title: [Decomposition of EMG signals based on combination of information diffusion theory and fuzzy neural network].
abstract: OBJECTIVE
  To solve the problem of large samples and contradictory samples in EMG during high level muscle contraction.
  
  
  METHODS
  By means of recording EMG during muscle contraction with linearly increasing force instead of constant force, basic MUAP templates were obtained with the combination of information diffusion theory and fuzzy neural network. Samples were compressed and contradictory samples were eliminated.
  
  
  RESULTS
  The method was tested by simulated and real EMG data and the results were satisfactory.
  
  
  CONCLUSIONS
  This method is meaningful for decomposing NEMG at high level muscle contraction.
authors.affiliation: Department of Electronic Science and Technology, University of Science & Technology of China (USTC), Hefei, Anhui, China. qxjhf@sina.com
authors.forename: Xiao-jin
authors.initials: XJ
authors.lastname: Qian
authors.forename: Ji-hai
authors.initials: JH
authors.lastname: Yang
authors.forename: Zheng
authors.initials: Z
authors.lastname: Liang
authors.forename: Xiang
authors.initials: X
authors.lastname: Chen
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
authors.forename: Huan-qing
authors.initials: HQ
authors.lastname: Feng
mesh_terms: D003198:Computer Simulation; D004576:Electromyography; D007091:Image Processing, Computer-Assisted; D007257:Information Theory; D009119:Muscle Contraction / Q000502:physiology*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted