Research on Decomposition of Surface EMG Signals Based on FastICA and Channel Correlation
Ning Yong
Li Jinrong
Zhu Shanan
Abstract：Objective A new and effective method for decomposing surface electromyography (sEMG) signals was explored for the relevant research,diagnosis and treatment of clinical neuromuscular diseases.Methods The FastICA method was employed to obtain the de-mixed matrix which was then used to transform the matrix of measurement.At last,the sEMG signals were decomposed by utilizing the channel correlations.Results Two groups of simulation signals and one group of real sEMG signals were tested and the results showed that for the first group of simulation signals with 0dB SNR,an average of 20 motor units were extracted with an average accuracy of 95.6％;While for the second group of simulation signals with 20 dB SNR,an average of 29 motor units were extracted with an average accuracy of 98.4％.As to the real sEMG signals,an average of 14.2motor units were extracted.A "two-source" test was further conducted to evaluate the performance of the proposed method,it showed that the proportion of the same MU extracted by both groups was 80％,and the average coincidence rate of the same MU was 95.1％.Conclusion The combination of FastICA and channel correlation method can effectively decompose the surface EMG signals with high accuracy.
Keywords：FastICAchannel correlationsurface electromyographymotor unit
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 191-197 )