select optimal channel subsets when developing clinical myoelectric prosthesis control systems based on continuous movements with multiple DoFs.
authors.affiliation: Machine Learning Group, Berlin Institute of Technology (TU Berlin), Marchstrasse 23, 10587 Berlin, Germany.
authors.forename: Han-Jeong
authors.initials: HJ
authors.lastname: Hwang
authors.forename: Janne Mathias
authors.initials: JM
authors.lastname: Hahne
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000328:Adult; D001186:Artificial Limbs; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D025461:Feedback, Physiological; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D009068:Movement; D009069:Movement Disorders / Q000503:physiopathology* / Q000534:rehabilitation; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D004856:Postural Balance; D016059:Range of Motion, Articular; D014955:Wrist Joint / Q000503:physiopathology*; D055815:Young Adult