脑机接口技术在慢性脑卒中患者上肢康复中的研究进展

doi:10.3969/j.issn.1006-9771.2021.03.005

摘要/Abstract

摘要：

脑机接口(BCI)技术可以激活神经系统可塑性。记录大脑活动的技术包括侵入性和非侵入性方法，后者具有安全、便携和低成本的优点，但容易产生信号污染。在慢性脑卒中患者上肢康复中，目前通常采用脑电图结合机械臂的方法。临床上，BCI多与其他康复方法联合应用，可提高康复效果。未来需要提高BCI运动意图解码的准确性，对患者进行分层和量身定制治疗，开发混合式和便携式BCI系统。

关键词: 慢性脑卒中, 脑机接口, 上肢, 运动功能, 康复, 综述

Abstract:

Brain-computer interface (BCI) technology can activate the plasticity of the nervous system and induce the reconnection of nervous system information pathways. Techniques for recording brain activity include invasive methods and non-invasive methods. Non-invasive methods are safer, more portable and cheaper, but are prone to signal pollution. For upper limbs rehabilitation in patients with severe chronic stroke, BCI of electroencephalogram and robotic arm is usually used, almost combined with other rehabilitation approaches, and seems to improve the effectiveness. It is necessary to improve the accuracy of BCI motion intention decoding, carry out hierarchical and customized treatment for patients, develop hybrid and portable BCI system.

Key words: chronic stroke, brain-computer interface, upper limb, motor function, rehabilitation, review

中图分类号:

R743.3

何艳,张通. 脑机接口技术在慢性脑卒中患者上肢康复中的研究进展[J]. 《中国康复理论与实践》, 2021, 27(3): 277-281.

Yan HE,Tong ZHANG. Advance in Application of Brain-computer Interface for Upper Limb Rehabilitation for Patients with Chronic Stroke (review)[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(3): 277-281.

参考文献 53

1	Curado M R, Cossio E G, Broetz D, et al. Residual upper arm motor function primes innervation of paretic forearm muscles in chronic stroke after brain-machine interface (BMI) training [J]. PLoS One, 2015, 10(10): e0140161.
2	Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation [J]. Lancet, 2011, 377(9778): 1693-1702.
3	López-Larraz E, Sarasola-Sanz A, Irastorza-Landa N, et al. Brain-machine interfaces for rehabilitation in stroke: a review [J]. NeuroRehabilitation, 2018, 43(1): 77-97.
4	Coscia M, Maximilian J W, Chaudary U, et al. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke [J]. Brain, 2019, 142(8): 2182-2197.
5	Veerbeek J M, Langbroek-Amersfoort A C, van Wegen E E H, et al. Effects of robot-assisted therapy for the upper limb after stroke [J]. Neurorehabil Neural Repair, 2017, 31(2): 107-121.
6	Pollock A, Farmer S E, Brady M C, et al. Interventions for improving upper limb function after stroke [J]. Cochrane Database Syst Rev, 2014(11): CD010820.
7	Raffin E, Hummel F C. Restoring motor functions after stroke: multiple approaches and opportunities [J]. Neuroscientist, 2018, 24(4): 400-416.
8	Wu X, Guarino P, Lo A C, et al. Long-term effect-iveness of intensive therapy in chronic stroke [J]. Neurorehabil Neural Repair, 2016, 30(6): 583-590.
9	Bell J A, Wolke M L, Ortez R C, et al. Training intensity affects motor rehabilitation efficacy following unilateral ischemic insult of the sensorimotor cortex in C57BL/6 mice [J]. Neurorehabil Neural Repair, 2015, 29(6): 590-598.
10	Byblow W D, Stinear C M, Barber P A, et al. Proportional recovery after stroke depends on corticomotor integrity [J]. Ann Neurol, 2015, 78(6): 848-859.
11	Buch E R, Rizk S, Nicolo O, et al. Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging [J]. Neurology, 2016, 86(20): 1924-1925.
12	Guggisberg A G, Nicolo P, Cohen L G, et al. Longitudinal structural and functional differences between proportional and poor recovery after stroke [J]. Neurorehabil Neural Repair, 2017, 31(12): 1029-1041.
13	Winters C, van Wegen E E, Daffertshofer A, et al. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke [J]. Neurorehabil Neural Repair, 2015, 29(7): 614-622.
14	Chen L C, Sandmann P, Thorne J D, et al. Association of concurrent sNIRS and EEG signatures in response to auditory and visual stimuli [J]. Brain Topogr, 2015, 28(5): 710-725.
15	Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control [J]. Clin Neurophysiol, 2002, 113(6): 767-791.
16	Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation [J]. Nat Rev Neurol, 2016, 12(9): 513-525.
17	Lebedev M A, Nicolelis M A. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation [J]. Physiol Rev, 2017, 97(2): 767-837.
18	Bouton C E, Shaikhouni A, Anneta N V, et al. Restoring cortical control of functional movement in a human with quadriplegia [J]. Nature, 2016, 533(7602): 247-250.
19	Mestais C S, Charvet G, Sauter-Starace F, et al. WIMAGINE: Wireless 64-channel ECoG recording inplant for long term clinical applications [J]. IEEE Trans Neural Syst Rehabil Eng, 2015, 23(1): 10-21.
20	Suner S, Fellows M R, Vargas-Irwin C, et al. Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex [J]. IEEE Trans Neural Syst Rehabil Eng, 2005, 13(4): 524-541.
21	Borton D A, Yin M, Aceros J, et al. An implantable wireless neural interface for recording cortical circuit dynamics in moving primates [J]. J Neural Eng, 2013, 10(2): 026010.
22	琚芬,赵晨光,袁华. 脑机接口在康复医学中的应用进展[J]. 中国康复, 2017, 32(6): 508-511.
	Ju F, Zhao C G, Yuan H, et al. Application progress of brain-computer interface in rehabilitation medicine [J]. Chin J Rehabil, 2017, 32(6): 508-511.
23	Li X, Samuel O W, Zhang X, et al. A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees [J]. J Neuroeng Rehabil, 2017, 14: 2.
24	Berger A, Horst F, Müller S, et al. Current state and future prospects of EEG and fNIRS in robot-assisted gait rehabilitation: a brief review [J]. Front Hum Neurosci, 2019, 13: 172.
25	Sburlea A I, Montesano L, de la Cuerda R C, et al. Detecting intention to walk in stroke patients from pre-movement EEG correlates [J]. J Neuroeng Rehabil, 2015, 12: 113.
26	Ofner P, Schwarz A, Pereira J. Upper limb movements can be decoded from the time-domain of low-frequency EEG [J]. PLoS One, 2017, 12(8): e0182578.
27	Shiman F, López-Larraz E, Sarasola-Sanz A, et al. Classification of different reaching movements from the same limb using EEG [J]. J Neural Eng, 2017, 14(4): 046018.
28	López-Larraz E, Ibáñez J, Trincado-Alonso F, et al. Comparing recalibration strategies for electroencephalography-based decoders of movement intention in neurological patients with motor disability [J]. Int J Neural Syst, 2018, 28(7): 1750060.
29	Insausti-Delgado A, López-Larraz E, Bibián C, et al. Influence of trans-spinal magnetic stimulation in electrophysiological recording for closed-loop rehabilitative systems [J]. Annu Int Conf IEEE Eng Med Biol Soc, 2017, 2017: 2518-2521.
30	López-Larraz E, Bibián C, Birbaumer N, et al. Influence of artifacts on movement intention decoding from EEG activity in severely paralyzed stroke patients [J]. IEEE Int Conf Rehabil Robot, 2017, 2017: 901-906.
31	Ang K K, Chua K S, Phua K S, et al. A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke [J]. Clin EEG Neurosci, 2015, 46(4): 310-320.
32	Krebs H I, Palazzolo J J, Dipietro L, et al. Rehabilitation robotics: performance-based progressive robot-assisted therapy [J]. Autonomous Robots, 2003, 15(1): 7-20.
33	Ramos-Murguialday A, Broetz D, Rea M, et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study [J]. Ann Neurol, 2013, 74(1): 100-108.
34	McConnell A C, Moioli R C, Brasil F L, et al. Robotic devices and brain-machine interfaces for hand rehabilitation post-stroke [J]. J Rehabil Med, 2017, 49(6): 449-460.
35	Ganguly K, Secundo L, Ranade G, et al. Cortical representation of ipsilateral arm movements in monkey and man [J]. J Neurosci, 2009, 29(41): 12948-12956.
36	Antelis J M, Montesano L, Ramos-Murguiaday A, et al. Decoding upper limb movement attempt from EEG measurements of the contralesional motor cortex in chronic stroke patients [J]. IEEE Trans Biomed Eng, 2017, 64(1): 99-111.
37	Marin-Pardo O, Laine C M, Rennie M, et al. A virtually reality muscle-computer interface for neurorehabilitation in chronic stroke: a pilot study [J]. Sensors, 2020, 20: 3754.
38	Ramos-Murguialday A, Curado M R, Broetz D, et al. Brain-machine-interface in chronic stroke: randomised trial long-term follow-up [J]. Neurorehabil Neural Repair, 2019, 33(3): 188-198.
39	Frolov A A, Mokienko O, Lyukmanov R, et al. Post-stroke rehabilitation training with a motor-imagery-based brain-computer interface (BCI) -controlled exoskeleton: a randomized controlled multicenter trial [J]. Front Neurosci, 2017, 11: 400.
40	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 [J]. Occup Ther Int, 2016, 23(1): 39-47.
41	Mrachacz-Kersting N, Jiang N, Stevenson A J, et al. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface [J]. J Neurophysiol, 2016, 115(3): 1410-1421.
42	Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke [J]. Nat Commun, 2018, 9(1): 2421.
43	Carvalho R, Dias N, Cerqueira J J. Brain-machine interface of upper limb recovery in stroke patients rehabilitation: a systematic review [J]. Physiother Res Int, 2019, 24: e1764.
44	Ang K K, Guan C, Phua K S, et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: result of a three-armed randomized controlled trial for chronic stroke [J]. Front Neuroeng, 2014, 7: 30.
45	Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis [J]. Ann Clin Transl Neurol, 2018, 5(5): 651-663.
46	Mugler E M, Tomic G, Singh A, et al. Myoelectric computer interface training for reducing co-activative and enhancing arm movement in chronic stroke survivors: a randomized trial [J]. Neurorehabil Neural Repair, 2019, 33(4): 284-295.
47	Vourvopoulos A, Pardo O M, Lefebvre S, et al. Effects of a brain-computer interface with virtual reality (VR) neurofeedback: a pilot study in chronic stroke patients [J]. Front Hum Neurosci, 2019, 13: 1-17.
48	Morone G, Pisotta I, Pichiorri F, et al. Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability [J]. Arch Phys Med Rehabil, 2015, 96(3): S71-S78.
49	Soekadar S R, Birbaumer N, Slutzky M W, et al. Brain-machine interface in neurorehabilitation of stroke [J]. Neurobiol Dis, 2015, 83: 172-179.
50	Li M, Liu Y, Wu Y, et al. Neurophysiological substrates of stroke patients with motor imagery-based brain-computer interface training [J]. Int J Neurosci, 2014, 124(6): 403-415.
51	Waldert S. Invasive vs. non-invasive neuronal signals for brain-machine interfaces: will one prevail? [J]. Front Neurosci, 2016, 10: 295.
52	宁晓路,曹永福,张颖,等. 脑机接口技术应用的伦理问题分析[J]. 医学与哲学, 2018, 39(9A): 35-38.
	Ning X L, Cao Y F, Zhang Y, et al. Analysis of ethical issues in the application of brain-computer interface technology [J]. Med Philosophy, 2018, 39(9A): 35-38.
53	Winters C, Heymans M W, van Wegen E E H, et al. How to design clinical rehabilitation trials for the upper paretic limb early post stroke? [J]. Trials, 2016, 17: 468.

[1]	邵伟婷, 雷江华. 反应中断再定向干预孤独症谱系障碍儿童刻板语言的效果：Scoping综述[J]. 《中国康复理论与实践》, 2024, 30(1): 10-20.
[2]	罗丽华, 王雨生, 李剑锋, 董继革. 术后早期综合康复对儿童青少年肱骨髁上骨折伴尺神经损伤的效果[J]. 《中国康复理论与实践》, 2024, 30(1): 105-110.
[3]	王子豪, 李昕华, 蒋慧萍, 郭赛男, 梁秋曼, 史婷奇. 全膝关节置换术后短期膝关节功能及其影响因素[J]. 《中国康复理论与实践》, 2024, 30(1): 111-118.
[4]	王航宇, 葛可可, 范永红, 都丽露, 邹敏, 封磊. 基于ICD-11和ICF主动式音乐疗法改善认知障碍老年人认知功能的系统综述[J]. 《中国康复理论与实践》, 2024, 30(1): 36-43.
[5]	闻嘉宁, 金秋艳, 张琦, 李杰, 司琦. 认知参与型身体活动对发展儿童青少年执行功能的效果：基于ICF的系统综述[J]. 《中国康复理论与实践》, 2024, 30(1): 44-53.
[6]	葛可可, 范永红, 王航宇, 都丽露, 李长江, 邹敏. 失眠老年人正念干预健康效益的系统综述[J]. 《中国康复理论与实践》, 2024, 30(1): 54-60.
[7]	林娜, 高菡璐, 卢惠苹, 陈燕清, 郑军凡, 陈述荣. 虚拟现实技术对脑卒中上肢功能影响的弥散张量成像研究[J]. 《中国康复理论与实践》, 2024, 30(1): 61-67.
[8]	陈珺雯, 陈谦, 陈程, 李淑月, 刘玲玲, 吴存书, 龚翔, 鲁俊, 许光旭. 改良八段锦身体活动对脑卒中患者心肺功能、运动功能和日常生活活动能力的效果[J]. 《中国康复理论与实践》, 2024, 30(1): 74-80.
[9]	张婧雅, 邹敏, 孙宏伟, 孙昌隆, 朱峻同. 听障儿童青少年焦虑或抑郁情绪心理干预效果的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1004-1011.
[10]	王俊宇, 杨永, 袁逊, 谢婷, 庄洁. 高强度间歇训练对健康儿童青少年执行功能效果的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1012-1020.
[11]	魏晓微, 杨剑, 魏春艳. 特殊教育学校孤独症谱系障碍儿童参与适应性瑜伽活动的心理与行为效益的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1021-1028.
[12]	杨亚茹, 杨剑. 基于WHO-HPS架构学校身体活动相关健康服务及其健康效益：系统综述的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1040-1047.
[13]	史佳伟, 李凌宇, 杨浩杰, 王琴潞, 邹海欧. 预康复对全膝关节置换术后患者的有效性：系统综述的系统综述[J]. 《中国康复理论与实践》, 2023, 29(9): 1057-1064.
[14]	蔡华年, 费思先, 张忆晨, 孙青, 郭帅, 宋韬. 基于导纳控制的双边康复机器人运动辅助分析[J]. 《中国康复理论与实践》, 2023, 29(9): 1104-1109.
[15]	孙藤方, 任梦婷, 杨琳, 王耀霆, 王红雨, 闫兴洲. 高压氧治疗联合重复外周磁刺激干预脑卒中患者踝运动功能和平衡能力的效果[J]. 《中国康复理论与实践》, 2023, 29(8): 875-881.