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

Abstract

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

CLC Number:

R743.3

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.

References 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.

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Advance in Application of Brain-computer Interface for Upper Limb Rehabilitation for Patients with Chronic Stroke (review)

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