[Decomposition of EMG signals based on combination of information diffusion theory and fuzzy neural network].
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.
Qian
Xiao-jin
XJ
Department of Electronic Science and Technology, University of Science & Technology of China (USTC), Hefei, Anhui, China. qxjhf@sina.com
Yang
Ji-hai
JH
Liang
Zheng
Z
Chen
Xiang
X
Zhou
Ping
P
Feng
Huan-qing
HQ
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