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A Hybrid Non-invasive Method for the Classification of Amputee’s Hand and Wrist Movements

  • Oluwarotimi Williams Samuel
  • Xiangxin Li
  • Xu Zhang
  • Hui Wang
  • Guanglin LiEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 64)

Abstract

The quest to develop dexterous artificial arm which supports multiple degrees of freedom for amputees has attracted a lot of study interest in the last few decades. The outcome of some of the studies had identified surface Electromyography (EMG) as the most commonly used biological signal in predicting the motion intention of an amputee. Different EMG based control methods for multifunctional prosthesis have been proposed and investigated in a number of previous studies. However, no any multifunctional prostheses are clinically available yet. One of the possible reasons would be that the residual muscles after amputations might not produce sufficient EMG signals for movement classifications. In this study, we proposed to use electroencephalography (EEG) signals recorded from the scalp of an amputee as additional signals for motion identifications. The performance of a hybrid scheme based on the combination of EMG and EEG signals in identifying different hand and wrist movements was evaluated in one transhumeral amputee. Our pilot results showed that the proposed hybrid method increased the classification accuracy in identifying different hand and wrist movements of the amputee. This suggests that the proposed method may have potential to improve the control of multifunctional prostheses.

Notes

Acknowledgements

The authors would like to thank members of the Key Laboratory of Human-Machine Intelligence-Synergy Systems, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, for their assistance in the data acquisition. Lastly, I (O. W. Samuel) sincerely appreciate the support of CAS-TWAS President’s Fellowship to pursue a Ph.D. degree at the University of Chinese Academy of Sciences, Beijing, China. The Research work was supported in part by the National Key Basic Research Program of China (#2013CB329505), the National Natural Science Foundation of China under Grants (#61135004, #61203209), and Shenzhen Governmental Basic Research Grant (#JCYJ20130402113127532).

References

  1. 1.
    T.A. Kuiken, G. Li, B.A. Lock, et al, “Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms,” The Journal of the American Medical Association, vol. 301, no. 6, pp. 619–628, 2009.CrossRefGoogle Scholar
  2. 2.
    G. Li, and T.A. Kuiken, “EMG Pattern Recognition Control of Multifunctional Protheses by Transradial Amputees,” 31st Annual International Conference of the IEEE EMBS, pp. 6914–6917, 2009.Google Scholar
  3. 3.
    U. Sahin and F. Sahin, “Pattern Recognition with surface EMG Signal based on Wavelet Transformation,” IEEE Int. Conf. on Systems, Man, and Cybernetics, Oct. 14–17, 2012, COEX, Seoul, Korea, pp. 295–300.Google Scholar
  4. 4.
    M. Asghari, O.H. Hu, “Myoelectric control systems: A survey”, Biomedical Signal Processing and Control, pp. 275–294, 2007.Google Scholar
  5. 5.
    G. Li, A.E. Schultz, and T.A. Kuiken, “Quantifying Pattern Recognition-Based Myoelectric Control of Multifunctional Transradial Prostheses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 2, pp. 185–192, 2010.CrossRefGoogle Scholar
  6. 6.
    Y.U. Huang, K. Englehart, and B. Hudgins, “A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 11, pp. 1801–1811, 2005.CrossRefGoogle Scholar
  7. 7.
    D.L. Thilina, T. Kenbu, H. Yoshiaki, and K. Kazuo, “Towards Hybrid EEG-EMG-based control approaches to be used in bio-robotics applications: current status, challenges, and future directions,” Paladyn Journal of Behavioral Robotics, vol. 4(2), pp. 147–154.Google Scholar
  8. 8.
    J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, and T.M. Vaughan, “Brain Computer Interfaces for Communication and Control,” Clinical Neurophysiology 113 (2002), pp. 767–791.CrossRefGoogle Scholar
  9. 9.
    D.J. McFarland, L.M. McCane, and J.R. Wolpaw, “EEG-based communication: short-term role of feedback,” IEEE Transactions on Rehabilitation Engineering, vol. 6, pp. 7–11, 1998.CrossRefGoogle Scholar
  10. 10.
    N. Birbaumer, “Breaking the silence: Brain—computer interfaces (BCI) for communication and motor control,” Psychophysiology, 43 (2006), pp. 517–532.CrossRefGoogle Scholar
  11. 11.
    R.W. Jonathan, B. Niels, J.M. Dennis, P. Gert, M.V. Theresa, “Brain-computer interfaces for communication and control,” Clinical Neurophysiology, 113(2002), 767–791.CrossRefGoogle Scholar
  12. 12.
    K. Kiguchi and Y. Hayashi, “Motion Estimation based on EMG and EEG Signals to Control Wearable Robots,” IEEE International Conference on Systems, Man, and Cybernetics, pp. 4214–4218, 2013.Google Scholar
  13. 13.
    H. Shibasaki and J.C. Rothwell, “EMG-EEG Correlation,” Recommendations for the Practice of Clinical Neurophysiology: Guidelines of the International Federation of Clinical Physiology (EEG Suppl. 52), pp. 269–274, 1999.Google Scholar
  14. 14.
    A. Ferreira1, W.C. Celeste1, F.A. Cheein, T.F. Bastos-Filho, M. Sarcinelli-Filho, and R. Carelli, “Human-machine interfaces based on EMG and EEG applied to robotic systems,” Journal of NeuroEngineering and Rehabilitation 2008, 5:10.CrossRefGoogle Scholar
  15. 15.
    V.V. Ramalingam, S. Mohan, V. Sugumaran, “A Comparison of EMG and EEG signals for prostheses control using decision tree,” International Journal of Research in Computer Applications & Information Technology, vol. 1, no. 1, pp. 01–08, 2013.Google Scholar
  16. 16.
    G. Li, Y. Li, L. Yu, and Y. Geng, “Conditioning and sampling issues of EMG signals in motion recognition of multifunctional myoelectric prostheses,” Annals of Biomedical Engineering., vol. 39, no. 6, pp. 1779–1787, 2011.CrossRefGoogle Scholar
  17. 17.
    L.J. Hargrove, G. Li, K.B. Englehart, and B.S. Hudgins, “Principal Components Analysis Preprocessing for Improved Classification Accuracies in Pattern-Recognition-Based Myoelectric Control,” IEEE Trans. on Biomedical Engineering, vol. 56, no. 5, pp. 1407–1414, 2009.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Oluwarotimi Williams Samuel
    • 1
    • 2
  • Xiangxin Li
    • 1
    • 2
  • Xu Zhang
    • 3
  • Hui Wang
    • 2
  • Guanglin Li
    • 1
    Email author
  1. 1.Key Laboratory of Human-Machine Intelligence-Synergy SystemsChinese Academy of Sciences, Shenzhen Institutes of Advanced TechnologyShenzhenChina
  2. 2.Shenzhen College of Advanced TechnologyUniversity of Chinese Academy of SciencesShenzhenChina
  3. 3.Department of BiologySouth University of Science and TechnologyShenzhenChina

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