Brain–Computer Interfaces in Poststroke Rehabilitation: a Clinical Neuropsychological Study

  • R. Kh. LyukmanovEmail author
  • G. A. Aziatskaya
  • O. A. Mokienko
  • N. A. Varako
  • M. S. Kovyazina
  • N. A. Suponeva
  • L. A. Chernikova
  • A. A. Frolov
  • M. A. Piradov

Objectives. To assess the efficacy of using a brain–computer interface with a hand exoskeleton (BCI–exoskeleton) in the complex rehabilitation of patients with the sequelae of cerebrovascular accidents and to determine the minimally adequate reserves of cognitive functions required for the patient to carry out effective mental training using the movement imagination paradigm. Materials and methods. The study included 55 patients (median age 54.0 [44.0; 61.0] years, median time since stroke 6.0 [3.0; 13.0] months) in study and control (simulation of BCI) groups. The severity of paresis was evaluated on the Fugl–Meyer Assessment of Motor Recovery after Stroke (FMA) scale and the Action Research Arm Test (ARAT). Neuropsychological investigations to identify predictors for learning by movement imagination were carried out in 12 patients of the study group before training started. After investigations, patients received courses of movement imagination (hand extension) training using a BCI to control a hand exoskeleton. On average, patients received 10 30-min training sessions. After training, repeat assessments of parameters on motor scales were run, along with analysis of electroencephalography data obtained during training sessions; these results were compared with neuropsychological investigation data. Results and conclusions. Both groups showed improvements in upper limb motor function on the ARAT and Fugl–Meyer (sections A–D, H, I) scales. Only the BCI-exoskeleton group showed improvements in the ball grasp (p = 0.012), finger pinch grip (p = 0.012), and gross arm movements (p = 0.002) scores on the ARAT scale. A significant correlation was found between BCI movement quality indicators with various neuropsychological test results: Taylor figures, Head test, reaction choice test. Thus, inclusion of the BCI-exoskeleton system into the complex rehabilitation of patients with poststroke upper limb paresis significantly improves a number of measures of grasping and movement functions in the proximal segments of the upper limb. Use of neuropsychological tests as screening to select patients may help with the personalized application of rehabilitation technologies.


stroke poststroke rehabilitation central upper limb paresis brain–computer interface exoskeleton 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. Langhorne, F. Coupar, and A. Pollock, “Motor recovery after stroke: a systematic review,” Lancet Neurology, 8, No. 8, 741–754 (2009), Scholar
  2. 2.
    A. Pollock, S. E. Farmer, M. C. Brady, et al., “Interventions for improving upper limb function after stroke,” Cochrane Database Syst. Rev., 11, CD010820 (2014),
  3. 3.
    I. V. Sidyakina, T. V. Shapovalenko, and K. V. Lyadov, “Mechanisms of neuroplasticity and rehabilitation in the acute period of stroke,” Ann. Klin. Eksperim. Nevrol., 7, No. 1, 52–56 (2013).Google Scholar
  4. 4.
    S. M. Hatem, G. Saussez, M. Della Faille, et al., “Rehabilitation of motor function after stroke: A multiple systematic review focused on techniques to stimulate upper extremity recovery,” Front. Hum. Neurosci., 10, 442 (2016),
  5. 5.
    R. E. Barclay-Goddard, T. J. Stevenson, W. Poluha, and L. Thalman, “Mental practice for treating upper extremity deficits in individuals with hemiparesis after stroke,” Cochrane Database Syst. Rev., 5, CD005950,
  6. 6.
    C. J. Winstein, J. Stein, R. Arena, et al., “Guidelines for Adult Stroke Rehabilitation and Recovery: A guideline for healthcare professionals from the American Heart Association/American Stroke Association,” Stroke, 47, No. 6, 98–169 (2016),
  7. 7.
    O. A. Mokienko, L. A. Chernikova, A. A. Frolov, and P. D. Bobrov, “Motor imagery and its practical application,” Zh. Vyssh. Nerv. Deiat., 63, No. 2, 195–204 (2013), Scholar
  8. 8.
    R. Schmidt and T. Lee, Motor Control and Learning: A Behavioral Emphasis, Human Kinetics, Champaign, IL (1999), 3rd ed., Scholar
  9. 9.
    S. Bajaj, A. J. Butler, D. Drake, and M. Dhamala, “Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation,” NeuroImage Clin, 8, 572–582 (2015), Scholar
  10. 10.
    N. Sharma, J. C. Baron, and J. B. Rowe, “Motor imagery after stroke: relating outcome to motor network connectivity,” Ann. Neurol., 66, No. 5, 604–616 (2009), Scholar
  11. 11.
    G. Pfurtscheller and F. H. Lopes da Silva, “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clin. Neurophysiol., 110, No. 11, 1842–1857 (1999), Scholar
  12. 12.
    K. K. Ang, C. Guan, K. S. Phua, 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., 7, 30 (2014),
  13. 13.
    K. K. Ang, K. S. Phua, C. Wang, et al., “A randomized controlled trial of EEG-based motor imagery brain–computer interface robotic rehabilitation for stroke,” Clin. EEG Neurosci., 46, No. 4, 310–320 (2015), Scholar
  14. 14.
    A. Ramos-Murguialday, D. Broetz, M. Rea, et al., “Brain-machine interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol., 74, No. 1, 100–108 (2013), Scholar
  15. 15.
    T. Ono, K. Shindo, K. Kawashima, et al., “Brain–computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke,” Front. Neuroeng., 7, 19 (2014),
  16. 16.
    A. A. Frolov, O. A. Mokienko, R. Kh. Lyukmanov, et al., “Preliminary results of a controlled study of BCI-exoskeleton technology efficacy in patients with poststroke arm paresis,” Bull. RSMU, 2, 17–25 (2016).Google Scholar
  17. 17.
    A. A. Frolov, L. A. Chernikova, R. Kh. Lyukmanov, et al., Use of ‘Noninvasive Brain–Computer-Interface–Hand-Exoskeleton’ Medical Technology, Pirogov National Medical Research University, Moscow (2016).Google Scholar
  18. 18.
    O. A. Mokienko, R. Kh. Lyukmanov, L. A. Chernikova, et al., “A brain–computer interface: first experience of clinical use in Russia,” Fiziol. Cheloveka, 42, No. 1, 31–39 (2016), Scholar
  19. 19.
    O. A. Mokienko, L. A. Chernikova, and A. A. Frolov, “A brain–computer interface as a new neurorehabilitation technology,” Ann. Klin. Eksperim. Nevrol., 5, No. 3, 46–52 (2011).Google Scholar
  20. 20.
    A. Compston, “Aids to the investigation of peripheral nerve injuries, Medical Research Council: Nerve Injuries Research Committee, His Majesty’s Stationery Office (1942); pp. 48 (iii) and 74 figures and 7 diagrams; with aids to the examination of the peripheral nervous system, Michael O’Brien for the Guarantors of Brain. Saunders Elsevier (2010); pp. [8] 64 and 94 Figures,” Brain, 133, No. 10, 2838–2844 (2010), Scholar
  21. 21.
    R. C. Oldfield, “The assessment and analysis of handedness: the Edinburgh inventory,” Neuropsychologia, 9, No. 1, 97–113 (1971), Scholar
  22. 22.
    C. Bocti, V. Legault, N. Leblanc, et al., “Vascular cognitive impairment: most useful subtests of the Montreal Cognitive Assessment in minor stroke and transient ischemic attack,” Dement. Geriatr. Cogn. Disord., 36, No. 3–4, 154–162 (2013), Scholar
  23. 23.
    R. W. Bohannon and M. B. Smith, “Interrater reliability of a modified Ashworth scale of muscle spasticity,” Phys. Ther., 67, No. 2, 206–207 (1987), Scholar
  24. 24.
    A. Frolov, D. Husek, and P. Bobrov, “Comparison of four classification methods for brain computer interface,” Neural Network World, 21, No. 2, 101–111 (2011), Scholar
  25. 25.
    P. D. Bobrov, A. V. Korshakov, V. Roshchin, and A. A. Frolov, “Bayesian classifier for brain–computer interface based on mental representation of movements,” Zh. Vyssh. Nerv. Deyat., 62, No. 1, 89–99 (2012).Google Scholar
  26. 26.
    J. Sanford, J. Moreland, L. R. Swanson, et al., “Reliability of the Fugl–Meyer assessment for testing motor performance in patients following stroke,” Phys. Ther., 73, No. 7, 447–454 (1993), Scholar
  27. 27.
    S. A. Doussoulin, S. R. Rivas, and S. V. Campos, “Validation of ‘Action Research Arm Test’ (ARAT) in Chilean patients with a paretic upper limb after a stroke,” Rev. Med. Chile, 140, No. 1, 59–65 (2012), Scholar
  28. 28.
    A. R. Luriya, Higher Cortical Functions in Humans and Their Impair ments in Local Brain Injury, Moscow State University, Moscow (1962).Google Scholar
  29. 29.
    E. D. Khomskaya, Neuropsychological Diagnosis, Voenizdat, Moscow (1994).Google Scholar
  30. 30.
    A. V. Semenovich, A Scheme for Neuropsychological Investigations in Children, Moscow (1999).Google Scholar
  31. 31.
    E. I. Rasskazova, M. S. Kovyazina, and N. A. Varako, “Use of screening scales in neuropsychological rehabilitation: potentials, requirements, and limitations,” Vestn. Yuzhn.-Urals. Gos. Univ. Ser. Psikhol., 9, No. 3, 5–15 (2016).Google Scholar
  32. 32.
    L. A. Chernikova, “Robot systems in neurorehabilitation,” Ann. Klin. Eksperim. Nevrol., 3, No. 3, 30–36 (2009).Google Scholar
  33. 33.
    N. A. Varako, G. A. Aziatskaya, M. S. Kovyazina, et al., “Motor imagery: neuropsychological predictors of failure in post stroke patients,” Cerebrovasc. Dis., 43, No. 1, 64 (2017),
  34. 34.
    M. S. Kovyazina, G. A. Aziatskaya, R. Kh. Lyukmanov, et al., “Neuropsychological predictors of BCI-enhanced mental practice efficacy in post stroke patients,” Brain Inj., 31, No. 6–7, 813 (2017),

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • R. Kh. Lyukmanov
    • 1
    • 2
    Email author
  • G. A. Aziatskaya
    • 1
  • O. A. Mokienko
    • 1
    • 2
  • N. A. Varako
    • 1
    • 3
  • M. S. Kovyazina
    • 1
    • 3
  • N. A. Suponeva
    • 1
  • L. A. Chernikova
    • 1
  • A. A. Frolov
    • 4
  • M. A. Piradov
    • 1
  1. 1.Research Center of NeurologyMoscowRussia
  2. 2.Pirogov Russian National Research Medical UniversityMoscowRussia
  3. 3.Lomonosov Moscow State UniversityMoscowRussia
  4. 4.Institute of Higher Nervous Activity and NeurophysiologyRussian Academy of SciencesMoscowRussia

Personalised recommendations