CoChat
Sign Up
Cited by 39Muscle activation and electromyography studiesNeuroscience and Neural EngineeringStroke Rehabilitation and Recovery

A stimulus artifact removal technique for SEMG signal processing during functional electrical stimulation.

Qiu, Shuang, Feng, Jing, Xu, Rui, Xu, Jiapeng, Wang, Kun, He, Feng, Qi, Hongzhi, Zhao, Xin +3 more·2015·IEEE Transactions on Biomedical Engineering
DOI 10.1109/tbme.2015.2407834
Open this paper in CoChat — free account

Abstract

GOAL: 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. METHODS: 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. RESULTS: 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 muscle and its adjacent muscle, and the GA-AMF filter performed better than did the comb filter. CONCLUSION: All these results demonstrate that the GA-AMF filter is capable of extracting volitional EMG from the stimulated muscle and adjacent muscles. SIGNIFICANCE: GA-AMF could provide technical support for improving EMG feedback control of FES rehabilitation system.

Integrity signals

Registry

Crossref · 10.1109/tbme.2015.2407834

Cites

34 of 34 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The artifact waveform template can be obtained by least-mean-square algorithms, genetic algorithms, principal component analysis, and wavelet algorithms.

    “The template of the artifact waveform can be obtained by the least-mean-square algorithms, genetic algorithms (Qiu et al., 2015), principal component analysis (Deprez et al., 2017), and wavelet algorithms (Yochum and Binczak, 2015).”

    From Advances in Neural Recording and Stimulation Integrated Circuits (2021) · cites this paper as Qiu et al., 2015

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Qiu, S., Feng, J., Xu, R., Xu, J., Wang, K., He, F., Qi, H., Zhao, X., Zhou, P., Zhang, L., & Ming, D.
Affiliations
  • Tianjin University (CN)
  • Shriners Hospitals for Children (US)
ORCID
Venue
IEEE Transactions on Biomedical Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2015-08
Volume
62
Issue
8
Pages
1959-1968
ISSN
  • 0018-9294 (ISSN-L)
  • 1558-2531
DOI
10.1109/tbme.2015.2407834
OpenAlex
W2051029025
Cited by
39
References
34
Citations by year
2026: 4 · 2025: 3 · 2024: 4 · 2023: 5 · 2022: 1 · 2021: 8 · 2020: 4 · 2019: 4 · 2018: 4 · 2017: 1 · 2016: 1
FWCI
0.99
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
  • IEEE Transactions on Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Natural Science Foundation of China (81222021, 61172008, 81171423, 81127003)
  • National Key Technology R&D Program of the Ministry of Science and Technology of China (2012BAI34B02)
  • Program for New Century Excellent Talents in University of the Ministry of Education of China (NCET-10-0618)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
  • Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
Keywords
surface electromyography, artifact removal, adaptive matched filter, genetic algorithm optimization, comb filter
MeSH terms
Adult, Electric Stimulation, Electromyography, Female, Humans, Male, Signal Processing, Computer-Assisted, Artifacts, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review