Skip to main content
SpringerLink
Log in
Book cover

Wearable Sensors and Robots pp 395–410Cite as

Hand Exoskeleton Control for Cerebrum Plasticity Training Based on Brain–Computer Interface

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 399))

Abstract

Rehabilitation therapy with exoskeleton robots has been widely adopted to realize normal traction training of muscles, but the plasticity training of cerebrum is usually ignored during rehabilitation with exoskeletons. This paper presents a new exoskeleton aided hand rehabilitation method for post-stroke patient to validate the feasibility and reliability of cerebrum plasticity training . The approach is based on the Brain–Computer Interface (BCI) technology with which the EEG can be acquired and processed to obtain the patient’s hand motion intention by applying Independent Component Analysis (ICA) algorithm. The hand exoskeleton system is motivated and controlled by the motion intention to assist the hand movement. Experiments of hand exoskeleton motion control and force control based on BCI validated the feasibility and reliability of the system. Despite the 1.8–2.9 s time delay of response during experiment, the subject’s hand motion intention was well acquired by BCI and the corresponding hand motion was executed by hand exoskeleton.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Amari S, Gardoso JF (1997) Blind source separation-semiparametric statistical approach. IEEE Trans Signal Process 45(11):2692–2700

    Article  Google Scholar 

  • Bi Q, Yang C-j (2014) Human-machine interaction force control using model reference adaptive impedance control for index finger exoskeleton. J Zhejiang Univ Sci C 15(4):275–283

    Article  Google Scholar 

  • Carr JH, Shepherd RB (1987) A motor relearning program for stroke, 2nd edn. Aspen Publisher, New York, pp 151–154

    Google Scholar 

  • Comon P (1994) Independent component analysis, a new concept. Sig Process 36:287–314

    Article  MATH  Google Scholar 

  • Doyle JC, Lenz K, Packard A (1987) Design examples using µ-synthesis: space shuttle lateral axis FCS during reentry. NATO ASI Ser Modell Robustness Sensitivity Reduction Control Syst 34:127–154

    Article  MathSciNet  Google Scholar 

  • Frisoli A, Loconsole C, Leonardis D, Banno F et al (2012) A new gaze-BCI-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks. IEEE Trans Syst 42(6):1169–1180

    Google Scholar 

  • Georgopoulos AP, Langheim FJP, Leuthold AC, Merkle AN (2005) Magnetoencephalographic signals predict movement trajectory in space. Exp Brain Res 167(1):132–135

    Article  Google Scholar 

  • Gomez-Rodriguez M, Grosse-Wentrup M, Hill J, Gharabaghi A et al (2011) Towards brain-robot interfaces in stroke rehabilitation. In: IEEE international conference on rehabilitation robotics. Switzerland, 29 Jun–1 July, 2011, pp 1–6

    Google Scholar 

  • Hyva ̈rinen A (1999) Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans Neural Netw 10(3):626–634

    Article  Google Scholar 

  • Hyva ̈rinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9(7):1483–1492

    Article  Google Scholar 

  • Quandt F, Reichert C, Hinrichs H et al (2012) Single trial discrimination of individual finger movements on one hand: a combined MEG and EEG study. NeuroImage 59:3316–3324

    Article  Google Scholar 

  • Reinkensmeyer DJ, Kahn LE, Averbuch M et al (2000) Understanding and teaching arm movement impairment after chronic brain injury: progress with the arm guide. J Rehabil Res Dev 37(6):653–662

    Google Scholar 

  • Waldert S, Preissl H, Demandt E, Braun C et al (2008) Hand movement direction decoded from MEG and EEG. J Neurosci 28(4):1000–1008

    Article  Google Scholar 

  • Xiao R, Ding L (2013) Evaluation of EEG Features in decoding individual finger movements from one hand. Comput Math Methods Med 2013:1–10

    MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was supported in part by Science Fund for Creative Research Groups of National Natural Science Foundation of China (No.:51221004).

Author information

Authors and Affiliations

  1. The State Key Lab. of Fluid Power Transmission and Control, Zhejiang University, Hangzhou, 310027, People’s Republic of China

    Qian Bi, Canjun Yang, Wei Yang, Jinchang Fan & Hansong Wang

Authors
  1. Qian Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Canjun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinchang Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hansong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Canjun Yang .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang, China

    Canjun Yang

  2. University of Gävle, Gävle, Sweden

    G. S. Virk

  3. Zhejiang University, Department of Mechanical Engineering, Hangzhou, Zhejiang, China

    Huayong Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Zhejiang University Press and Springer Science+Business Media Singapore

About this paper

Cite this paper

Bi, Q., Yang, C., Yang, W., Fan, J., Wang, H. (2017). Hand Exoskeleton Control for Cerebrum Plasticity Training Based on Brain–Computer Interface. In: Yang, C., Virk, G., Yang, H. (eds) Wearable Sensors and Robots. Lecture Notes in Electrical Engineering, vol 399. Springer, Singapore. https://doi.org/10.1007/978-981-10-2404-7_31

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2404-7_31

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2403-0

  • Online ISBN: 978-981-10-2404-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation