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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

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Abstract

The dynamics of motor function recovery in a patient with an extensive brain lesion has been investigated during a course of neurorehabilitation assisted by a hand exoskeleton controlled by a brain–computer interface. Biomechanical analysis of the movements of the paretic arm recorded during the rehabilitation course was used for an unbiased assessment of motor function. Fifteen procedures involving hand exoskeleton control (one procedure per week) yielded the following results: (a) the velocity profile for targeted movements of the paretic hand became nearly bell-shaped; (b) the patient began to extend and abduct the hand, which was flexed and adducted at the beginning of the course; and (c) the patient started supinating the forearm, which was pronated at the beginning of the rehabilitation course. The first result is interpreted as improvement of the general level of control over the paretic hand, and the two other results are interpreted as a decrease in spasticity of the paretic hand.

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References

  1. Kotov, S.V., Stakhovskaya, L.V., Isakova, E.V., et al., in Insul’t (Stroke), Stakhovskaya, L.V. Kotov, S.V., Eds., Moscow: Med. Inf. Agentstvo, 2014.

  2. Kübler, A. and Birbaumer, N., Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients?, Clin. Neurophysiol., 2008, vol. 119, no. 11, p. 2658.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Millán, J.d.R., Rupp, R., Müller-Putz, G.R., et al., Combining brain-computer interfaces and assistive technologies: State-of the-art and challenges, Front. Neurosci., 2010, no. 4, p. 161.

    PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. 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 

  6. 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 

  7. Butler, A.J. and Page, S.J., Mental practice with motor imagery: Evidence for motor recovery and cortical reorganization after stroke, Arch. Phys. Med. Rehabil., 2006, vol. 87, no. 12, suppl. 2, p. S2.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Sharma, N., Pomeroy, V.M., and Baron, J.C., Motor imagery: A backdoor to the motor system after stroke?, Stroke, 2006, vol. 37, no. 7, p. 1941.

    Article  PubMed  Google Scholar 

  9. Mokienko, O.A., Brain-computer interface based on motor imagery during rehabilitation of patients with focal brain lesions, Cand. Sci. (Med.) Dissertation, Moscow, 2013.

    Google Scholar 

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

    Article  Google Scholar 

  11. Machado, S., Araújo, F., Paes, F., et al., EEG-base brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation, Rev. Neurosci., 2010, vol. 21, no. 6, p. 451.

    Article  PubMed  Google Scholar 

  12. Lotze, M., Braun, C., Birbaumer, N., et al., Motor learning elicited by voluntary drive, Brain, 2003, vol. 126, pt. 4, p. 866.

    Article  PubMed  Google Scholar 

  13. Jackson, P.L., Doyon, J., Richards, C.L., and Malouin, F., The efficacy of combined physical and mental practice in the learning of a foot-sequence task after stroke: A case report, Neurorehabil. Neural Repair, 2004, vol. 18, no. 2, p. 106.

    Article  PubMed  Google Scholar 

  14. Ang, K.K., Guan, C., Chua, K.S., et al., A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface, Clin. EEG Neurosci., 2011, vol. 42, no. 4, p. 253.

    Article  PubMed  Google Scholar 

  15. Ang, K.K., Guan, C., Phua, K.S., et al., Brain-computer interface-based robotic and effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke, Front. Neuroeng., 2014, art. 30. doi 10.3389/fneng.2014.00030.

  16. 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, no. 3, p. 910.

    Article  PubMed  Google Scholar 

  17. Kotov, S.V., Turbina, L.G., Bobrov, P.D., et al., Rehabilitation of post stroke patients using a bioengineering system “brain-computer interface + exoskeleton”, Zh. Nevrol. Psikhiatr. im. S.S. Korsakova, 2014, vol. 12, pp. 66.

    Google Scholar 

  18. Teo, W.P. and Chew, E., Is motor imagery brain-computer interface feasible in stroke rehabilitation?, Phys. Med. Rehabil., 2014, vol. 6, no. 8, p. 723.

    Google Scholar 

  19. Fugl-Meyer, A.R., Jääko, L., Leyman, I., et al., The post stroke hemiplegic patient. A method for evaluation of physical performance, Scand. J. Rehabil. Med., 1975, vol. 7, no. 1, p. 13.

    CAS  PubMed  Google Scholar 

  20. Scott, S.H. and Dukelow, S.P., Potential of robots as next-generation technology for clinical assessment of neurological disorders and upper-limb therapy, J. Rehabil. Res. Dev., 2011, vol. 48, no. 4, p. 335.

    Article  PubMed  Google Scholar 

  21. Levin, M., Interjoint coordination during pointing movements is disrupted in spastic hemiparesis, Brain, 1996, vol. 119, pt. 1, p. 281.

    Article  PubMed  Google Scholar 

  22. Krebs, H.I., Hogan, N., Aisen, M.L., and Volpe, B.T., Robot-aided neurorehabilitation, IEEE Trans. Rehabil. Eng., 1998, vol. 6, no. 1, p. 75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Beer, R.F., Dewald, J.P.A., and Rymer, W.Z., Deficits in the coordination of multijoint arm movements in patients with hemiparesis: evidence for disturbed control of limb dynamics, Exp. Brain Res., 2000, vol. 131, no. 3, p. 305.

    Article  CAS  PubMed  Google Scholar 

  24. Cirstea, M.C. and Levin, M.F., Compensatory strategies for reaching in stroke, Brain, 2000, vol. 123, no. 5, p. 940.

    Article  PubMed  Google Scholar 

  25. Cirstea, M.C., Mitnitski, A.B., Feldman, A.G., and Levin, M.F., Interjoint coordination dynamics during reaching in stroke, Exp. Brain Res., 2003, vol. 151, no. 3, p. 289.

    Article  CAS  PubMed  Google Scholar 

  26. Rohrer, B., Fasoli, S., Krebs, H.I., et al., Submovements grow larger, fewer, and more blended during stroke recovery, Mot. Control, 2004, vol. 8, pp. 472.

    Google Scholar 

  27. Chang, J.-J., Wu, T.-I., Wu, W.-L., and Su, F.-C., Kinematical measure for spastic reaching in children with cerebral palsy, Clin. Biomech., 2005, vol. 20, no. 4, p. 381.

    Article  Google Scholar 

  28. Finley, M.A., Fasoli, S.E., Dipietro, L., et al., Short duration upper extremity robotic therapy in stroke patients with severe upper extremity motor impairment, J. Rehabil. Res. Dev., 2005, vol. 42, no. 5, p. 683.

    Article  PubMed  Google Scholar 

  29. Micera, S., Carpaneto, J., Posteraro, F., et al., Characterization of upper arm synergies during reaching tasks in able-bodied and hemiparetic subjects, Clin. Biomech., 2005, vol. 20, no. 9, p. 939.

    Article  CAS  Google Scholar 

  30. Hogan, N. and Flash, T., Moving gracefully: quantitative theories of motor coordination, Trends Neurosci., 1987, vol. 10, no. 4, p. 170.

    Article  Google Scholar 

  31. Frolov, A.A., Dufosse, M., Rizek, S., and Kaladjan, A., On the possibility of linear modeling of the human arm neuromuscular apparatus, Biol. Cybern., 2000, vol. 82, no. 6, p. 499.

    Article  CAS  PubMed  Google Scholar 

  32. Frolov, A.A., Prokopenko, R.A., Dufosse, M., and Ouezdou, F.B., Adjustment of the human arm viscoelastic properties to the direction of reaching, Biol. Cybern., 2006, vol. 94, pp. 97.

    Article  CAS  PubMed  Google Scholar 

  33. Shelton, F.N.A.P. and Reding, M.J., Effect of lesion location on upper limb motor recovery after stroke, Stroke, 2001, vol. 32, no. 1, p. 107.

    Article  CAS  PubMed  Google Scholar 

  34. Mercier, C. and Bourbonnais, D., Relative shoulder flexor and handgrip strength is related to upper limb function after stroke, Clin. Rehabil., 2004, vol. 18, no. 2, p. 215.

    Article  PubMed  Google Scholar 

  35. Paolucci, S., Bragoni, M., Coiro, P., et al., Quantification of the probability of reaching mobility independence at discharge from a rehabilitation hospital in nonwalking early ischemic stroke patients: a multivariate study, Cerebrovasc. Dis., 2008, vol. 26, no. 1, p. 16.

    Article  PubMed  Google Scholar 

  36. Bobrov, P.D., Gusek, D., Korshakov, A.V., and Frolov, A.A., Sources of brain activity that either contribute or not to EEG pattern classification corresponding to motor imagery, Neirokomp. Razrab. Primen., 2011, vol. 12, pp. 1.

    Google Scholar 

  37. Bobrov, P.D., Korshakov, A.V., Roshchin, V.Yu., and Frolov, A.A., Bayesian classifier for brain-computer interface based on mental representation of movements, Zh. Vyssh. Nervn. Deyat. im. I.P. Pavlova, 2012, vol. 62, no. 1, p. 89.

    CAS  Google Scholar 

  38. Bobrov, P., Frolov, A., Cantor, C., et al., Brain-computer interface based on generation of visual images, PLoS One, 2011, vol. 6, no. 6, e20674. doi 10.1371/journal.pone.0020674.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Frolov, A., Húsek, D., and Bobrov, P., Comparison of four classification methods for brain computer interface, Neural Network World, 2011, vol. 21, no. 2, p. 101.

    Article  Google Scholar 

  40. 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 

  41. Prokopenko, R.A., Frolov, A.A., Biryukova, E.V., and Roby-Brami, A., Assessment of the accuracy of a human arm model with seven degrees of freedom, J. Biomech., 2001, vol. 34, no. 2, p. 177.

    Article  CAS  PubMed  Google Scholar 

  42. Jaspers, E., Desloovere, K., Bruyninckx, H., et al., Review of quantitative measurements of upper limb movements in hemiplegic cerebral palsy, Gait Posture, 2009, vol. 30, no. 4, p. 395.

    Article  PubMed  Google Scholar 

  43. Michaelsen, S.M., Jacobs, S., Roby-Brami, A., and Levin, M.F., Compensation for distal impairments of grasping in adults with hemiparesis, Exp. Brain Res., 2004, vol. 157, no. 2, p. 162.

    Article  PubMed  Google Scholar 

  44. Roby-Brami, A., Feydy, A., Combeaud, M., et al., Motor compensation and recovery for reaching in stroke patients, Acta Neurol. Scand., 2003, vol. 107, no. 5, p. 369.

    Article  CAS  PubMed  Google Scholar 

  45. Neumann, N. and Birbaumer, N., Predictors of successful self control during brain-computer communication, J. Neurol., Neurosurg. Psychiatry, 2003, vol. 74, no. 8, p. 1117.

    Article  CAS  Google Scholar 

  46. Kübler, A., Neumann, N., Wilhelm, B., et al., Predictability of brain-computer communication, J. Psychophysiol., 2004, vol. 18, pp. 121.

    Article  Google Scholar 

  47. Kung, P.-C., Lin, C.-C.K., and Ju, M.-S., Neurorehabilitation robot-assisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients, Clin. Biomech., 2010, vol. 25, no. 7, p. 647.

    Article  Google Scholar 

  48. Latash, M.L. and Anson, J.G., What are normal movements in atypical populations?, J. Behav. Brain Sci., 1996, vol. 19, no. 1, p. 55.

    Article  Google Scholar 

  49. Dipietro, L., Krebs, H.I., Fasoli, S.E., et al., Submovement changes characterize generalization of motor recovery after stroke, Cortex, 2009, vol. 45, no. 3, p. 318.

    Article  PubMed  Google Scholar 

  50. Feldman, A.G. and Levin, M.F., The origin and use of positional frames of reference in motor control., J. Behav. Brain Sci., 1995, vol. 18, no. 4, p. 723.

    Article  Google Scholar 

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

    Google Scholar 

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Correspondence to E. V. Biryukova.

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Original Russian Text © E.V. Biryukova, O.G. Pavlova, M.E. Kurganskaya, P.D. Bobrov, L.G. Turbina, A.A. Frolov, V.I. Davydov, A.V. Silchenko, O.A. Mokienko, 2016, published in Fiziologiya Cheloveka, 2016, Vol. 42, No. 1, pp. 19–30.

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Biryukova, E.V., Pavlova, O.G., Kurganskaya, M.E. et al. 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 42, 13–23 (2016). https://doi.org/10.1134/S0362119716010035

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