Skip to main content

Detection of Attempted Stroke Hand Motions from Surface EMG

  • 669 Accesses

Part of the Biosystems & Biorobotics book series (BIOSYSROB,volume 28)

Abstract

Brain-Computer Interfaces have been proposed for stroke rehabilitation, but a potential problem with this technology is the dependence of high-quality brain signals. The aim of this study was to investigate if attempted hand open motions can be detected from the muscle activity instead. Ten stroke patients performed 63 ± 7 attempted movements while three channels of EMG were recorded. Hudgins time-domain features and linear discriminant analysis were used, and 92 ± 3% of the movement activity was correctly classified. The Spearman correlation between the upper limb Fugl-Meyer score and the classification accuracies was 0.58 (P = 0.08). In conclusion, attempted movements from stroke patients can be detected using EMG.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-70316-5_8
  • Chapter length: 6 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   349.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-70316-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   449.99
Price excludes VAT (USA)
Fig. 1
Fig. 2

References

  1. M.A. Cervera et al., Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann. Clin. Transl. Neurol. 5(5), 651–663 (2018)

    Google Scholar 

  2. M. Jochumsen et al., Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG. J. Neural Eng. 12(5), 056013 (2005)

    CrossRef  Google Scholar 

  3. M. Jochumsen et al., Evaluation of EEG headset mounting for brain-computer interface-based stroke rehabilitation by patients, therapists, and relatives. Front. Hum. Neurosci. 14, 13 (2020)

    CrossRef  Google Scholar 

  4. M. Jochumsen et al., EMG-versus EEG-triggered electrical stimulation for inducing corticospinal plasticity. IEEE TNSRE 27(9), 1901–1908 (2019)

    Google Scholar 

  5. S.W. Lee et al., Subject-specific myoelectric pattern classification of functional hand movements for stroke survivors. IEEE TNSRE 19(5), 558–566 (2010)

    Google Scholar 

  6. A. Ramos-Murguialdayet et al., Decoding upper limb residual muscle activity in severe chronic stroke. Ann. Clin. Transl. Neurol. 2(1), 1–11 (2015)

    CrossRef  Google Scholar 

  7. S. Balasubramanian et al., Is EMG a viable alternative to BCI for detecting movement intention in severe stroke? IEEE Trans. Biomed. Eng. 65(12), 2790–2797 (2018)

    CrossRef  Google Scholar 

  8. X. Zhang, P. Zhou, High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans. Biomed. Eng. 59(6), 1649–1657 (2012)

    CrossRef  Google Scholar 

Download references

Acknowledgements

This work was funded by VELUX FONDEN (project no. 22357).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mads Jochumsen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Jochumsen, M., Waris, A., Niazi, I.K. (2022). Detection of Attempted Stroke Hand Motions from Surface EMG. In: Torricelli, D., Akay, M., Pons, J.L. (eds) Converging Clinical and Engineering Research on Neurorehabilitation IV. ICNR 2020. Biosystems & Biorobotics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-030-70316-5_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-70316-5_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70315-8

  • Online ISBN: 978-3-030-70316-5

  • eBook Packages: EngineeringEngineering (R0)