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下肢运动想象脑机接口的研究进展及康复应用

Research progress and rehabilitation application of brain-computer interface based on lower-limb motor imagery

作者: 王维振  曲皓  雷杨浩  尹帅  王晶 
单位:西安交通大学机械工程学院机器人与智能系统研究所(西安 710049)<br />西安交通大学NRR-神经康复机器人研究院(西安 710049)<br />通信作者:王晶。E-mail: wangpele@gmail.com <p>&nbsp;</p>
关键词: 下肢;运动想象;脑机接口;功能康复 
分类号:R318.04
出版年·卷·期(页码):2023·42·2(204-211)
摘要:

运动想象脑机接口(brain computer interface based on motor imagery, MI-BCI)是一种完全不依赖于外周神经和肌肉就可实现与外界环境交互的前沿技术,该技术能够充分利用患者的运动意图,并触发大脑的神经可塑性,以实现脑卒中患者的运动功能康复。针对下肢MI-BCI系统识别率低、实验环境要求高等问题,研究人员从范式、算法和应用等方面不断地探索下肢MI-BCI技术的新方向,并通过长期的临床研究验证了下肢MI-BCI技术在脑卒中患者下肢功能康复领域的优越性。本文对近年来下肢MI-BCI的国内外研究进展进行综述,归纳下肢MI-BCI在临床康复领域的应用现状,最后分析其面临的挑战和发展趋势,以期推动下肢MI-BCI的快速发展,并为下肢功能康复提供新思路。

Brain computer interface based on motor imagery (MI-BCI) is a cutting-edge technology that can communicate with the external environment completely without relying on peripheral nerves and muscles, which can fully utilize the patient's motor intentions and trigger the neuro-plasticity of the brain to achieve function rehabilitation. In view of the low recognition rate and strict experimental environment of lower-limb MI-BCI, researchers explored the new direction of lower-limb MI-BCI from the aspects of paradigm, algorithm and application, and have verified the superiority of lower limb MI-BCI technology in the field of lower limb functional rehabilitation of stroke patients through long-term clinical trials. This paper reviewed the research progress and summarized the clinical application status of lower-limb MI-BCI. Finally, the challenges and development trends of lower-limb MI-BCI were analyzed to provide new ideas for lower limbs rehabilitation.

参考文献:

[1] Katan M, Luft A. Global burden of stroke [J]. Seminars in Neurology, 2018, 38(2): 208-211.
[2] 常琪, 单新颖, 毕胜. 基于脑电图的脑机接口在肢体康复中的应用进展 [J].中国康复医学杂志, 2019, 34(12): 1488-1492.
[3] Mane R, Chouhan T, Guan CT. BCI for stroke rehabilitation: motor and beyond [J]. Journal of Neural Engineering, 2020, 17(4): 041001.
[4] Lebedev MA, Nicolelis MAL. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation [J]. Physiological Reviews, 2017, 97(2): 767-837.
[5] 王俊,廖麟荣,杨振辉, 等. 运动想象结合下肢康复机器人训练对脑卒中患者步行障碍的影响[J]. 中国康复医学杂志, 2015, 30(6): 542-546.
Wang J, Liao L, Yang Z, et al. Effects of robotic-assisted gait training with motor imagery on gait impairments in patients with stroke [J]. Chinese Journal of Rehabilitation Medicine, 2015, 30(6): 542-546.
[6] 方文垚, 刘昊, 杨柳, 等. 脑机接口技术在脑卒中偏瘫患者下肢运动功能康复治疗中的应用 [J].山东医药, 2018, 58(10): 66-68.
[7] 张瑞萍, 姜雪婷, 张杨, 等. 脑机接口康复机器人在脑卒中急性期患者下肢运动功能康复中的应用研究 [J].中国实用医药, 2021, 16(34): 196-198.
[8] Yuan Z, Peng Y, Wang L, 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]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 2569-2577.
[9] Takakusaki K. Functional neuroanatomy for posture and gait control [J]. Journal of Movement Disorders, 2017, 10(1): 1-17.
[10] 邵南平, 向春生, 唐岚, 等. 基于AdaBoost BP神经网络的无参考图像质量评价的研究 [J].国外电子测量技术, 2017, 36(11): 108-113.
Shao NP, Xiang CS, Tang L, et al. Blind image quality assessment based on AdaBoost BP neural network [J].Foreign Electronic Measurement Technology, 2017, 36(11): 108-113.
[11] 李鹏海, 王丽余, 刘瀛涛, 等. 下肢运动想象和运动执行的EEG节律特性研究 [J].仪器仪表学报, 2018, 39(3): 207-214.
Li P, Wang L, Liu Y, et al. Study on EEG rhythm features of lower Iimb motor imagery and motor performance [J].Chinese Journal of Scientific Instrument, 2018, 39(3): 207-214.
[12] Cassidy JM, Cramer SC. Spontaneous and therapeutic-induced mechanisms of functional recovery after stroke [J]. Translational Stroke Research, 2017, 8(1): 33-46.
[13] Keysers C, Perrett DI. Demystifying social cognition: a Hebbian perspective [J]. Trends in Cognitive Sciences, 2004, 8(11): 501-507.
[14] Kulasingham JP, Brodbeck C, Khan S, et al. Bilaterally reduced rolandic beta band activity in minor stroke patients [J]. Frontiers in Neurology, 2022, 13: 819603.?
[15] Barios J, Ezquerro S, Bertomeu-Motos A, et al. Movement-related EEG oscillations of contralesional hemisphere discloses compensation mechanisms of severely affected motor chronic stroke patients [J]. International Journal of Neural Systems, 2021, 31(12): 2150053.
[16] Flesher SN, Downey JE, Weiss JM, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control [J]. Science, 2021, 372(6544): 831-836.
[17] Zhang JJQ, Fong KNK, Welage N, et al. The activation of the mirror neuron system during action observation and action execution with mirror visual feedback in stroke: a systematic review [J]. Neural Plasticity, 2018, 2018: 2321045.
[18] Li L, Wang J, Xu G, et al. The study of object-oriented motor imagery based on EEG suppression [J]. PLoS One, 2015, 10(12): e0144256.
[19] Yu Z, Li L, Song J, et al. The study of visual-auditory interactions on lower limb motor imagery [J]. Frontiers in Neuroscience, 2018, 12: 509.
[20] Ferrero L, Ortiz M, Quiles V, et al. Improving motor imagery of gait on a brain-computer interface by means of virtual reality: a case of study [J]. IEEE Access, 2021, 9: 49121-49130.
[21] Duan SC, Wang C, Li MY, et al. Haptic and visual enhance-based motor imagery BCI for rehabilitation lower-limb exoskeleton [M]// 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). Dali: IEEE Press, 2019: 2025-2030.
[22] Yu GJ, Wang JH, Chen WH, et al. EEG-based brain-controlled lower extremity exoskeleton rehabilitation robot [C]// 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Ningbo: IEEE Press, 2017: 763-767.
[23] Choi J, Kim KT, Jeong JH, et al. Developing a motor imagery-based real-time asynchronous hybrid BCI controller for a lower-limb exoskeleton [J]. Sensors, 2020, 20(24): 7309.
[24] Ren SX, Wang WQ, Hou ZG, et al. Enhanced motor imagery based brain- computer interface via FES and VR for lower limbs [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(8): 1846-1855.
[25] Goodwin GM, McCloskey DI, Matthews PB. Proprioceptive illusions induced by muscle vibration: contribution by muscle-spindles to perception[J]. Science, 1972, 175(4028): 1382-1384.
[26] Tapin A, Duclos NC, Jamal K, et al. Perception of gait motion during multiple lower-limb vibrations in young healthy individuals: a pilot study [J]. Experimental Brain Research, 2021, 239(11): 3267-3276.
[27] Naito E. Sensing limb movements in the motor cortex: How humans sense limb movement [J]. Neuroscientist, 2004, 10(1): 73-82.
[28] Somadder R, Saha DK. Frequency domain CSP for foot motor imagery classification using SVM for BCI application [C]// 2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) . Langkawi Island, Malaysia: IEEE Press, 2021: 30-34.
[29] Gu L, Yu Z, Ma T, et al. Random matrix theory for analysing the brain functional network in lower limb motor imagery[C]// 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Montreal, QC, Canada: IEEE Press, 2020: 506-509.
[30] Gu LY, Yu ZH, Ma T, et al. EEG-based classification of lower limb motor imagery with brain network analysis [J]. Neuroscience, 2020, 436: 93-109.
[31] 李昭阳, 龚安民, 伏云发. 基于EEG脑网络下肢动作视觉想象识别研究 [J].南京大学学报(自然科学), 2020, 56(4): 570-580.
?Li Z, Gong AM, Fu YF. Identification of visual imagery of movements involving the lower limbs based on EEG network [J].Journal of Nanjing University (Natural Science), 2020, 56(4): 570-580.
[32] Xu MP, He F, Jung TP, et al. Current challenges for the practical application of electroencephalography-based brain-computer interfaces [J]. Engineering, 2021, 7(12): 1710-1712.
[33] Bhaduri S, Khasnobish A, Bose R, et al. Classification of lower limb motor imagery using K nearest neighbor and naive-bayesian classifier[C]// 2016 3rd IEEE International Conference on Recent Advances in Information Technology (RAIT). Dhanbad, India: IEEE Press, 2016: 499-504.
[34] Hsu WC, Lin LF, Chou CW, et al. EEG classification of imaginary lower limb stepping movements based on fuzzy support vector machine with kernel-induced membership function [J]. International Journal of Fuzzy Systems, 2017, 19(2): 566-579.
[35] 李嘉莹, 赵丽, 边琰, 等. 基于LDA和KNN的下肢运动想象脑电信号分类研究 [J].国外电子测量技术, 2021, 40(1): 9-14.
Li JY, Zhao L, Bian Y, et al. Classification of lower limb motor imagination signals based on LDA and KNN [J].Foreign Electronic Measurement Technology, 2021, 40(1): 9-14.
[36] Jeong JH, Kim DJ, Kim H, et al. Hybrid zero-training BCI based on convolutional neural network for lower-limb motor-imagery[C]// 2021 9th IEEE International Winter Conference on Brain-Computer Interface (BCI). Gangwon, Korea (South): IEEE Press, 2021: 1-4.
[37] Zhang X, Yao L, Wang X, et al. A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers [J]. Journal of Neural Engineering, 2021, 18: 031002.?
[38] Kumar VK, Chakrapani M, Kedambadi R. Motor imagery training on muscle strength and gait performance in ambulant stroke subjects-a randomized clinical trial [J]. Journal of Clinical and Diagnostic Research: JCDR, 2016, 10(3): YC01-YC04.
[39] 徐博然, 陈惠君. 步态运动想象疗法对脑卒中偏瘫患者步行功能的影响[J].解放军护理杂志, 2019, 36(5): 16-20.
Xu BR, Chen HJ. Effect of gait imagery therapy on walking function in hemiplegic patients after stroke[J].Nursing Journal of Chinese People's Liberation Army, 2019, 36(5): 16-20.
[40] Yin XJ, Wang YJ, Ding XD, et al. Effects of motor imagery training on lower limb motor function of patients with chronic stroke: a pilot single-blind randomized controlled trial[J]. International Journal of Nursing Practice, 2022, 28(3): e12933.
[41] da Silva ST, Borges L, Santiago LMD, et al. Motor imagery for gait rehabilitation after stroke [J]. Stroke, 2021, 52(6): e272-e273.
[42] Bobrova EV, Reshetnikova VV, Frolov AA, et al. Use of imaginary lower limb movements to control brain–computer interface systems [J]. Neuroscience and Behavioral Physiology, 2020, 50(5): 585-592.

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