Brain–Computer Interface Using Functional Near-Infrared Spectroscopy for Post-Stroke Motor Rehabilitation: Case Series

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Abstract

Introduction. Non-invasive brain–computer interfaces (BCIs) enable feedback motor imagery [MI] training in neurological patients to support their motor rehabilitation. Nowadays, the use of BCIs based on functional near-infrared spectroscopy (fNIRS) for motor rehabilitation is yet to be investigated.

Objective: To evaluate the potential fNIRS BCI use in hand MI training for comprehensive post-stroke rehabilitation.

Materials and methods. This pilot study included clinically stable patients with mild-to-moderate post-stroke hand paresis. In addition to the standard rehabilitation, the patients underwent 10 nine-minute MI fNIRS BCI training sessions. To evaluate the quality of fNIRS BCI control, we assessed the percentage of time during which the classifier accurately detected patient's mental state. We scored the hand function using the Action Research Arm Test (ARAT) and the Fugl-Meyer Assessment (FMA).

Results. The study included 5 patients at 1 day to 12 months of stroke. All the participants completed the study. All study participants achieved BCI control rates higher than random (41–68%). While three patients demonstrated the clinically significant improvements in their ARAT scores, one of them also showed an improvement in the FMA score. All the participants reported experiencing drowsiness during training.

Conclusions. Post-stroke patients can operate the fNIRS BCI system under investigation. We suggest adjusting the feedback system, extending the duration of training, and incorporating functional electromyostimulation to enhance training effectiveness.

About the authors

Roman Kh. Lyukmanov

Research Center of Neurology

Email: xarisovich@gmail.com
ORCID iD: 0000-0002-8671-5861

Cand. Sci. (Med.), Researcher, Head, Brain–Computer Interface Group, Institute of Neurorehabilitation

Russian Federation, Moscow

Mikhail R. Isaev

Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences

Email: shycmympuk@yandex.ru
ORCID iD: 0000-0002-3907-5056

Junior Researcher, Laboratory of Mathematical Neurobiology of Learning Department

Russian Federation, Moscow

Olesya A. Mokienko

Research Center of Neurology; Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences

Email: lesya.md@yandex.ru
ORCID iD: 0000-0002-7826-5135

Cand. Sci. (Med.), Researcher, Brain–Computer Interface Group, Institute of Neurorehabilitation, Research Center of Neurology; Senior Researcher, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences

Russian Federation, Moscow; Moscow

Pavel D. Bobrov

Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences

Author for correspondence.
Email: bobrov_pd@mail.ru
ORCID iD: 0000-0003-2566-1043

Cand. Sci. (Biol.), Head, Laboratory of Mathematical Neurobiology of Learning Department

Russian Federation, Moscow

Ekaterina S. Ikonnikova

Research Center of Neurology

Email: xarisovich@gmail.com
ORCID iD: 0000-0001-6836-4386

Junior Researcher, Brain–Computer Interface Group, Institute of Neurorehabilitation

Russian Federation, Moscow

Anastasiia N. Cherkasova

Research Center of Neurology

Email: lesya.md@yandex.ru
ORCID iD: 0000-0002-7019-474X

Junior Researcher, Brain–Computer Interface Group, Institute of Neurorehabilitation

Russian Federation, Moscow

Natalia A. Suponeva

Research Center of Neurology

Email: xarisovich@gmail.com
ORCID iD: 0000-0003-3956-6362

D. Sci. (Med.), Corresponding Member of the Russian Academy of Sciences, Director, Institute of Neurorehabilitation

Russian Federation, Moscow

References

  1. Monge-Pereira E., Ibañez-Pereda J., Alguacil-Diego I.M. et al. Use of electroencephalography brain-computer interface systems as a rehabilitative approach for upper limb function after a stroke: a systematic review. PM. R. 2017;9(9):918–932. doi: 10.1016/j.pmrj.2017.04.016
  2. Baniqued P.D.E., Stanyer E.C., Awais M. et al. Brain-computer interface robotics for hand rehabilitation after stroke: a systematic review. J. Neuroeng Rehabil. 2021;18 (1):15. doi: 10.1186/s12984-021-00820-8
  3. Fu J., Chen S., Jia J. Sensorimotor rhythm-based brain-computer interfaces for motor tasks used in hand upper extremity rehabilitation after stroke: a systematic review. Brain Sci. 2022;13(1):56. doi: 10.3390/brainsci13010056
  4. Carvalho R., Dias N., Cerqueira J.J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: a systematic review. Physiother. Res. Int. 2019;24(2):e1764. doi: 10.1002/pri.1764
  5. Frolov A.A., Mokienko O., Lyukmanov R. 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. 2017;11:400. doi: 10.3389/fnins.2017.00400
  6. Люкманов Р.Х., Азиатская Г.А., Мокиенко О.А. и др. Интерфейс мозг–компьютер в постинсультной реабилитации: клинико-нейропсихологическое исследование. Журнал неврологии и психиатрии им. С.С. Корсакова. 2018;118(8):43–51. Lyukmanov R.Kh., Aziatskaya GA, Mokienko O.A. et al. Post-stroke rehabilitation training with a brain-computer interface: a clinical and neuropsychological study. Zhurnal Nevrologii i Psikhiatrii imeni S.S. Korsakova. 2018;118(8):43–51. (In Russ.). doi: 10.17116/jnevro201811808143
  7. Soekadar S.R., Kohl S.H., Mihara M., von Lühmann A. Optical brain imaging and its application to neurofeedback. Neuroimage Clin. 2021;30:102577. doi: 10.1016/j.nicl.2021.102577
  8. Mihara M., Hattori N., Hatakenaka M. et al. Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study. Stroke. 2013;44(4):1091–1098. doi: 10.1161/STROKEAHA.111.674507
  9. Исаев М.Р., Бобров П.Д. Влияние выбора стратегии формирования обучающего множества и способа фильтрации на эффективность ИМК, основанного на спектрометрии в ближнем инфракрасном диапазоне. Журнал высшей нервной деятельности им. И.П. Павлова. 2022;72(5):728–738. Isaev M.R., Bob-rov P.D. Effect of session to session learning and filtration method on the performance of BCI based on near infrared spectroscopy. Žurnal vysšej nervnoj deâtelʹnosti im. I.P. Pavlova. 2022;72(5):728–738. (In Russ.) doi: 10.31857/S0044467722050070
  10. Мокиенко О.А., Супонева Н.А. (ред.) Инсульт у взрослых: центральный парез верхней конечности. Клинические рекомендации. М.;2018:64. Mokienko O.A., Suponeva N.A. (eds.) Stroke in adults: central paresis of the upper limb. Clinical guidelines. Moscow; 2018:64. (In Russ.)
  11. Carroll D. A quantitative test of upper extremity function. J. Chronic Dis. 1965;18: 479–491. doi: 10.1016/0021-9681(65)90030-5
  12. Fugl-Meyer A.R., Jääskö L., Leyman I. et al. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand. J. Rehabil. Med. 1975;7(1):13–31.
  13. Xie Y.L., Yang Y.X., Jiang H. et al. Brain-machine interface-based training for improving upper extremity function after stroke: а meta-analysis of randomized controlled trials. Front. Neurosci. 2022;16:949575. doi: 10.3389/fnins.2022.949575
  14. Simon C., Bolton D.A.E., Kennedy N.C. et al. Challenges and opportunities for the future of brain–computer interface in neurorehabilitation. Front. Neurosci. 2021;15:699428. doi: 10.3389/fnins.2021.699428

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2. Fig. 1. The fNIRS BCI and post-priming training flow chart

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3. Fig. 2. Changes in motor scores during rehabilitation and additional fNIRS BCI training.

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Copyright (c) 2023 Lyukmanov R.K., Isaev M.R., Mokienko O.A., Bobrov P.D., Ikonnikova E.S., Cherkasova A.N., Suponeva N.A.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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