Skip to main content
Springer Nature Link
Log in

Spatio-spectral filters for low-density surface electromyographic signal classification

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Al-Assaf Y (2006) Surface myoelectric signal analysis: dynamic approaches for change detection and classification. IEEE Trans Biomed Eng 53(11):2248–2256

    Article  PubMed  Google Scholar 

  2. Andreasen D, Gabbert D (2006) Electromyographic switch navigation of power wheelchairs. In: Annual conference of the rehabilitation engineering and assistive technology society of North America

  3. Arieta A, Katoh R, Yokoi H, Wenwei Y (2006) Development of a multi-DOF electromyography prosthetic system using the adaptive joint mechanism. Appl Bionics Biomech 3(2):101–112

    Article  Google Scholar 

  4. Barry D, Gordon K, Hinton G (1990) Acoustic and surface EMG diagnosis of pediatric muscle disease. Muscle Nerve 13(4): 286–290

    Article  PubMed  CAS  Google Scholar 

  5. Bhullar H, Loudon G, Fothergill J, Jones N (1990) Selective noninvasive electrode to study myoelectric signals. Med Biol Eng Comput 28(6):581–586

    Article  PubMed  CAS  Google Scholar 

  6. Day S (2002) Important factors in surface EMG measurement. Bortec Biomedical Ltd publishers, Calgary, pp 1–17

  7. De Luca C (2002) Surface electromyography: detection and recording. DelSys Incorporated, pp 1–10

  8. Disselhorst-Klug C, Silny J, Rau G (1997) Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters. IEEE Trans Biomed Eng 44(7):567–574

    Article  PubMed  CAS  Google Scholar 

  9. Du S, Vuskovic M (2004) Temporal vs. spectral approach to feature extraction from prehensile EMG signals. In: Information reuse and integration, 2004. IRI 2004. IEEE international conference on proceedings of the 2004 , pp 344–350. IEEE

  10. Duda R, Hart P, Stork D (2000) Pattern classification, 2nd edn, Wiley, New York

  11. Englehart K, Hudgins B (2003) A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans Biomed Eng 50(7):848–854

    Article  PubMed  Google Scholar 

  12. Farina D, Cescon C (2001) Concentric-ring electrode systems for noninvasive detection of single motor unit activity. IEEE Trans Biomed Eng 48(11):1326–1334

    Article  PubMed  CAS  Google Scholar 

  13. Farina D, Schulte E, Merletti R, Rau G, Disselhorst-Klug C (2003) Single motor unit analysis from spatially filtered surface electromyogram signals. Part I: spatial selectivity. Med Biol Eng Comput 41(3):330–337

    Article  PubMed  CAS  Google Scholar 

  14. Farquhar J, Hill J, Lal T, Schölkopf B (2006) Regularised CSP for sensor selection in BCI. In: 3rd international brain-computer interface workshop and training course, pp 14–15

  15. Freeman W, Holmes M, Burke B, Vanhatalo S (2003) Spatial spectra of scalp EEG and EMG from awake humans. Clin Neurophysiol 114(6):1053–1068

    Article  PubMed  Google Scholar 

  16. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press, London (1990)

  17. Graupe D, Cline W (1975) Functional separation of EMG signals via arma identification methods for prosthesis control purposes. IEEE Trans Syst Man Cybern 2:252–259

    Google Scholar 

  18. Hahne J, Graimann B, Muller K (2012) Spatial filtering for robust myoelectric control. IEEE Trans Biomed Eng 59(5):1436–1443

    Google Scholar 

  19. Huang H, Zhou P, Li G, Kuiken T (2008) An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface. IEEE Trans Neural Syst Rehabil Eng 16(1):37–45

    Article  PubMed  Google Scholar 

  20. Huang H, Zhou P, Li G, Kuiken T (2009) Spatial filtering improves EMG classification accuracy following targeted muscle reinnervation. Ann Biomed Eng 37(9):1849–1857

    Article  PubMed  Google Scholar 

  21. Hudgins B, Parker P, Scott R (1993) A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 40(1):82–94

    Article  PubMed  CAS  Google Scholar 

  22. Koles Z (1991) The quantitative extraction and topographic mapping of the abnormal components in the clinical EEG. Electroencephalogr Clin Neurophysiol Suppl 79(6):440–447

    Article  PubMed  CAS  Google Scholar 

  23. Lemm S, Blankertz B, Curio G, Muller K (2005) Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng 52(9):1541

    Article  PubMed  Google Scholar 

  24. Peerdeman B, Boere D, Witteveen H, Veld R, Hermens H, Stramigioli S, Rietman H, Veltink P, Misra S (2011) Myoelectric forearm prostheses: State of the art from a user-centered perspective. J Rehabil Res Dev 48(6):719

    Article  PubMed  Google Scholar 

  25. Proakis J, Manolakis D (1996) Digital signal processing. Macmillan Publishing Company, New York, vol 1(99), 2

  26. Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined handmovement. IEEE Trans Rehabil Eng 8(4):441–446

    Article  PubMed  CAS  Google Scholar 

  27. Reucher H, Rau G, Silny J (1987) Spatial filtering of noninvasive multielectrode EMG: Part I-introduction to measuring technique and applications. IEEE Trans Biomed Eng 2:98–105

    Article  Google Scholar 

  28. Reucher H, Silny J, Rau G (1987) Spatial filtering of noninvasive multielectrode EMG: Part II-filter performance in theory and modeling. IEEE Trans Biomed Eng 2: 106–113

    Article  Google Scholar 

  29. Wheeler K, Jorgensen C (2003) Gestures as input: Neuroelectric joysticks and keyboards. IEEE Pervasive Comput 2(2):56–61

    Article  Google Scholar 

  30. Yang Y, Chevallier S, Wiart J, Bloch I (2012) Automatic selection of the number of spatial filters for motor-imagery BCI. ESANN 2012. In: 20th European symposium on artificial neural networks, computational intelligence and machine learning, pp 109–114

  31. Zhou P, Lowery M, Englehart K, Huang H, Li G, Hargrove L, Dewald J, Kuiken T (2007) Decoding a new neural–machine interface for control of artificial limbs. J Neurophysiol 98(5): 2974–2982

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Basic Research Program (973 Program) of China (Grant No. 2011CB013305), the Science and Technology Commission of Shanghai Municipality (Grant No. 11JC1406000) and the State Key Laboratory of Mechanical System and Vibration (Grant No. MSVZD201204). Zhiguo Zhang is partially supported by the University of Hong Kong Seed Funding Programme for Basic Research (Grant No. 201203159009).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University, Shanghai, 200240, China

    Gan Huang, Dingguo Zhang & Xiangyang Zhu

  2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

    Zhiguo Zhang

Authors
  1. Gan Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhiguo Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Dingguo Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiangyang Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Gan Huang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Huang, G., Zhang, Z., Zhang, D. et al. Spatio-spectral filters for low-density surface electromyographic signal classification. Med Biol Eng Comput 51, 547–555 (2013). https://doi.org/10.1007/s11517-012-1024-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-012-1024-3

Keywords