基于信息扩散理论和模糊神经网络的肌电信号分解研究

目的：肌肉重度收缩时产生的混合相肌电信号由于各运动单元动作电位(MUAP)叠加形成的波形较多，易产生大样本和矛盾样本。尝试采用以信息扩散原理为基础构成的模糊人工神经网络来解决NEMG分解问题。方法：采用控制肌肉按线性变化力增长方式采集肌电信号的方法，可以建立MUAP模板，将模糊信息处理技术与神经网络技术相结合可以同时达到压缩样本和消去矛盾样本的目的。结果：利用这种方法对模拟和真实混合相肌电信号的分解进行实验研究，均取得了较好的效果。结论：这种方法对于研究重度收缩时的NEMG分解具有较大的意义。

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. 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.