Skip to main content

Advertisement

SpringerLink
Log in

Denoising of HD-sEMG signals using canonical correlation analysis

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

Abstract

High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.

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

Access this article

Log in via an institution

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
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Agante PM, de Sa J (1999) ECG noise filtering using wavelets with soft-thresholding methods. Comput Cardiol 1999:353–358

    Google Scholar 

  2. Al Harrach M, Boudaoud S, Gamet D, Grosset J, Marin F (2014) Evaluation of hd-semg probability density function deformations in ramp exercise. In: Engineering in medicine and biology society (EMBC), 2014 36th annual international conference of the IEEE, pp 2209–2212. IEEE

  3. Allouch S, Al Harrach M, Boudaoud S, Laforet J, Ayachi F, Younes R (2013) Muscle force estimation using data fusion from high-density semg grid. In: 2013 2nd International conference on advances in biomedical engineering (ICABME), pp 195–198. IEEE

  4. Aschero G, Gizdulich P (2010) Denoising of surface EMG with a modified wiener filtering approach. J Electromyogr Kinesiol 20:366–373

    Article  PubMed  Google Scholar 

  5. Ayachi F, Boudaoud S, Marque C (2014) Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study. Med Biol Eng Comput 52(8):673–684

    Article  CAS  PubMed  Google Scholar 

  6. Baratta R, Solomonow M, Zhou B-H, Zhu M (1998) Methods to reduce the variability of EMG power spectrum estimates. J Electromyogr Kinesiol 8(5):279–285

    Article  CAS  PubMed  Google Scholar 

  7. Clancy EA, Morin EL, Merletti R (2002) Sampling, noise-reduction and amplitude estimation issues in surface electromyography. J Electromyogr Kinesiol 12:1–16

    Article  CAS  PubMed  Google Scholar 

  8. De Clercq W, Vergult A, Vanrumste B, Van Paesschen W, Van Huffel S (2006) Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng 53:2583–2587

    Article  PubMed  Google Scholar 

  9. Euljoon P, Meek S (1995) Adaptive filtering of the electromyographic signal for prosthetic control and force estimation. IEEE Trans Biomed Eng 42:1048–1052

    Article  Google Scholar 

  10. Farina D, Merletti R (2001) A novel approach for precise simulation of the emg signal detected by surface electrodes. IEEE Trans Biomed Eng 48:637–646

    Article  CAS  PubMed  Google Scholar 

  11. Glaser V, Holobar A, Zazula D (2013) Real-time motor unit identification from high-density surface EMG. IEEE Trans Neural Syst Rehabil Eng 21:949–958

    Article  PubMed  Google Scholar 

  12. Hassan M, Boudaoud S, Terrien J, Karlsson B, Marque C (2011) Combination of canonical correlation analysis and empirical mode decomposition applied to denoising the labor electrohysterogram. IEEE Trans Biomed Eng 58:2441–2447

    Article  PubMed  Google Scholar 

  13. Hermens HJ, Freriks B, Merletti R, Stegeman D, Blok J, Rau G, Disselhorst-Klug C, Hägg G (1999) European recommendations for surface electromyography. Roessingh Res Dev 8(2):13–54

    Google Scholar 

  14. Kleine BU, van Dijk JP, Lapatki BG, Zwarts MJ, Stegeman DF (2007) Using two-dimensional spatial information in decomposition of surface EMG signals. J Electromyogr Kinesiol 17:535–548

    Article  PubMed  Google Scholar 

  15. De Luca CJ, Contessa P (2012) Hierarchical control of motor units in voluntary contractions. J Neurophysiol 107(1):178–195

    Article  PubMed  Google Scholar 

  16. Mello RGT, Oliveira LF, Nadal J (2006) Emg signal filtering based on empirical mode decomposition. Biomed Signal Process Control 1:44–55

    Article  Google Scholar 

  17. Mello RGT, Oliveira LF, Nadal J (2007) Digital butterworth filter for subtracting noise from low magnitude surface electromyogram. Comput Methods Prog Biomed 87:28–35

    Article  Google Scholar 

  18. Murphy SA, Berrios R, Nelson PA, Negro F, Farina D, Schmit B, Hyngstrom A (2015) Impaired regulation post-stroke of motor unit firing behavior during volitional relaxation of knee extensor torque assessed using high density surface emg decomposition. In: Engineering in medicine and biology society (EMBC), 2015 37th annual international conference of the IEEE, pp 4606–4609. IEEE

  19. Rojas-Martinez M, Mananas MA, Alonso JF (2012) High-density surface EMG maps from upper-arm and forearm muscles. J NeuroEng Rehabil 9:85

    Article  PubMed  PubMed Central  Google Scholar 

  20. Safieddine D, Kachenoura A, Albera L, Birot G, Wendling F, Senhadji L, Merlet I (2011) ICA vs CCA for the denoising of interictal epileptic signals: a study of performance based on source localization. IRBM 32(5):298–301

    Article  Google Scholar 

  21. Stegeman DF, K Bert U, L Bernd G, VD Johannes P (2012) High-density surface EMG: techniques and applications at a motor unit level. Biocybern Biomed Eng 32(3):3–27

    Article  Google Scholar 

  22. Sweeney K, McLoone S, Ward T (2013) The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Trans Biomed Eng 60:97–105

    Article  PubMed  Google Scholar 

  23. van Dijk JP, Blok JH, Lapatki BG, van Schaik IN, Zwarts MJ, Stegeman DF (2008) Motor unit number estimation using high-density surface electromyography. Clin Neurophysiol 119(1):33–42

    Article  PubMed  Google Scholar 

  24. Vergult A, De Clercq W, Palmini A, Vanrumste B, Dupont P, Van Huffel S, Van Paesschen W (2007) Improving the interpretation of ictal scalp EEG: BSS-CCA algorithm for muscle artifact removal. Epilepsia 48:950–958

    Article  PubMed  Google Scholar 

  25. Yavuz UŞ, Negro F, Sebik O, Holobar A, Frömmel C, Türker KS, Farina D (2015) Estimating reflex responses in large populations of motor units by decomposition of the high-density surface electromyogram. J Physiol 593(19):4305–4318

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Zhang X, Zhou P (2013) Filtering of surface EMG using ensemble empirical mode decomposition. Med Eng Phys 35:537–542

    Article  PubMed  Google Scholar 

  27. Zhou P, Lowery M, Dewald JA, Kuiken T (2005) Towards improved myoelectric prosthesis control: high density surface EMG recording after targeted muscle reinnervation. IEEE Eng Med Biol Soc 4:4064–4067

    Google Scholar 

  28. Zwarts MJ, Stegeman DF (2003) Multichannel surface EMG: basic aspects and clinical utility. Muscle Nerve 28:1–17

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was carried out and funded in the framework of the Labex MS2T. It was supported by the French Government, through the program “Investments for the future” managed by the National Agency for Research (Reference ANR-11-IDEX-0004-02).

Author information

Authors and Affiliations

  1. CNRS UMR 7338, Sorbonne Universités, Université de Technologie de Compiègne, Compiègne, France

    M. Al Harrach, S. Boudaoud, D. Gamet, J. F. Grosset & F. Marin

  2. Rennes 1 University, 35000, Rennes, France

    M. Hassan

  3. Institut universitaire de gériatrie de Montréal, Montreal, Canada

    F. S. Ayachi

Authors
  1. M. Al Harrach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Boudaoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. S. Ayachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D. Gamet
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J. F. Grosset
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. F. Marin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Boudaoud.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Harrach, M., Boudaoud, S., Hassan, M. et al. Denoising of HD-sEMG signals using canonical correlation analysis. Med Biol Eng Comput 55, 375–388 (2017). https://doi.org/10.1007/s11517-016-1521-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-016-1521-x

Keywords

Search

Navigation