Evaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems_Journal of Biomedical Engineering

Authors：

PAN He ^1,2 , DING Peng ^1,2 , WANG Fan ^1,2 , LI Tianwen ^2,3 , ZHAO Lei ^2,3 , NAN Wenya ⁴ , GONG Anmin ⁵ ,  FU Yunfa ^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;
2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China;
3. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China;
4. Department of Psychology, School of Education, Shanghai Normal University, Shanghai 200234, P. R. China;
5. School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an 710000, P. R. China;

Corresponding author：

FU Yunfa, Email: fyf@ynu.edu.cn

Keywords：

Motor imagery based brain-computer interface; Active rehabilitation training; Evaluation method for rehabilitation efficacy; Motor dysfunction; Brain-computer interface

DOI：

10.7507/1001-5515.202407097

Video：

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Abstract Full text Figures/Tables Video References Cited by

The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.

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6.	Lin M, Huang J, Fu J, et al. A VR-based motor imagery training system with EMG-based real-time feedback for post-stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2022, 31: 1-10.
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9.	Zhao L J, Jiang L H, Zhang H, et al. Effects of motor imagery training for lower limb dysfunction in patients with stroke: A systematic review and meta-analysis of randomized controlled trials. Am J Phys Med Rehabil, 2023, 102(5): 409-418.
10.	Lee J, Kim D Y, Lee S H, et al. End-effector lower limb robot-assisted gait training effects in subacute stroke patients: A randomized controlled pilot trial. Medicine, 2023, 102(42): e35568.
11.	Su D, Hu Z, Wu J, et al. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. Front Neurorobot, 2023, 17: 1186175.
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16.	Chen S, Shu X, Wang H, et al. The differences between motor attempt and motor imagery in brain-computer interface accuracy and event-related desynchronization of patients with hemiplegia. Front Neurorobot, 2021, 15: 706630.
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18.	Ang K K, Chua K S G, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci, 2015, 46(4): 310-320.
19.	Brunner I, Lundquist C B, Pedersen A R, et al. Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial. J Neuroeng Rehabil, 2024, 21(1): 10.
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23.	Yozbatiran N, Der-Yeghiaian L, Cramer S C. A standardized approach to performing the action research arm test. Neurorehabil Neural Repair, 2008, 22(1): 78-90.
24.	Wang Z, Liu Y, Huang S, et al. EEG characteristic comparison of motor imagery between supernumerary and inherent limb: Sixth-finger MI enhances the ERD pattern and classification performance. IEEE J Biomed Health Inform, 2024, 28(12): 7078-7089.
25.	Boren S B, Savitz S I, Ellmore T M, et al. Longitudinal resting-state functional magnetic resonance imaging study: A seed-based connectivity biomarker in patients with ischemic and intracerebral hemorrhage stroke. Brain Connect, 2023, 13(8): 498-507.
26.	Reddy N A, Zvolanek K M, Moia S, et al. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. Imaging Neurosci, 2024, 2: 1-30.
27.	Ding W, Chen J, Liu J, et al. Development and validation of the Health Education Adherence Scale for Stroke Patients: a cross-sectional study. BMC Neurol, 2022, 22(1): 69.
28.	Ren W, Wang M, Wang Q, et al. Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study. J Affect Disord, 2024, 350: 468-475.
29.	Schuber A A, Schmidt S, Hombach S, et al. The effects of exercise therapy feedback on subjective treatment outcome and patient satisfaction: study protocol for a mono-centric, randomized, controlled trial in orthopedic rehabilitation (FeedYou). BMC Sports Sci Med Rehabil, 2023, 15(1): 17.
30.	Rochester C L, Alison J A, Carlin B, et al. Pulmonary rehabilitation for adults with chronic respiratory disease: an official American Thoracic Society clinical practice guideline. Am J Respir Crit Care Med, 2023, 208(4): e7-e26.
31.	Zemková E. Strength and power-related measures in assessing core muscle performance in sport and rehabilitation. Front Physiol, 2022, 13: 861582.
32.	Acar S, Aljumaa H, Şevik K, et al. The intrarater and interrater reliability and validity of universal goniometer, digital inclinometer, and smartphone application measuring range of motion in patients with total knee arthroplasty. Indian J Orthop, 2024, 58(6): 732-739.
33.	Kasmi S, Sariati D, Hammami R, et al. The effects of different rehabilitation training modalities on isokinetic muscle function and male athletes’ psychological status after anterior cruciate ligament reconstructions. BMC Sports Sci Med Rehabil, 2023, 15(1): 43.
34.	Allison B Z, Dunne S, Leeb R, 等. 面向实用的脑-机接口: 缩小研究与实际应用之间的差距. 伏云发, 龚安民, 陈超, 等译. 北京: 电子工业出版社, 2022: 45-47.
35.	Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems. Front Hum Neurosci, 2024, 18: 1429130.
36.	人工智能医疗器械创新合作平台. 脑机接口技术在医疗健康领域应用白皮书. 北京: 人工智能医疗器械创新合作平台, 2023.
37.	Liu X, Zhang W, Li W, et al. Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial. BMC neurol, 2023, 23(1): 136.
38.	Su J, Wang J, Wang W, et al. An adaptive hybrid brain computer interface for hand function rehabilitation of stroke patients. IEEE Trans Neural Syst Rehabil Eng, 2024(32): 2950-2960.

1. Graimann B, Allison B Z, Pfurtscheller G. 脑-机接口-革命性的人机交互. 伏云发, 郭衍龙, 张夏冰, 等译.北京: 国防工业出版社, 2020.
2. Chen Y, Wang F, Li T, et al. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces. Front Neurosci, 2024, 18: 1449208.
3. Ramsey N F, Millán J R. 脑-计算机接口. 伏云发, 王帆, 丁鹏, 等译. 北京: 国防工业出版社, 2023.
4. World Stroke Organization. WSO Global Stroke Fact Sheet 2022. Geneva: World Stroke Organization, 2022.
5. Li W, Luo Z, Jiang J, et al. The effects of exercise intervention on cognition and motor function in stroke survivors: a systematic review and meta-analysis. Neuro Sci, 2023, 44(6): 1891-1903.
6. Lin M, Huang J, Fu J, et al. A VR-based motor imagery training system with EMG-based real-time feedback for post-stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng, 2022, 31: 1-10.
7. Lima J P S, Silva L A, Delisle-Rodriguez D, et al. Unraveling transformative effects after tDCS and BCI intervention in chronic post-stroke patient rehabilitation—An alternative treatment design study. Sensors, 2023, 23(23): 9302.
8. Su H, Wang S, Huang M, et al. VR and exoskeleton assisted lower limb rehabilitation based on motor imagery BCI// 2023 IEEE International Biomedical Instrumentation and Technology Conference (IBITeC). Yogyakarta: IEEE, 2023: 74-79.
9. Zhao L J, Jiang L H, Zhang H, et al. Effects of motor imagery training for lower limb dysfunction in patients with stroke: A systematic review and meta-analysis of randomized controlled trials. Am J Phys Med Rehabil, 2023, 102(5): 409-418.
10. Lee J, Kim D Y, Lee S H, et al. End-effector lower limb robot-assisted gait training effects in subacute stroke patients: A randomized controlled pilot trial. Medicine, 2023, 102(42): e35568.
11. Su D, Hu Z, Wu J, et al. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. Front Neurorobot, 2023, 17: 1186175.
12. Ferrero L, Quiles V, Ortiz M, et al. Brain-computer interface enhanced by virtual reality training for controlling a lower limb exoskeleton. Iscience, 2023, 26(5): 106675.
13. 贾杰. “中枢-外周-中枢”闭环康复——脑卒中后手功能康复新理念. 中国康复医学杂志, 2016, 31(11): 1180-1182.
14. Jia J. Exploration on neurobiological mechanisms of the central–peripheral–central closed-loop rehabilitation. Front Cell Neurosci, 2022, 16: 982881.
15. Cervera M A, Soekadar S R, Ushiba J, et al. Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis. Ann Clin Transl Neurol, 2018, 5(5): 651-663.
16. Chen S, Shu X, Wang H, et al. The differences between motor attempt and motor imagery in brain-computer interface accuracy and event-related desynchronization of patients with hemiplegia. Front Neurorobot, 2021, 15: 706630.
17. Quiroga A, del Valle D V, Pilz M, et al. Performance comparison of different classifiers to detect motor intention in EEG-based BCI// Latin American Conference on Biomedical Engineering. Cham: Springer Nature Switzerland, 2022: 90-101.
18. Ang K K, Chua K S G, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci, 2015, 46(4): 310-320.
19. Brunner I, Lundquist C B, Pedersen A R, et al. Brain computer interface training with motor imagery and functional electrical stimulation for patients with severe upper limb paresis after stroke: a randomized controlled pilot trial. J Neuroeng Rehabil, 2024, 21(1): 10.
20. Ma Z Z, Wu J J, Cao Z, et al. Motor imagery-based brain–computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients. J Neuroeng Rehabil, 2024, 21(1): 91.
21. Sebastián-Romagosa M, Cho W, Ortner R, et al. Brain–computer interface treatment for gait rehabilitation in stroke patients. Front Neurosci, 2023, 17: 1256077.
22. 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. Neurorehabil Neural Repair, 2002, 16(3): 232-240.
23. Yozbatiran N, Der-Yeghiaian L, Cramer S C. A standardized approach to performing the action research arm test. Neurorehabil Neural Repair, 2008, 22(1): 78-90.
24. Wang Z, Liu Y, Huang S, et al. EEG characteristic comparison of motor imagery between supernumerary and inherent limb: Sixth-finger MI enhances the ERD pattern and classification performance. IEEE J Biomed Health Inform, 2024, 28(12): 7078-7089.
25. Boren S B, Savitz S I, Ellmore T M, et al. Longitudinal resting-state functional magnetic resonance imaging study: A seed-based connectivity biomarker in patients with ischemic and intracerebral hemorrhage stroke. Brain Connect, 2023, 13(8): 498-507.
26. Reddy N A, Zvolanek K M, Moia S, et al. Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA. Imaging Neurosci, 2024, 2: 1-30.
27. Ding W, Chen J, Liu J, et al. Development and validation of the Health Education Adherence Scale for Stroke Patients: a cross-sectional study. BMC Neurol, 2022, 22(1): 69.
28. Ren W, Wang M, Wang Q, et al. Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study. J Affect Disord, 2024, 350: 468-475.
29. Schuber A A, Schmidt S, Hombach S, et al. The effects of exercise therapy feedback on subjective treatment outcome and patient satisfaction: study protocol for a mono-centric, randomized, controlled trial in orthopedic rehabilitation (FeedYou). BMC Sports Sci Med Rehabil, 2023, 15(1): 17.
30. Rochester C L, Alison J A, Carlin B, et al. Pulmonary rehabilitation for adults with chronic respiratory disease: an official American Thoracic Society clinical practice guideline. Am J Respir Crit Care Med, 2023, 208(4): e7-e26.
31. Zemková E. Strength and power-related measures in assessing core muscle performance in sport and rehabilitation. Front Physiol, 2022, 13: 861582.
32. Acar S, Aljumaa H, Şevik K, et al. The intrarater and interrater reliability and validity of universal goniometer, digital inclinometer, and smartphone application measuring range of motion in patients with total knee arthroplasty. Indian J Orthop, 2024, 58(6): 732-739.
33. Kasmi S, Sariati D, Hammami R, et al. The effects of different rehabilitation training modalities on isokinetic muscle function and male athletes’ psychological status after anterior cruciate ligament reconstructions. BMC Sports Sci Med Rehabil, 2023, 15(1): 43.
34. Allison B Z, Dunne S, Leeb R, 等. 面向实用的脑-机接口: 缩小研究与实际应用之间的差距. 伏云发, 龚安民, 陈超, 等译. 北京: 电子工业出版社, 2022: 45-47.
35. Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction and usage of online BCI systems. Front Hum Neurosci, 2024, 18: 1429130.
36. 人工智能医疗器械创新合作平台. 脑机接口技术在医疗健康领域应用白皮书. 北京: 人工智能医疗器械创新合作平台, 2023.
37. Liu X, Zhang W, Li W, et al. Effects of motor imagery based brain-computer interface on upper limb function and attention in stroke patients with hemiplegia: a randomized controlled trial. BMC neurol, 2023, 23(1): 136.
38. Su J, Wang J, Wang W, et al. An adaptive hybrid brain computer interface for hand function rehabilitation of stroke patients. IEEE Trans Neural Syst Rehabil Eng, 2024(32): 2950-2960.

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Latest ArticlesEvaluation methods for the rehabilitation efficacy of bidirectional closed-loop motor imagery brain-computer interface active rehabilitation training systems

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