Research Progress of Limb Motor Function Rehabilitation after Cerebrovascular Diseases Based on Brain-Computer Interface

doi:10.3969/j.issn.1673-5765.2025.01.008

Abstract

Abstract: Recently, brain-computer interface (BCI) has attracted significant attention in the field of neurorehabilitation. Commonly used experimental paradigms include motor imagery and steady state visual evoked potential, which have been widely applied in the research of motor disorders rehabilitation. This paper reviews the mechanisms of BCI technology in treating motor disorders after cerebrovascular diseases and the progress of electroencephalography-based BCI application in the rehabilitation of upper and lower limb motor disorders. The results show that BCI technology can help patients actively control external devices and stimulate the remodeling of motor cortex neural pathways, and realize the reconstruction of motor functions. It has great potential in the rehabilitation of cerebrovascular diseases. Despite significant advancements in BCI research, there are still many challenges in real-world applications, especially in signal processing, cross-subject research, and user experience. This paper aims to provide references for future research.

Key words: Brain-computer interface; Stroke; Motor imagery; Steady state visual evoked potential

摘要： 近年来，脑机接口在神经康复领域受到了广泛关注。常用的脑机接口实验范式包括运动想象及稳态视觉诱发电位等，已被广泛应用于运动障碍康复研究中。本文对脑机接口技术治疗脑血管病后运动障碍的机制以及基于脑电图的脑机接口在上肢和下肢运动障碍康复中的应用进展进行综述，结果表明，脑机接口技术可以帮助患者主动控制外接设备，并促进运动皮质神经通路重塑，实现运动功能的重建，在脑血管病康复中的应用潜力巨大。尽管脑机接口在研究层面取得了显著进展，但在实际场景中应用仍面临诸多挑战，尤其是在信号处理、跨被试研究和用户体验等方面。本文旨在为未来的研究提供一定的参考。

关键词: 脑机接口; 卒中; 运动想象; 稳态视觉诱发电位

CLC Number:

R74
R318

SANG Zhenhua, XUE Siyang, WEI Chenming, WU Jian. Research Progress of Limb Motor Function Rehabilitation after Cerebrovascular Diseases Based on Brain-Computer Interface[J]. Chinese Journal of Stroke, 2025, 20(1): 63-69.

桑振华, 薛司洋, 魏宸铭, 武剑. 基于脑机接口的脑血管病后肢体运动功能康复研究进展[J]. 中国卒中杂志, 2025, 20(1): 63-69.

References

[1] 高一鹭，王文志. 脑血管病流行病学研究进展[J]. 中华神经科杂志，2015，48（4）：337-340.
GAO Y L，WANG W Z. Process in epidemiological studies of cerebrovascular diseases[J]. Chin J Neurol，2015，48（4）：337-340.
[2] 中华医学会神经病学分会，中华医学会神经病学分会神经康复学组，中华医学会神经病学分会脑血管病学组. 中国脑卒中早期康复治疗指南[J]. 中华神经科杂志，2017，50（6）：405-412.
Chinese Society of Neurology；Neurorehabilitation Society，Chinese Society of Neurology；Cerebrovascular Disease Society，Chinese Society of Neurology. Guidelines for early rehabilitation of stroke in China[J]. Chin J Neurol，2017，50（6）：405-412.
[3] HOCHBERG L R，SERRUYA M D，FRIEHS G M，et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia[J]. Nature，2006，442（7099）：164-171.
[4] LEUTHARDT E C，MILLER K J，SCHALK G，et al. Electrocorticography-based brain-computer interface：the seattle experience[J]. IEEE Trans Neural Syst Rehabil Eng，2006，14（2）：194-198.
[5] PICHIORRI F，MORONE G，PETTI M，et al. Brain-computer interface boosts motor imagery practice during stroke recovery[J]. Ann Neurol，2015，77（5）：851-865.
[6] REMSIK A，YOUNG B，VERMILYEA R，et al. A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke[J]. Expert Rev Med Devices，2016，13（5）：445-454.
[7] HUANG Q，FAN F F，LIU X，et al. NCyborg project—a new stroke rehabilitation pattern based on brain computer interface[J]. Brain Hemorrhages，2021，2（2）：95-96.
[8] HWANG H J，KWON K，IM C H. Neurofeedback-based motor imagery training for brain-computer interface（BCI）[J]. J Neurosci Methods，2009，179（1）：150-156.
[9] BRADBERRY T J，GENTILI R J，CONTRERAS-VIDAL J L. Reconstructing three-dimensional hand movements from noninvasive electroencephalographic signals[J]. J Neurosci，2010，30（9）：3432-3437.
[10] KIM J H，BIEßMANN F，LEE S W. Decoding three-dimensional trajectory of executed and imagined arm movements from electroencephalogram signals[J]. IEEE Trans Neural Syst Rehabil Eng，2015，23（5）：867-876.
[11] CHOWDHURY A，RAZA H，MEENA Y K，et al. Online covariate shift detection-based adaptive brain-computer interface to trigger hand exoskeleton feedback for neuro-rehabilitation[J]. IEEE Trans Cogn Dev Syst，2018，10（4）：1070-1080.
[12] 赵欣，武海霞，陈龙，等. 运动想象结合作业疗法在脑卒中患者康复训练中的研究进展[J]. 中国生物医学工程学报，2021，40（2）：228-236.
ZHAO X，WU H X，CHEN L，et al. Research progress of motor imagery combined with occupational therapy in rehabilitation training of stroke patients[J]. Chinese Journal of Biomedical Engineering，2021，40（2）：228-236.
[13] 刘小燮，毕胜，高小榕，等. 基于运动想象的脑机交互康复训练新技术对脑卒中大脑可塑性影响[J]. 中国康复医学杂志，2013，28（2）：97-102.
LIU X X，BI S，GAO X R，et al. A new technique of rehabilitation training based on motor imagine using brain computer interface-functional electric stimulation system and it’s effect on plasticity of brain of a stroke patient[J]. Chinese Journal of Rehabilitation Medicine，2013，28（2）：97-102.
[14] SINHA A M，NAIR V A，PRABHAKARAN V. Brain-computer interface training with functional electrical stimulation：facilitating changes in interhemispheric functional connectivity and motor outcomes post-stroke[J/OL]. Front Neurosci，2021，15：670953[2024-11-12]. https://doi.org/10.3389/fnins.2021.670953.
[15] BIASIUCCI A，LEEB R，ITURRATE I，et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke[J/OL]. Nat Commun，2018，9（1）：2421[2024-11-12]. https://doi.org/10.1038/s41467-018-04673-z.
[16] KRUEGER J，KRAUTH R，REICHERT C，et al. Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery[J/OL]. Sci Rep，2024，14：18700[2024-11-12]. https://doi.org/10.1038/s41598-024-69037-8.
[17] STEVENS J A，STOYKOV M E P. Using motor imagery in the rehabilitation of hemiparesis[J]. Arch Phys Med Rehabil，2003，84（7）：1090-1092.
[18] JEON Y，NAM C S，KIM Y J，et al. Event-related（De）synchronization（ERD/ERS）during motor imagery tasks：implications for brain-computer interfaces[J]. International Journal of Industrial Ergonomics，2011，41（5）：428-436.
[19] BÖNSTRUP M，SCHULZ R，SCHÖN G，et al. Parietofrontal network upregulation after motor stroke[J/OL]. Neuroimage Clin，2018，18：720-729[2024-11-12]. https://doi.org/10.1016/j.nicl.2018.03.006.
[20] CHEN X G，CHEN Z K，GAO S K，et al. Brain-computer interface based on intermodulation frequency[J/OL]//2022 14th Biomedical Engineer ing International Conference（BMEiCON）. J Neural Eng，2013，10（6）：066009[2024-11-12]. https://doi.org/10.1088/1741-2560/10/6/066009.
[21] GUO N，WANG X J，DUANMU D H，et al. SSVEP-based brain-computer interface controlled soft robotic glove for post-stroke hand function rehabilitation[J/OL]. IEEE Trans Neural Syst Rehabil Eng，2022，30：1737-1744[2024-11-12]. https://doi.org/10.1109/TNSRE.2022.3185262.
[22] LI X D，WANG J L，CAO X，et al. Soft robotic glove with alpha band brain-computer interface for post-stroke hand function rehabilitation[C/OL]//2022 14th Biomedical Engineer ing International Conference（BMEiCON）. New York：IEEE，2022：1-5[2024-11-12]. https://doi.org/10.1109/BMEiCON56653.2022.10012103.
[23] MCGEADY C，VUČKOVIĆ A，PUTHUSSERYPADY S. A hybrid MI-SSVEP based brain computer interface for potential upper limb neurorehabilitation：a pilot study[C/OL]//2019 7th International Winter Conference on Brain-Computer Interface（BCI）. New York：IEEE，2019：106-111[2024-11-12]. https://doi.org/10.1109/iww-bci.2019.8737333.
[24] CHI X Y，WAN C X，WANG C Y，et al. A novel hybrid brain-computer interface combining motor imagery and intermodulation steady-state visual evoked potential[J/OL]. IEEE Trans Neural Syst Rehabil Eng，2022，30：1525-1535[2024-11-12]. https://doi.org/10.1109/TNSRE.2022.3179971.
[25] SU J Q，WANG J X，WANG W Q，et al. An adaptive hybrid brain-computer interface for hand function rehabilitation of stroke patients[J/OL]. IEEE Trans Neural Syst Rehabil Eng，2024，32：2950-2960[2024-11-12]. https://doi.org/10.1109/TNSRE.2024.3431025.
[26] ACHANCCARCY D，IZUMI S I，HAYASHIBE M. Visual-electrotactile stimulation feedback to improve immersive brain-computer interface based on hand motor imagery[J/OL]. Comput Intell Neurosci，2021，2021：8832686[2024-11-12]. https://doi.org/10.1155/2021/8832686.
[27] BALASUBRAMANIAN S，GARCIA-COSSIO E，BIRBAUMER N，et al. Is EMG a viable alternative to BCI for detecting movement intention in severe stroke？[J]. IEEE Trans Biomed Eng，2018，65（12）：2790-2797.
[28] TIDONI E，ABU-ALQUMSAN M，LEONARDIS D，et al. Local and remote cooperation with virtual and robotic agents：a P300 BCI study in healthy and people living with spinal cord injury[J]. IEEE Trans Neural Syst Rehabil Eng，2017，25（9）：1622-1632.
[29] BUNDY D T，SOUDERS L，BARANYAI K，et al. Contralesional brain-computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors[J]. Stroke，2017，48（7）：1908-1915.
[30] JOHNSON N N，CAREY J，EDELMAN B J，et al. Combined rTMS and virtual reality brain-computer interface training for motor recovery after stroke[J/OL]. J Neural Eng，2018，15（1）：016009[2024-11-12]. https://doi.org/10.1088/1741-2552/aa8ce3.
[31] CANTILLO-NEGRETE J，CARINO-ESCOBAR R I，CARRILLO-MORA P，et al. Brain-computer interface coupled to a robotic hand orthosis for stroke patients’ neurorehabilitation：a crossover feasibility study[J/OL]. Front Hum Neurosci，2021，15：656975[2024-11-12]. https://doi.org/10.3389/fnhum.2021.656975.
[32] MANSOUR S，GILES J，ANG K K，et al. Exploring the ability of stroke survivors in using the contralesional hemisphere to control a brain-computer interface[J/OL]. Sci Rep，2022，12（1）：16223[2024-11-12]. https://doi.org/10.1038/s41598-022-20345-x.
[33] CHUNG E，KIM J H，PARK D S，et al. Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients：a pilot randomized controlled trial[J]. J Phys Ther Sci，2015，27（3）：559-562.
[34] YUAN Z W，PENG Y，WANG L S，et al. Effect of BCI-controlled pedaling training system with multiple modalities of feedback on motor and cognitive function rehabilitation of early subacute stroke patients[J/OL]. IEEE Trans Neural Syst Rehabil Eng，2021，29：2569-2577[2024-11-12]. https://doi.org/10.1109/TNSRE.2021.3132944.
[35] 唐欢，苏彬，车培，等. 脑机接口联合末端驱动型下肢机器人对脑卒中患者平衡及步行功能的影响[J]. 中国康复医学杂志，2024，39（6）：791-797.
TANG H，SU B，CHE P，et al. Effects of a brain-computer interface combined with an end-driven lower limb robot on the balance and walking function in stroke patients[J]. Chinese Journal of Rehabilitation Medicine，2024，39（6）：791-797.
[36] LUO X. Effects of motor imagery-based brain-computer interface-controlled electrical stimulation on lower limb function in hemiplegic patients in the acute phase of stroke：a randomized controlled study[J/OL]. Front Neurol，2024，15：1394424[2024-11-12]. https://doi.org/10.3389/fneur.2024.1394424.
[37] HE H，WU D R. Transfer learning for brain-computer interfaces：a euclidean space data alignment approach[J]. IEEE Trans Biomed Eng，2020，67（2）：399-410.
[38] 桑振华，魏宸铭，王准，等. 人工智能技术在脑卒中筛查领域的研究进展[J]. 中华脑血管病杂志（电子版），2022，16（4）：225-229.
SANG Z H，WEI C M，WANG Z，et al. Progress of artificial intelligence in the field of stroke screening[J]. Chinese Journal of Cerebrovascular Diseases（Electronic Edition），2022，16（4）：225-229.
[39] KINDERMANS P J，SCHREUDER M，SCHRAUWEN B，et al. True zero-training brain-computer interfacing—an online study[J/OL]. PLoS One，2014，9（7）：e102504[2024-11-12]. https://doi.org/10.1371/journal.pone.0102504.
[40] SELLERS E W，VAUGHAN T M，WOLPAW J R. A brain-computer interface for long-term independent home use[J]. Amyotroph Lateral Scler，2010，11（5）：449-455.