Serious Game Controlled by a Human-Computer Interface for Upper Limb Motor Rehabilitation: A Feasibility Study

  • Sergio David Pulido
  • Álvaro José Bocanegra
  • Sandra Liliana Cancino
  • Juan Manuel LópezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11868)


Stroke affects the population worldwide, with a prevalence of 0.58% worldwide. One of the possible consequences is the negative impact in the motor function of the patient, limiting their quality of life. For these reason, Brain-Computer Interfaces are studied as a tool for improving rehabilitation processes. Nevertheless, to the best of our knowledge, there are no Brain-Computer Interface systems which use video-games for upper limb motor rehabilitation. This study aimed to design and assess a Human-Computer Interface that includes electroencephalography, forearm motion and postural analysis, with healthy subjects. This assessment was made by designing two scenarios in which the participant carried out exercises involving the mouth and the hand and forearm trajectory symmetry. Results show that the system is ready to be tested on patients, since the participants were comfortable using it. Also, the quantitative results, particularly, the metrics used in the video-game, are an important start for health professionals to characterize motor rehabilitation in stroke patients, enabling the path to the use of the designed system in motor rehabilitation therapies.


Human-Computer Interface Motor rehabilitation Stroke Serious games 


  1. 1.
    Vargas, G., et al.: On behalf of Observatorio Nacional de Salud - Colombia: Carga de enfermedad por enfermedades crónicas, no transmisibles y discapacidad en Colombia. MinSalud (2015)Google Scholar
  2. 2.
    Benjamin, E.J., et al.: Heart Disease and Stroke Statistics - 2018 Update. Circulation, New York (2018)Google Scholar
  3. 3.
    Johnson, N.N., et al.: Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke. J. Neural Eng. 15(1), 016009 (2018)CrossRefGoogle Scholar
  4. 4.
    Frolov, A.A., et al.: Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI)-controlled hand exoskeleton: a randomized controlled multicenter trial. Front. Neurosci. 11, 400–411 (2017)CrossRefGoogle Scholar
  5. 5.
    Li, M., et al.: Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training. Int. J. Neurosci. 124(6), 403–415 (2014)CrossRefGoogle Scholar
  6. 6.
    Ibáñez, J., et al.: Low latency estimation of motor intentions to assist reaching movements along multiple sessions in chronic stroke patients: a feasibility study. Front. Neurosci. 11, 126 (2017)Google Scholar
  7. 7.
    Mrachacz-Kersting, N., et al.: Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 115(3), 1410–1421 (2016)CrossRefGoogle Scholar
  8. 8.
    Marquez-Chin, C., Marquis, A., Popovic, M.R.: BCI-triggered functional electrical stimulation therapy for upper limb. Eur. J. Transl. Myol. 26(3), 6222 (2016)CrossRefGoogle Scholar
  9. 9.
    Ono, T., et al.: Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. 7, 19 (2014)CrossRefGoogle Scholar
  10. 10.
    Young, B.M., et al.: Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front. Neuroeng. 7, 25 (2014)Google Scholar
  11. 11.
    Foldes, S.T., Weber, D.J., Collinger, J.L.: MEG-based neurofeedback for hand rehabilitation. J. Neuroeng. Rehabil. 12, 85–93 (2015)CrossRefGoogle Scholar
  12. 12.
    Pulido, S.D., López, J.M.: Brain-computer interface based on detection of movement intention as a means of brain wave modulation enhancement. In: 13th International Symposium on Medical Information Processing and Analysis. Proceedings of SPIE, San Andrés, Colombia (2017)Google Scholar
  13. 13.
    Mrachacz-Kersting, N.: Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface. J. Neurophysiol. 115(3), 1410–1421 (2016)CrossRefGoogle Scholar
  14. 14.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Oxford University Press, Oxford (2006)zbMATHGoogle Scholar
  15. 15.
    Charness, A.: Stroke/Head Injury: A Guide to Functional Outcomes in Physical Therapy Management, 1st edn. Aspen Systems Corporation, Chicago (1986)Google Scholar
  16. 16.
    Kommalapati, R., Michmizos, K.P.: Virtual reality for pediatric neuro-rehabilitation: adaptive visual feedback of movement to engage the mirror neuron system. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, pp. 5849–5852 (2016)Google Scholar
  17. 17.
    Bermúdez i Badia, S., Fluet, G.G., Llorens, R., Deutsch, J.E.: Virtual reality for sensorimotor rehabilitation post stroke: design principles and evidence. In: Reinkensmeyer, D.J., Dietz, V. (eds.) Neurorehabilitation Technology, pp. 573–603. Springer, Cham (2016). Scholar
  18. 18.
    Monteiro-Junior, R., Vaghetti, C.O., Nascimento, O.J., Laks, J., Deslandes, A.: Exergames: neuroplastic hypothesis about cognitive improvement and biological effects on physical function of institutionalized older persons. Neural Regen. Res. 11(2), 201 (2016)CrossRefGoogle Scholar
  19. 19.
    Nikolaidis, A., Voss, M.W., Lee, H., Vo, L.T.K., Kramer, A.F.: Parietal plasticity after training with a complex video game is associated with individual differences in improvements in an untrained working memory task. Front. Hum. Neurosci. 8, 169 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sergio David Pulido
    • 1
  • Álvaro José Bocanegra
    • 1
  • Sandra Liliana Cancino
    • 1
  • Juan Manuel López
    • 1
    Email author
  1. 1.Escuela Colombiana de Ingenierí­a Julio GaravitoBogotá D.CColombia

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