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Updated: Aug 29, 2026
Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Published on: July 26, 2013
[Decomposition of EMG signals based on combination of information diffusion theory and fuzzy neural network]
Xiao-jin Qian1, Ji-hai Yang, Zheng Liang
1Department of Electronic Science and Technology, University of Science & Technology of China (USTC), Hefei, Anhui, China. qxjhf@sina.com
Objective:
To solve the problem of large samples and contradictory samples in EMG during high level muscle contraction.
Method:
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.
Result:
The method was tested by simulated and real EMG data and the results were satisfactory.
Conclusion:
This method is meaningful for decomposing NEMG at high level muscle contraction.