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Establishing Clinical Protocols for BCI-Based Motor Rehabilitation in Individuals Post Stroke - The Impact of Feedback Type and Selected Outcome Measures: A Systematic Review

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HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13519))

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Abstract

Stroke is a major cause of disability resulting in multiple system impairments. Limited extended care resulted in prioritizing high level repetitions of task-specific activities to improve function. One such modality is BCI to drive motor rehabilitation. While several systematic reviews and meta-analyses highlight the benefits of utilizing BCIs to enhance motor recovery, it is still unclear how these interventions facilitate rehabilitation of motor function in individuals post-stroke. This systematic review analyzed outcome measures and type of feedback during BCI interventions to inform future protocol development. Included articles were held to rigorous criteria, and potential studies were assessed for methodological quality using the PEDro Scale. Only articles that scored six or greater were included for analysis, and nine randomized controlled trials were included. In brief, the randomized controlled trials demonstrated that BCI enhanced the motor function of the upper extremity as measured by the FMA UE, however no other consistent outcome measures of function or self-efficacy were reported. EEG and ERD of the affected sensorimotor cortices were significantly enhanced in the BCI groups (p < 0.05). For those studies that measured retention of function, long-lasting improvements were noted, and BCI coupled to FES elicited significant, clinically relevant motor recovery. Somatosensory/motor and visual feedback were the most common across reviewed studies. While each of the studies had a wide variety of methods, all the evidence suggested that subjects improved. These findings suggest that the most important concept in protocol development may have been the incorporation of principles of motor learning.

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References

  1. Sacco, R.L., Kasner, S.E., Broderick, J.P., et al.: An Updated definition of stroke for the 21st century. AHA J. 44(7), 2064–2089 (2013)

    Google Scholar 

  2. Go, A.S., Mozaffarian, D., Roger, V.L., et al. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. PMC 127(1) (2013)

    Google Scholar 

  3. Tsao, C.W., Aday, A.W., Almarzooq, Z.I., et al.: Heart disease and stroke statistics - 2022 update: a report from the American Heart Association. Circulation 145, e153–e639 (2022)

    Article  Google Scholar 

  4. Girotra, T., Lekoubou, A., Bishu, K.G., et al.: A contemporary and comprehensive analysis of the costs of stroke in the United States. J. Neurol. Sci. 410, 116643 (2020)

    Article  Google Scholar 

  5. Stroke CDC, https://www.cdc.gov/stroke/facts.htm. Accessed 24 Oct 2018

  6. Benjamin, E.J., et al.: Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 135(10), e146–e603 (2017)

    Article  Google Scholar 

  7. Cervera, M.A., et al.: Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann. Clin. Transl. Neurol. 5, 651–663 (2018)

    Article  Google Scholar 

  8. Zhang, X., et al.: Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. Front. Hum. Neurosci. 12, 125 (2018)

    Article  Google Scholar 

  9. Mrachacz-Kersting, N., Aliakbaryhosseinabadi, S.: Comparison of the efficacy of a real-time and offline associative brain-computer-interface. Front. Neurosci. 12, 455 (2018)

    Article  Google Scholar 

  10. 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 (2017)

    Google Scholar 

  11. Irimia, D.C., et al.: Brain‐computer interfaces with multi‐sensory feedback for stroke rehabilitation: a case study. Artif. Org. 41, E178–E184 (2017)

    Google Scholar 

  12. 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. Occup. Ther. Int. 23(1), 39–47 (2016)

    Article  Google Scholar 

  13. Pichiorri, F., et al.: Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851–865 (2015)

    Article  Google Scholar 

  14. 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. PMR 9(9), 918–932 (2017)

    Google Scholar 

  15. Cervera, M.A., Soekadar, S.R., Ushiba, J., Millán, J.d.R., et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann. Clin. Transl. Neuro. 5, 651–663 (2018)

    Google Scholar 

  16. Carvalho, R., Dias, N., Cerqueira, J.J.: Brain-machine interface of upper limb recovery in stroke patients rehabilitation: a systematic review. Physiother. Res. Int. 24(2), e1764 (2019)

    Article  Google Scholar 

  17. Bai, Z., Fong, K.N.K., Zhang, J.J., Chan, J., Ting, K.H. Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. [Meta-Analysis Research Support, Non-U.S. Gov't Systematic Review]. J Neuroeng. Rehabil. 17(1) (2020)

    Google Scholar 

  18. Kruse, A., Suica, Z., Taeymans, J., Schuster-Amft, C.: Effect of brain-computer interface training based on non-invasive electroencephalography using motor imagery on functional recovery after stroke - a systematic review and meta-analysis. BMC Neurol. 20(1) (2020)

    Google Scholar 

  19. Baniqued, P.D.E., et al.: Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J. Neuroeng. Rehabil. 18(1) (2021)

    Google Scholar 

  20. Camargo-Vargas, D., Callejas-Cuervo, M., Mazzoleni, S.: Brain-computer interfaces systems for upper and lower limb rehabilitation: a systematic review. Sensors 21(13), 4312 (2021)

    Article  Google Scholar 

  21. Mansour, S., Ang, K.K., Nair, K.P.S., Phua, K.S., Arvaneh, M.: Efficacy of brain-computer interface and the impact of its design characteristics on poststroke upper-limb rehabilitation: a systematic review and meta-analysis of randomized controlled trials. Clin. EEG Neurosci. 53(1), 79–90 (2022)

    Article  Google Scholar 

  22. Yang, W., Zhang, X., Li, Z., Zhang, Q., Xue, C., Huai, Y.: The effect of brain-computer interface training on rehabilitation of upper limb dysfunction after stroke: a meta-analysis of randomized controlled trials. Front. Neurosci. 15, 766–879 (2022)

    Article  Google Scholar 

  23. Horowitz, A.J., Guger, C., Korostenskaja, M. What external variables affect sensorimotor rhythm brain-computer interface (SMR-BCI) performance? HCA Healthc. J. Med. 2(3) (2021)

    Google Scholar 

  24. Clark, E., et al.: Brain-computer interface for motor rehabilitation. In: Stephanidis, C. (ed.) HCII 2019. CCIS, vol. 1032, pp. 243–254. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23522-2_31

    Chapter  Google Scholar 

  25. Fugl-Meyer, A.R., Jaasko, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. A method for evaluation of physical performance. Scand. J. Rehabil. Med. 7, 13–31 (1975)

    Google Scholar 

  26. Physiotherapy Evidence Database. https://pedro.org.au/english/summary-of-measurement-properties-of-the-pedro-scale/. Accessed 20 May 2022

  27. Cashin, A.G., et al.: Measurement properties of the PEDro scale (2020): clinimetrics: physiotherapy evidence database (PEDro) scale. J. Physiother. 66(1), 59 (2020)

    Article  Google Scholar 

  28. Maher, C.G., et al.: Reliability of the PEDro scale for rating quality of randomized controlled trials. Phys. Ther. 83(8), 713–721 (2003)

    Article  Google Scholar 

  29. Moseley, A.M., et al.: Agreement between the Cochrane risk of bias tool and physiotherapy evidence database (PEDro) scale: a meta-epidemiological study of randomized controlled trials of physical therapy interventions. PLoS ONE 14(9), e0222770 (2019)

    Article  Google Scholar 

  30. Biasiucci, A., Leeb, R., Iturrate, I., et al.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9(1) (2018)

    Google Scholar 

  31. Cantillo-Negrete, J., et al.: Brain-computer interface coupled to a robotic hand orthosis for stroke patients’ neurorehabilitation: a crossover feasibility study. Front. Hum. Neurosci. 15, 656–975 (2021)

    Article  Google Scholar 

  32. Chen, S., et al.: Longitudinal electroencephalography analysis in subacute stroke patients during intervention of brain-computer interface with exoskeleton feedback. Front. Neurosci.14 (2020)

    Google Scholar 

  33. Chew, E., et al.: Using transcranial direct current stimulation to augment the effect of motor imagery-assisted brain-computer interface training in chronic stroke patients-cortical reorganization considerations. Front. Neurol. 11 (2020)

    Google Scholar 

  34. Hu, Y.Q., et al.: Motor imagery-based brain-computer interface combined with multimodal feedback to promote upper limb motor function after stroke: a preliminary study. Evid. Based Complement. Alternat. Med. 3 (2021)

    Google Scholar 

  35. Li, M., Liu, Y., Wu, Y., Liu, S., Jia, J., Zhang, L.: Neurophysiological substrates of stroke patients with motor imagery based brain-computer interface training. Int. J. Neurosci. 124, 403–415 (2014)

    Article  Google Scholar 

  36. Li, X., et al.: Sensorimotor rhythm-brain computer interface with audio-cue, motor observation and multisensory feedback for upper-limb stroke rehabilitation: a controlled study. Front. Neurosci. 16 (2021)

    Google Scholar 

  37. Ramos-Murguialday, A., Broetz, D., Rea, M., et al.: Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74(1), 100–108 (2013)

    Article  Google Scholar 

  38. Furlan, L., Sterr, A.: The applicability of standard error of measurement and minimum detectable change to motor learning research - a behavior study. Front. Hum. Neurosci. 12 (2018)

    Google Scholar 

  39. Portney, L.G., Watkins, M.P.: Foundations of Clinical Research: Applications to Practice, 3rd edn. Pearson/Prentice Hall, Upper Saddle River (2009)

    Google Scholar 

  40. Moore, J.L., et al.: A core set of outcome measures for adults with neurologic conditions undergoing rehabilitation. A clinical practice guideline. J. Neurol. Phys. Therapy 42, 74–220 (2018)

    Google Scholar 

  41. Salter, K., Jutai, J.W., Teasell, R., Foley, N.C., Bitensky, J., Bayley, J.: Issues for selection of outcome measures in stroke rehabilitation: ICF activity. Disabil. Rehabil. 27(6), 315–340 (2015)

    Article  Google Scholar 

  42. Pichiorri, F., Fallani, F.D.V., Cincotti, F., Babiloni, F., Molinari, M., et al.: Sensorimotor rhythm-based brain–computer interface training: the impact on motor cortical responsiveness. J. Neural. Eng. 8, 025020 (2011)

    Article  Google Scholar 

  43. Angulo-Sherman, I.N., Gutierrez, D.: A link between the increase in electroencephalographic coherence and performance improvement in operating a brain-computer interface. Comput. Intell. Neurosci. 2015, 824175 (2015)

    Article  Google Scholar 

  44. Darvishi, S., Gharabaghi, A., Boulay, C.B., Ridding, M.C., Abbott, D., Baumert, M.: Proprioceptive feedback facilitates motor imagery-related operant learning of sensorimotor beta-band modulation. Front. Neurosci. 11, 60 (2017)

    Article  Google Scholar 

  45. Vukelic, M., Gharabaghi, A.: Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality. Neuroimage 111, 1–11 (2015)

    Article  Google Scholar 

  46. McCreadie, K.A., Coyle, D.H., Prasad, G.: Learning to modulate sensorimotor rhythms with stereo auditory feedback for a brain-computer interface. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2012, pp. 6711–6714 (2012)

    Google Scholar 

  47. Kleim, J.A., Jones, T.A.: Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J. Speech Lang. Hear. Res. 51, S225–S239 (2008)

    Google Scholar 

  48. Clark, E, et al.: Computer Interface for Motor Rehabilitation. In: Stephanidis, C. (eds) HCI International 2019 - Posters. HCII 2019. Communications in Computer and Information Science, vol. 1032. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23522-2_31

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Authors and Affiliations

  1. AdventHealth University, Orlando, FL, 32803, USA

    Elizabeth Clark, Adrienne Czaplewski, Khoa Nguyen, Patrick Pasciucco & Marimar Rios

  2. AdventHealth, Functional Brain Mapping and BCI Lab, Orlando, FL, 32803, USA

    Milena Korostenskaja

  3. The Institute of Neuroapproaches, Winter Springs, FL, 32708, USA

    Milena Korostenskaja

Authors
  1. Elizabeth Clark
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  2. Adrienne Czaplewski
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  4. Patrick Pasciucco
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  5. Marimar Rios
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  6. Milena Korostenskaja
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Corresponding author

Correspondence to Milena Korostenskaja .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  3. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  4. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  5. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

  6. Smart Future Initiative, Frankfurt am Main, Germany

    Norbert A. Streitz

  7. Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

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Clark, E., Czaplewski, A., Nguyen, K., Pasciucco, P., Rios, M., Korostenskaja, M. (2022). Establishing Clinical Protocols for BCI-Based Motor Rehabilitation in Individuals Post Stroke - The Impact of Feedback Type and Selected Outcome Measures: A Systematic Review. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments. HCII 2022. Lecture Notes in Computer Science, vol 13519. Springer, Cham. https://doi.org/10.1007/978-3-031-17618-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-17618-0_27

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