Onset detection in surface electromyographic signals: a systematic comparison of methods
EURASIP Journal on Applied Signal Processing
Classification of surface EMG signal using relative wavelet packet energy
Computer Methods and Programs in Biomedicine
An approach to surface EMG decomposition based on higher-order cumulants
Computer Methods and Programs in Biomedicine
A simulation model of the surface EMG signal for analysis of muscle activity during the gait cycle
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders
Computers in Biology and Medicine
Computers in Biology and Medicine
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Muscle coordination during periodic movements is often studied using the average envelope of the electromyographic (EMG) signal. We show that this method causes a loss of important information, and potentially gives rise to errors in analysis of muscle activity coordination. We created four simulated two-channel surface EMG signals, in order to compare the results of muscle onset/cessation detection, performed on the average EMG envelope and the EMG envelopes in every single movement cycle. Our results show that the common method using the average EMG envelope is unable to reveal certain important characteristics of the EMG signals, while the analysis performed on individual cycles accentuates this information. This ability was verified on 16-channel surface EMGs obtained during walking and cycling. By detecting muscle activity in individual movement cycles, we could observe fine changes in muscle coordination. Moreover, muscles with questionable reliability of activity detection were distinguished and highlighted in the presented summary figures. In the second part of the paper, our publicly available set of MATLAB files for surface EMG signal processing is described.