A Stimulus Artifact Removal Technique for SEMG Signal Processing During Functional Electrical Stimulation

IEEE Transactions on Bio-medical Engineering
Shuang QiuDong Ming

Abstract

The purpose of this study was to design a method for extracting the volitional EMG from recorded surface electromyography (EMG), contaminated by functional electrical stimulation (FES) artifact. Considering that the FES artifact emerges periodically with rather large amplitude in nonstationary EMG, we designed an adaptive-matched filter (AMF) via genetic algorithm (GA) optimization. Both the simulated and real data from seven subjects were processed, using the GA-AMF filter and comb filter, respectively. To test the filtering effect on the EMG, contaminated with FES artifact of different current intensities, the contaminated EMG was simulated by combining the simulation artifact and clean EMG with various FES artifacts to clean EMG ratios. The results show that, in simulation test, compared to the EMG filtered by comb filter, the simulated EMG (p < 0.05), filtered by using GA-AMF, had significantly higher correlation coefficient, higher signal to noise ratio, and lower normalized root mean square error, whereas the real EMG (p < 0.05), filtered by using GA-AMF had higher power reduction than that filtered by using comb filter. The results indicate that GA-AMF can effectively remove FES artifact from the EMG of the stimulated mu...Continue Reading

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Citations

Aug 8, 2018·Biomedical Engineering Online·Qingshu LiuXiaojing Ma
May 22, 2018·Frontiers in Neuroscience·Kevin JosephUlrich G Hofmann
Jan 17, 2019·International Journal of Neural Systems·Yurong LiYuan Yang
Dec 6, 2020·Scientific Reports·Bethel A C OsuagwuRebecca Shirley
Aug 24, 2021·Frontiers in Neuroscience·Juzhe LiHao Yu

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