FastICA-Comb: A Novel Algorithm for Extracting Voluntary Electromyography and M-Wave in Functional Electrical Stimulation Scenarios

Functional electrical stimulation (FES) is commonly used in clinical practice to induce muscle contractions for rehabilitation therapy. However, the collected electromyography (EMG) under FES is a mixed signal containing voluntary electromyography (VEMG), which is generated by neural system recruited motor units, and FES response, which is composed of initial spikes generated by current passage and M-waves generated by stimulation recruited motor units. To realize close-loop control of FES system or to reveal the mechanism of FES through EMG signal, it is necessary to separate the three components from mixed signal. Based on the assumption that signal sources of initial spikes and action potentials related signals (VEMG and M-waves) are independent of each other, this study presents a novel mixed FES signal decomposition algorithm termed FastICA-Comb. Specifically, in order to achieve high-quality separation of VEMG, M-waves and initial spikes, the algorithm is designed as a unique scheme combining one-stage comb filtering and two-stage fast independent component analysis (FastICA) decomposition and classification. To evaluate the proposed algorithm's effectiveness, FES data collection experiment was conducted on 6 healthy subjects. The experimental results confirm that the FastICA-Comb algorithm has better VEMG and M-wave extraction capabilities than the classical methods including comb filter, GS-PEF, EMD-notch and blanking window in both simulated and real mixed FES signal. Therefore, the research findings provide an effective signal analysis tool for exploring the therapeutic mechanism of FES.