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Brain-Computer Interface for Motor Rehabilitation

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1032)

Abstract

Stroke is the fifth leading cause of death and disability in the United States with approximately 6.8 million people living with residual deficits and approximately $34 billion spent on treatment annually [1, 2]. Simultaneously, dramatic healthcare shifts have limited extended care accessibility, with many individuals discharged from restorative therapy by three-months post-stroke. Decreased access and increased costs have led clinicians, and scientists to investigate more effective and efficient interventions to improve the function of the hemiparetic upper extremity of individuals post-stroke. One such modality is a brain-computer interface (BCI) technology that utilizes brain signals to drive rehabilitation of motor function. Emerging data suggests the use of BCI for motor rehabilitation post-stroke, facilitating an individual’s return to function and improving quality of life [3, 4, 5, 6, 7, 8, 9, 10]. Specifically, integration of virtual reality (VR) and functional electrical stimulation (FES) components is an innovative rehabilitation strategy with a strong potential to reinstitute central motor programs specific to hand function in patients’ status post-stroke. By utilizing the Fugl-Meyer Assessment (FMA), researchers can monitor the motor function of the hemiparetic upper extremity pre/post-intervention, objectively quantifying the effectiveness of BCI for the restoration of upper extremity motor function [11]. Neurophysiological brain imaging techniques allow tracking changes in the neural substrates of motor function due to BCI intervention. Therefore, the purpose of our study is to demonstrate the utility of BCI-VR-FES intervention for motor rehabilitation of upper extremity, based upon the theory of neuroplasticity, in individuals’ post-stroke by using functional (FMA) and neurophysiological outcome measures.

Keywords

Brain-computer interface Virtual reality Functional electrical stimulation Stroke Motor rehabilitation 

Notes

Acknowledgments

We would like to thank both the g.tech company, Austria for providing us with the RecoveriX device, as well as AdventHealth Sports Medicine & Rehabilitation for their collaboration on this study.

References

  1. 1.
    Center for Disease Control and Prevention. https://www.cdc.gov/stroke/
  2. 2.
    Benjamin, E., et al.: Heart disease and stroke statistics—2018 update: a report from the American Heart Association. 137, e67–e492 (2018).  https://doi.org/10.1161/cir.0000000000000558
  3. 3.
    Cervera, M.A., et al.: Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. 5, 651–663 (2018).  https://doi.org/10.1002/acn3.544CrossRefGoogle Scholar
  4. 4.
    Zhang, X., Elnady, A.M., Randhawa, B.K., Boyd, L.A., Menon, C.: Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. 12, 125 (2018).  https://doi.org/10.3389/fnhum.2018.00125
  5. 5.
    Mrachacz-Kersting, N., Aliakbaryhosseinabadi, S.: Comparison of the efficacy of a real-time and offline associative brain-computer-interface. 12, 455 (2018).  https://doi.org/10.3389/fnins.2018.00455
  6. 6.
    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. 11, 400 (2017).  https://doi.org/10.3389/fnins.2017.00400
  7. 7.
    Irimia, D.C., et al.: Brain-computer interfaces with multi-sensory feedback for stroke rehabilitation: a case study: BCI for stroke rehabilitation. 41, E178–E184 (2017).  https://doi.org/10.1111/aor.13054CrossRefGoogle Scholar
  8. 8.
    Kim, T., Kim, S., Lee, B.: Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial: effects of AOT Plus BCI-FES on arm motor recovery. 23, 39–47 (2016).  https://doi.org/10.1002/oti.1403CrossRefGoogle Scholar
  9. 9.
    Pichiorri, F., et al.: Brain–computer interface boosts motor imagery practice during stroke recovery. 77, 851–865 (2015).  https://doi.org/10.1002/ana.24390CrossRefGoogle Scholar
  10. 10.
    Ang, K.K., et al.: A large clinical study on the ability of stroke patients to use an EEG-based motor imagery brain-computer interface. 42, 253–258 (2011).  https://doi.org/10.1109/iembs.2009.5335381
  11. 11.
    Kim, H., et al.: Reliability, concurrent validity, and responsiveness of the Fugl-Meyer Assessment (FMA) for hemiplegic patients. 24, 893–899 (2012).  https://doi.org/10.1589/jpts.24.893CrossRefGoogle Scholar
  12. 12.
    Hankey, G.J., Jamrozik, K., Broadhurst, R.J., Forbes, S., Anderson, C.S.: Long-term disability after first-ever stroke and related prognostic factors in the Perth Community Stroke Study, 1989–1990. 33, 1034–1040 (2002).  https://doi.org/10.1161/01.str.0000012515.66889.24CrossRefGoogle Scholar
  13. 13.
    Calabrò, R.S., et al.: Robotic neurorehabilitation in patients with chronic stroke. Int. J. Rehabil. Res. 38, 219–225 (2015).  https://doi.org/10.1097/MRR.0000000000000114CrossRefGoogle Scholar
  14. 14.
    Doyle, S.D., Bennett, S., Dudgeon, B.J.: Sensory impairment after stroke: exploring therapists’ clinical decision making. Can. J. Occup. Ther. 81, 215–225 (2014).  https://doi.org/10.1177/0008417414540516CrossRefGoogle Scholar
  15. 15.
    Doyle, S., Bennett, S., Fasoli, S.E., Mckenna, K.T.: Interventions for sensory impairment in the upper limb after stroke. Cochrane Database Syst. Rev. (2010).  https://doi.org/10.1002/14651858.cd006331.pub2
  16. 16.
    Kiper, P., et al.: Computational models and motor learning paradigms: could they provide insights for neuroplasticity after stroke? An overview. J. Neurol. Sci. 369, 141–148 (2016).  https://doi.org/10.1016/j.jns.2016.08.019CrossRefGoogle Scholar
  17. 17.
    Wolpert, D.M., Flanagan, J.R.: Motor learning. Curr. Biol. CB 20, R467–R472 (2010).  https://doi.org/10.1016/j.cub.2010.04.035CrossRefGoogle Scholar
  18. 18.
    Kleim, J.A., Jones, T.A.: Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. 51, S225–S239 (2008).  https://doi.org/10.1044/1092-4388(2008/018)
  19. 19.
    Cho, W., et al.: Hemiparetic stroke rehabilitation using avatar and electrical stimulation based on non-invasive brain computer interface. 5 (2017).  https://doi.org/10.4172/2329-9096.1000411
  20. 20.
    Cho, W., et al.: Paired associative stimulation using brain-computer interfaces for stroke rehabilitation: a pilot study. 26 (2016).  https://doi.org/10.4081/ejtm.2016.6132
  21. 21.
    Irimia, D., et al.: recoveriX: a new BCI-based technology for persons with stroke. 2016, 1504 (2016).  https://doi.org/10.1109/embc.2016.7590995
  22. 22.
    Monge-Pereira, E., et al.: Use of electroencephalography brain computer interface systems as a rehabilitative approach for upper limb function after a stroke. A systematic review. 9, 918–932 (2017)Google Scholar
  23. 23.
    Venkatakrishnan, A., Francisco, G.E., Contreras-Vidal, J.L.: Applications of brain–machine interface systems in stroke recovery and rehabilitation. 2, 93–105 (2014).  https://doi.org/10.1007/s40141-014-0051-4CrossRefGoogle Scholar
  24. 24.
    Ang, K.K., et al.: A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. 46, 310–320 (2015).  https://doi.org/10.1177/1550059414522229CrossRefGoogle Scholar
  25. 25.
    Ang, K.K., Guan, C: EEG-based strategies to detect motor imagery for control and rehabilitation. 25, 392–401 (2017).  https://doi.org/10.1109/tnsre.2016.2646763CrossRefGoogle Scholar
  26. 26.
    Calabrò, R.S., et al.: The role of virtual reality in improving motor performance as revealed by EEG: a randomized clinical trial. 14, 1–16 (2017).  https://doi.org/10.1186/s12984-017-0268-4
  27. 27.
    Alon, G., Levitt, A.F., McCarthy, P.A.: Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: a preliminary study. 87, 627–636 (2008).  https://doi.org/10.1097/phm.0b013e31817fabc1CrossRefGoogle Scholar
  28. 28.
    Gladstone, D.J., Danells, C.J., Black, S.E.: The Fugl-Meyer Assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabilitation Neural Repair 16, 232–240 (2002).  https://doi.org/10.1177/154596802401105171CrossRefGoogle Scholar
  29. 29.
    Page, S.J., Hade, E., Persch, A.: Psychometrics of the wrist stability and hand mobility subscales of the Fugl-Meyer Assessment in moderately impaired stroke. 95, 103–108 (2015).  https://doi.org/10.2522/ptj.20130235CrossRefGoogle Scholar
  30. 30.
    Woytowicz, E.J., et al.: Determining levels of upper extremity movement impairment by applying a cluster analysis to the Fugl-Meyer Assessment of the upper extremity in chronic stroke. 98, 456–462 (2017).  https://doi.org/10.1016/j.apmr.2016.06.023CrossRefGoogle Scholar
  31. 31.
    Hoonhorst, M.H., et al.: How do Fugl-Meyer arm motor scores relate to dexterity according to the action research arm test at 6 months poststroke? 96, 1845–1849 (2015).  https://doi.org/10.1016/j.apmr.2015.06.009CrossRefGoogle Scholar
  32. 32.
    Woodbury, M.L., Velozo, C.A., Richards, L.G., Duncan, P.W.: Rasch analysis staging methodology to classify upper extremity movement impairment after stroke. 94, 1527–1533 (2013).  https://doi.org/10.1016/j.apmr.2013.03.007CrossRefGoogle Scholar
  33. 33.
    Michaelsen, S.M., Luta, A., Roby-Brami, A., Levin, M.F.: Effect of trunk restraint on the recovery of reaching movements in hemiparetic patients. 32, 1875–1883 (2001).  https://doi.org/10.1161/01.str.32.8.1875CrossRefGoogle Scholar
  34. 34.
    Pang, M.Y., Harris, J.E., Eng, J.J.: A community-based upper-extremity group exercise program improves motor function and performance of functional activities in chronic stroke: a randomized controlled trial. 87, 1–9 (2006).  https://doi.org/10.1016/j.apmr.2005.08.113CrossRefGoogle Scholar
  35. 35.
    Duncan, P.W., Wallace, D., Lai, S.M., Johnson, D., Embretson, S., Laster, L.J.: The stroke impact scale version 2.0: evaluation of reliability, validity, and sensitivity to change. 30, 2131–2140 (1999).  https://doi.org/10.1161/01.str.30.10.2131CrossRefGoogle Scholar
  36. 36.
    Chen, H.M., Chen, C.C., Hsueh, I.P., Huang, S.L., Hsieh, C.L.: Test-retest reproducibility and smallest real difference of 5 hand function tests in patients with stroke. 23, 435 (2009).  https://doi.org/10.1177/1545968308331146CrossRefGoogle Scholar
  37. 37.
    Fulk, G.D., Echternach, J.L.: Test-retest reliability and minimal detectable change of gait speed in individuals undergoing rehabilitation after stroke. 32, 8–13 (2008).  https://doi.org/10.1097/npt0b013e31816593c0CrossRefGoogle Scholar
  38. 38.
    Webster, K.E., Wittwer, J.E., Feller, J.A.: Validity of the GAITRite walkway system for the measurement of averaged and individual step parameters of gait. 22, 317–321 (2005).  https://doi.org/10.1016/j.gaitpost.2004.10.005CrossRefGoogle Scholar
  39. 39.
    Bohannon, R.W., Andrews, A.W., Glenney, S.S.: Minimal clinically important difference for comfortable speed as a measure of gait performance in patients undergoing inpatient rehabilitation after stroke. 25, 1223–1225 (2013).  https://doi.org/10.1589/jpts.25.1223CrossRefGoogle Scholar
  40. 40.
    Lin, K., et al.: Minimal detectable change and clinically important difference of the Stroke Impact Scale in stroke patients. 24, 486 (2010).  https://doi.org/10.1177/1545968309356295CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Physical TherapyAdventHealth UniversityOrlandoUSA
  2. 2.MEG LabAdventHealth for ChildrenOrlandoUSA
  3. 3.Functional Brain Mapping and Brain-Computer Interface LabAdventHealth for ChildrenOrlandoUSA

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