Skip to main content
SpringerLink
Log in

Rehabilitation potential of post-stroke patients training for kinesthetic movement imagination: Motor and cognitive aspects

  • Published:
Human Physiology Aims and scope Submit manuscript

Abstract

The rehabilitation potential of post-stroke patients was evaluated after a rehabilitation procedure using a hand exoskeleton controlled via a brain–computer interface (BCI). Examples are given for parameters describing the motor and cognitive functions and the capacity for kinesthetic movement imagination. It is emphasized that instrumental quantitative methods are important to use for adequate assessment of both the rehabilitation potential and the effectiveness of the BCI + exoskeleton procedure.

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

Similar content being viewed by others

References

  1. Nudo, R.J., Milliken, G.W., Jenkins, W.M., and Merzenich, M.M., Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys, J. Neurosci., 1996, vol. 16, no. 2, p. 785.

    CAS  PubMed  Google Scholar 

  2. Taub, E., Uswatte, G., and Elbert, T., New treatments in neurorehabilitation founded on basic research, Nat. Rev. Neurosci., 2002, vol. 3, no. 3, p. 228.

    Article  CAS  PubMed  Google Scholar 

  3. Bach-Y-Rita, P., Theoretical and practical considerations in the restoration of function after stroke, Top Stroke Rehabil., 2001, vol. 8, no. 3, p. 1.

    Article  CAS  PubMed  Google Scholar 

  4. Frolov, A.A., Biryukova, E.V., Bobrov, P.D., et al., Principles of neurorehabilitation based on the braincomputer interface and biologically adequate control of the exoskeleton, Hum. Physiol., 2013, vol. 39, no. 2, p. 196.

    Article  Google Scholar 

  5. Frolov, A.A., Husek, D., Biryukova, E.V., et al., Principles of motor recovery in post-stroke patients using hand exoskeleton controlled by the brain-computer interface based on motor imagery, Neural Network World, 2017, vol. 27, no. 1, p. 107.

    Article  Google Scholar 

  6. Pfurtscheller, G., EEG event-related desynchronization (ERD) and event related synchronization (ERS), in Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Niedermeyer, E. and Lopes da Silva, F.H., Eds., Baltimore, MD: Williams and Wilkins, 1999, 4th ed.

    Google Scholar 

  7. Pfurtscheller, G. and Lopes da Silva, F.H., Eventrelated EEG/MEG synchronization and desynchronization: basic principles, Clin. Neurophysiol., 1999, vol. 110, p. 1842.

    Article  CAS  PubMed  Google Scholar 

  8. Jeannerod, M., Neural simulation of action: a unifying mechanism for motor cognition, NeuroImage, 2001, vol. 14, p. 103.

    Article  Google Scholar 

  9. Jeannerod, M. and Frak, V., Mental imaging of motor activity in humans, Curr. Opin. Neurobiol., 1999, vol. 9, p. 735.

    Article  CAS  PubMed  Google Scholar 

  10. Neuper, C., Scherer, R., Reiner, M., and Pfurtscheller, G., Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG, Cognit. Brain Res., 2005, vol. 25, no. 3, p. 668.

    Article  Google Scholar 

  11. Bloom, F.E., Lazerson, A., and Hofstadter, L., Brain, Mind, and Behavior, New York: W.H. Freeman, 1984.

    Google Scholar 

  12. Bobrov, P.D., Korshakov, A.V., Roshchin, V.Yu., and Frolov, A.A., Bayesian approach to the implementation of the brain-computer interface based on the imaged movements, Zh. Vyssh. Nervn. Deyat. im. I.P. Pavlova, 2012, vol. 62, no. 1, p. 89.

    CAS  Google Scholar 

  13. Ferris, D.P., The exoskeletons are here, J. NeuroEng. Rehabil., 2009, vol. 6, no. 17. http://www.jneuroengrehab. com/content/6/1/17.

    Google Scholar 

  14. Buch, E., Weber, C., Cohen, L.G., et al., Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke, Stroke, 2008, vol. 39, p. 910.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ang, K.K., Chua, K.S., Phua, K.S., et al., A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke, Clin. EEG Neurosci., 2015, vol. 46, no. 4, p. 310.

    Article  PubMed  Google Scholar 

  16. Ang, K.K., Guan, C., Phua, K.S., et al., Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke, Front. Neuroeng., 2014, vol. 7, p. 30.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Ramos-Murguialday, A., Broetz, D., Rea, M., et al., Brain–machine interface in chronic stroke rehabilitation: a controlled study, Ann. Neurol., 2013, vol. 74, no. 1, p. 100.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ono, T., Shindo, K., Kawashima, K., et al., Braincomputer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke, Front. Neuroeng., 2014, vol. 7, p. 19.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Frolov, A.A., Mokienko, O.A., Lyukmanov, R.Kh., Chernikova, L.A., Kotov, S.V., Turbina, L.G., Bobrov, P.D., Biryukova, E.V., Kondur, A.A., Ivanova, G.E., Staritsyn, A.N., Bushkova, Yu.V., Dzhalagoniya, I.Z., Kurganskaya, M.E., Pavlova, O.G., et al., Preliminary results of a controlled study of BCI–exoskeleton technology efficacy in patients with poststroke arm paresis, Bull. Russ. State Med. Univ., 2016, no. 2, p. 16.

    Article  Google Scholar 

  20. Kotov, S.V., Turbina, L.G., Bobrov, P.D., et al., Use of BCI–exoskeleton technology and movement imagination technique for post-stroke rehabilitation, Al’m. Klin. Med., 2015, no. 39, p. 15.

    Google Scholar 

  21. Kotov, S.V., Turbina, L.G., Bobrov, P.D., Frolov, A.A., Pavlova, O.G., Kurganskaya, M.E., and Biryukova, E.V., Rehabilitation of stroke patients with a bioengineered “brain–computer interface with exoskeleton” system, Neurosci. Behav. Physiol., 2016, vol. 46, no. 5, p. 518.

    Article  Google Scholar 

  22. Mokienko, O.A., Lyukmanov, R.Kh., Chernikova, L.A., Suponeva, N.A., Piradov, M.A., and Frolov, A.A., Brain–computer interface: The first experience of clinical use in Russia, Hum. Physiol., 2016, vol. 42, no. 1, p. 24.

    Article  Google Scholar 

  23. Fugl-Meyer, A.R., Jääskö, L., Leyman, I., et al., The poststroke hemiplegic patient. A method for evaluation of physical performance, Scand. J. Rehabil. Med., 1975, vol. 7, p. 13.

    CAS  PubMed  Google Scholar 

  24. Gladstone, D.J., Daniells, C.J., and Black, S.E., The Fugl-Meyer assessment of motor recovery after stroke: A critical review of its measurement properties, Neurorehab. Neural Repair., 2002, vol. 16, p. 232.

    Article  Google Scholar 

  25. Bernshtein, N.A., O postroenii dvizhenii (The Design of Motions), Moscow: Meditsina, 1947.

    Google Scholar 

  26. Kozlovskaya, I.B., Afferentnyi kontrol’ proizvol’nykh dvizhenii (Afferent Control of Simultaneous Movements), Moscow: Nauka, 1976.

    Google Scholar 

  27. Smith, R., Kinaesthesia and touching reality, Interdiscip. Stud. Long Nineteenth Century, 2014, vol. 19.

  28. Bobrov, P., Frolov, A., Cantor, C., et al., Brain–computer interface based on generation of visual images, PLoS One, 2011, vol. 6, p. 20674. doi 10.1371/journal. pone.0020674

    Article  Google Scholar 

  29. Frolov, A., Husek, D., and Bobrov, P., Comparison of four classification methods for brain computer interface, Neural Network World, 2011, vol. 21, p. 101.

    Article  Google Scholar 

  30. Kohavi, R. and Provost, F., Glossary of terms. Special issue of applications of machine learning and the knowledge discovery process, Mach. Learn., 1998, vol. 30, nos. 2–3, p. 271.

    Google Scholar 

  31. Nasreddine, Z.S., Phillips, N.A., Bédirian, V., et al., The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment, J. Am. Geriatr. Soc., 2005, vol. 53, no. 4, p. 695.

    PubMed  Google Scholar 

  32. Schulte’s tables, in Al’manakh psikhologicheskikh testov (Almanac of Psychological Tests), Moscow, 1995.

  33. Biryukova, E.V., Roby-Brami, A., Frolov, A.A., and Mokhtari, M., Kinematics of human arm reconstructed from spatial tracking system recordings, J. Biomech., 2000, vol. 33, no. 8, p. 985.

    Article  CAS  PubMed  Google Scholar 

  34. Biryukova, E.V., Bril, B., Frolov, A.A., and Koulikov, M.A., Movement kinematics characterise the level of motor skill: the case of stone knapping in India, Motor Control, 2015, vol. 19, no. 1, p. 34.

    Article  PubMed  Google Scholar 

  35. Teo, W.P. and Chew, E., Is motor imagery brain–computer interface feasible in stroke rehabilitation? Phys. Med. Rehabil., 2014, vol. 6, p. 723.

    Google Scholar 

  36. Biryukova, E.V., Pavlova, O.G., Kurganskaya, M.E., Bobrov, P.D., Turbina, L.G., Frolov, A.A., Davydov, V.I., Silchenko, A.V., and Mokienko, O.A., Recovery of the motor function of the arm with the aid of a hand exoskeleton controlled by a brain–computer interface in a patient with an extensive brain lesion, Hum. Physiol., 2016, vol. 42, no. 1, p. 13.

    Article  Google Scholar 

  37. Alt Murphy, M.A. and Häger, C.K., Kinematic analysis of the upper extremity after stroke—how far have we reached and what have we grasped? Phys. Ther. Rev., 2015, vol. 20, p. 137.

    Article  Google Scholar 

  38. Zakharov, V.V., Neuropsychological tests: necessity and possibility of application, Consilium Med., 2011, no. 2, p. 98.

    Google Scholar 

  39. Kondur, A.A., Biryukova, E.V., Kotov, S.V., et al., Kinematic portrait of the patient as an objective indicator of the state of motor function during neurorehabilitation using an hand exoskeleton controlled by the brain-computer interface, Uch. Zap. S.-Peterb. Med. Univ. I.P. Pavlova, 2016, vol. 23, no. 3, p. 28.

    Google Scholar 

  40. Zeiler, S.R. and Krakauer, J.W., The interaction between training and plasticity in the post-stroke brain, Curr. Opin. Neurol., 2013, vol. 26, no. 6, p. 609.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Zackowski, K.M., Dromerick, A.W., Sahrmann, S.A., et al., How do strength, sensation, spasticity and joint individuation relate to the reaching deficits of people with chronic hemiparesis? Brain, 2004, vol. 127, p. 1035.

    Article  CAS  PubMed  Google Scholar 

  42. Nijboer, F., Birbaumer, N., and Kübler, A., The influence of psychological state and motivation on braincomputer interface performance in patients with amyotrophic lateral sclerosis—a longitudinal study, Front. Neurosci., 2010, vol. 4, no. 55, p. 1.

    Google Scholar 

  43. Kleih, S.C., Nijboer, F., Halder, S., and Kübler, A., Motivation modulates the P300 amplitude during brain-computer interface use, Clin. Neurophysiol., 2010, vol. 121, p. 1023.

    Article  CAS  PubMed  Google Scholar 

  44. Sprague, S.A., Ryan, D.B., and Sellers, E.W., The effects of motivation on task performance using a brain–computer interface, Proc. 6th Int. Brain–Computer Interface Conf., Graz: Graz Univ. Technol., 2014, art. ID 101-1.

    Google Scholar 

  45. Frolov, A.A., Aziatskaya, G.A., Bobrov, P.D., Luykmanov, R.Kh., Fedotova, I.R., Húsek, D., and Snašel, V., Electrophysiological activity of the brain during control of a motor imagery based on brain–computer interface, Hum. Physiol., 2017, vol. 43, no. 5, p. 501.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vladimirskii Moscow Regional Clinical Research Institute, Moscow, Russia

    S. V. Kotov, L. G. Turbina, A. A. Kondur & E. V. Zaitseva

  2. Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia

    E. V. Biryukova, A. A. Frolov & P. D. Bobrov

  3. Pirogov Russian National Medical Research University, Moscow, Russia

    E. V. Biryukova, A. A. Frolov & P. D. Bobrov

Authors
  1. S. V. Kotov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. G. Turbina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. V. Biryukova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. A. Frolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. A. Kondur
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. E. V. Zaitseva
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P. D. Bobrov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. V. Biryukova.

Additional information

Original Russian Text © S.V. Kotov, L.G. Turbina, E.V. Biryukova, A.A. Frolov, A.A. Kondur, E.V. Zaitseva, P.D. Bobrov, 2017, published in Fiziologiya Cheloveka, 2017, Vol. 43, No. 5, pp. 52–62.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kotov, S.V., Turbina, L.G., Biryukova, E.V. et al. Rehabilitation potential of post-stroke patients training for kinesthetic movement imagination: Motor and cognitive aspects. Hum Physiol 43, 532–541 (2017). https://doi.org/10.1134/S0362119717050097

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0362119717050097

Search

Navigation