植入式脑机接口在医疗与科研中的作用与应用

doi:10.12211/2096-8280.2022-071

摘要/Abstract

摘要：

脑机接口，目前主要作为一种神经替代体存在，它使电子设备能够直接与大脑的某些部分，通常是大脑皮层进行通信。近几年植入式脑机接口技术取得了非常显著的进步，功能应用方面也有了重大的拓展。最常见的脑机接口应用在医疗方面，典型形式如仅通过采集大脑的信号就能合成出听得懂的语音，通过对大脑感觉皮层进行定点电刺激来获得人工触觉，让上肢瘫痪的患者通过想象拨动手指来使用平板电脑，通过电刺激手部特定肌肉群来恢复对手的控制功能，等等。像脊髓损伤、运动神经元疾病或中风等病症目前是难以治疗的，通过脑机接口的方式可以恢复瘫痪患者的实质性交互功能。另外，在神经科学研究领域，脑机接口也是研究神经替代体和大脑-行为关系中意识和潜意识反馈的强大方法。这两个方面中的研究主流都是针对运动功能的脑机接口。本文回顾了脑机接口的发展以及不同类型的信号源，并分别对面向医疗和面向科研的脑机接口进行介绍。

关键词: 神经替代体, 通信, 医疗, 人工触觉, 瘫痪, 神经科学, 运动功能

Abstract:

Brain-computer interfaces (BCI) currently exist primarily as a neuroprosthesis that allows electronic devices to communicate directly with parts of human brain, typically the cerebral cortex. In recent years, implantable BCI technology has made remarkable progress, and its applications have been expanded significantly, indicating that research achievements on neuroscience and their important transformation to BCI technology are interacted more efficiently and effectively. BCI is a process in which collected brain signals are decoded into digital information by a decoding algorithm through signal analysis, and computer, mechanical prosthesis, or other electrical stimulation device can be controlled based on this information. The most common applications of BCI are in medical applications, usually by capturing brain signals to synthesize understandable communications. For example, artificial tactile stimuli can be obtained through targeted electrical stimulation of the sensory cortex of brain, allowing patients with upper limb paralysis to imaginatively move their arms with the help from a tablet computer, restoring hand control through electrical stimulation of specific muscle tissues in the hand, and so on. Diseases like spinal cord injury, motor neuron disease, and stroke are currently untreatable, and BCI can be used to restore many interactive functions for paralyzed patients so that they can live and work normally, such as being able to communicate with others, such as talking, typing, and online socializing. On the other hand, BCI is also a powerful tool in neuroscience to study brain functions, such as conscious and subconscious feedback in brain-behavioral relationships. BCI for the motor function is the mainstream of research in these two fields. More and more BCI applications would be introduced in the near future, which could benefit paralyzed patients and patients with mental disorders, improving, even restoring their life qualities. In this article, we review the development of BCI and the different types of signal sources, with a focus on the medical-oriented and research-oriented BCI as well.

Key words: neuroprosthesis, communication, medical, artificial tactile stimuli, paralysis, neuroscience, motor function

中图分类号:

Q819

刘菱, 郑胜杰, 窦汇溪, 李骁健. 植入式脑机接口在医疗与科研中的作用与应用[J]. 合成生物学, 2023, 4(2): 407-417.

Ling LIU, Shengjie ZHENG, Huixi DOU, Xiaojian LI. Functions and applications of implantable brain-computer interfaces in medical treatment and scientific research[J]. Synthetic Biology Journal, 2023, 4(2): 407-417.

图/表 4

图1 脑机接口的信号源、传感器位置及信号波形［15］

Fig. 1 Sensor locations and signal sources and waves of various brain-computer interfaces[15]

图2 脑机接口中记录到的神经脉冲信号不能保持长时间稳定

Fig. 2 Nerve pulse signals recorded in brain-computer interfaces cannot remain stable for a long time

图3 初级运动皮层神经元的活性与肢体运动状态有直接对应关系［3］

Fig. 3 Activities of primary motor cortex neurons correlate directly with limb movement state[3]

图4 自然运动学习中小鼠运动皮层神经活动的变化［67］

Fig. 4 Changes in neural activities in mouse motor cortex during natural motor learning[67]

参考文献 68

1	FETZ E E. Operant conditioning of cortical unit activity[J]. Science, 1969, 163(3870): 955-958.
2	EVARTS E V. Relation of pyramidal tract activity to force exerted during voluntary movement[J]. Journal of Neurophysiology, 1968, 31(1): 14-27.
3	GEORGOPOULOS A P, KALASKA J F, CAMINITI R, et al. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex[J]. The Journal of Neuroscience, 1982, 2(11): 1527-1537.
4	CHENG M, GAO X R, GAO S K, et al. Design and implementation of a brain-computer interface with high transfer rates[J]. IEEE Transactions on Bio-Medical Engineering, 2002, 49(10): 1181-1186.
5	PFURTSCHELLER G, ARANIBAR A. Event-related cortical desynchronization detected by power measurements of scalp EEG[J]. Electroencephalography and Clinical Neurophysiology, 1977, 42(6): 817-826.
6	SERRUYA M D, HATSOPOULOS N G, PANINSKI L, et al. Brain-machine interface: instant neural control of a movement signal[J]. Nature, 2002, 416(6877): 141-142.
7	ROUSCHE P J, NORMANN R A. Chronic recording capability of the Utah Intracortical Electrode Array in cat sensory cortex[J]. Journal of Neuroscience Methods, 1998, 82(1): 1-15.
8	NICOLELIS M A L, DIMITROV D, CARMENA J M, et al. Chronic, multisite, multielectrode recordings in macaque monkeys[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(19): 11041-11046.
9	MUSALLAM S, CORNEIL B D, GREGER B, et al. Cognitive control signals for neural prosthetics[J]. Science, 2004, 305(5681): 258-262.
10	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.
11	CHAUDHARY U, XIA B, SILVONI S, et al. Correction: Brain-computer interface-based communication in the completely locked-in state[J]. PLoS Biology, 2018, 16(12): e3000089.
12	WANDER J D, BLAKELY T, MILLER K J, et al. Distributed cortical adaptation during learning of a brain-computer interface task[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(26): 10818-10823.
13	LOGOTHETIS N K. The underpinnings of the BOLD functional magnetic resonance imaging signal[J]. The Journal of Neuroscience, 2003, 23(10): 3963-3971.
14	GALLEGOS-AYALA G, FURDEA A, TAKANO K, et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy[J]. Neurology, 2014, 82(21): 1930-1932.
15	SLUTZKY M W. Brain-machine interfaces: powerful tools for clinical treatment and neuroscientific investigations[J]. The Neuroscientist, 2019, 25(2): 139-154.
16	BIRBAUMER N, HINTERBERGER T, KUBLER A, et al. The thought-translation device: an update[C]//Proceedings of the Psychophysiology. New York: Cambridge University Press, 2000, 37: S28.
17	FARWELL L A, DONCHIN E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials[J]. Electroencephalography and Clinical Neurophysiology, 1988, 70(6): 510-523.
18	GUAN C T, THULASIDAS M, WU J K. High performance P300 speller for brain-computer interface[C]//IEEE International Workshop on Biomedical Circuits and Systems. December 1-3, 2004, Singapore. IEEE, 2005: S3/5 /INV-S 3/13.
19	PFURTSCHELLER G, MÜLLER G R, PFURTSCHELLER J, et al. 'Thought'-control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia[J]. Neuroscience Letters, 2003, 351(1): 33-36.
20	WOLPAW J R, MCFARLAND D J. Multichannel EEG-based brain-computer communication[J]. Electroencephalography and Clinical Neurophysiology, 1994, 90(6): 444-449.
21	MCFARLAND D J, SARNACKI W A, WOLPAW J R. Electroencephalographic (EEG) control of three-dimensional movement[J]. Journal of Neural Engineering, 2010, 7(3): 036007.
22	GANGADHAR G, CHAVARRIAGA R, MILLÁN J D E L R. Fast recognition of anticipation-related potentials[J]. IEEE Transactions on Bio-Medical Engineering, 2009, 56(4): 1257-1260.
23	MITZDORF U. Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena[J]. Physiological Reviews, 1985, 65(1): 37-100.
24	BOTO E, HOLMES N, LEGGETT J, et al. Moving magnetoencephalography towards real-world applications with a wearable system[J]. Nature, 2018, 555(7698): 657-661.
25	WANG Y J, WANG R P, GAO X R, et al. A practical VEP-based brain-computer interface[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006, 14(2): 234-239.
26	MORROW M M, MILLER L E. Prediction of muscle activity by populations of sequentially recorded primary motor cortex neurons[J]. Journal of Neurophysiology, 2003, 89(4): 2279-2288.
27	TODOROVA S, SADTLER P, BATISTA A, et al. To sort or not to sort: the impact of spike-sorting on neural decoding performance[J]. Journal of Neural Engineering, 2014, 11(5): 056005.
28	BUZSÁKI G, ANASTASSIOU C A, KOCH C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes[J]. Nature Reviews Neuroscience, 2012, 13(6): 407-420.
29	STARK E, ABELES M. Predicting movement from multiunit activity[J]. The Journal of Neuroscience, 2007, 27(31): 8387-8394.
30	CHESTEK C A, GILJA V, NUYUJUKIAN P, et al. Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex[J]. Journal of Neural Engineering, 2011, 8(4): 045005.
31	SANES J N, DONOGHUE J P. Oscillations in local field potentials of the primate motor cortex during voluntary movement[J]. Proceedings of the National Academy of Sciences of the United States of America, 1993, 90(10): 4470-4474.
32	MURTHY V N, FETZ E E. Oscillatory activity in sensorimotor cortex of awake monkeys: synchronization of local field potentials and relation to behavior[J]. Journal of Neurophysiology, 1996, 76(6): 3949-3967.
33	BUNDY D T, PAHWA M, SZRAMA N, et al. Decoding three-dimensional reaching movements using electrocorticographic signals in humans[J]. Journal of Neural Engineering, 2016, 13(2): 026021.
34	SCHALK G, KUBÁNEK J, MILLER K J, et al. Decoding two-dimensional movement trajectories using electrocorticographic signals in humans[J]. Journal of Neural Engineering, 2007, 4(3): 264-275.
35	CRONE N E, MIGLIORETTI D L, GORDON B, et al. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization[J]. Brain, 1998, 121(12): 2271-2299.
36	KUBÁNEK J, MILLER K J, OJEMANN J G, et al. Decoding flexion of individual fingers using electrocorticographic signals in humans[J]. Journal of Neural Engineering, 2009, 6(6): 066001.
37	FLINT R D, WANG P T, WRIGHT Z A, et al. Extracting kinetic information from human motor cortical signals[J]. NeuroImage, 2014, 101: 695-703.
38	MCCRIMMON C M, WANG P T, HEYDARI P, et al. Electrocorticographic encoding of human gait in the leg primary motor cortex[J]. Cerebral Cortex, 2018, 28(8): 2752-2762.
39	MUGLER E M, PATTON J L, FLINT R D, et al. Direct classification of all American English phonemes using signals from functional speech motor cortex[J]. Journal of Neural Engineering, 2014, 11(3): 035015.
40	WANG P T, KING C E, MCCRIMMON C M, et al. Comparison of decoding resolution of standard and high-density electrocorticogram electrodes[J]. Journal of Neural Engineering, 2016, 13(2): 026016.
41	PANDARINATH C, NUYUJUKIAN P, BLABE C H, et al. High performance communication by people with paralysis using an intracortical brain-computer interface[J]. eLife, 2017, 6: e18554.
42	MOSES D A, METZGER S L, LIU J R, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria[J]. The New England Journal of Medicine, 2021, 385(3): 217-227.
43	COLLINGER J L, WODLINGER B, DOWNEY J E, et al. High-performance neuroprosthetic control by an individual with tetraplegia[J]. The Lancet, 2013, 381(9866): 557-564.
44	BOUTON C E, SHAIKHOUNI A, ANNETTA N V, et al. Restoring cortical control of functional movement in a human with quadriplegia[J]. Nature, 2016, 533(7602): 247-250.
45	NOWAK D A, GREFKES C, AMELI M, et al. Interhemispheric competition after stroke: brain stimulation to enhance recovery of function of the affected hand[J]. Neurorehabilitation and Neural Repair, 2009, 23(7): 641-656.
46	BACHER D, JAROSIEWICZ B, MASSE N Y, et al. Neural point-and-click communication by a person with incomplete locked-in syndrome[J]. Neurorehabilitation and Neural Repair, 2015, 29(5): 462-471.
47	JAROSIEWICZ B, SARMA A A, BACHER D, et al. Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface[J]. Science Translational Medicine, 2015, 7(313): 313ra179.
48	GUENTHER F H, BRUMBERG J S, WRIGHT E J, et al. A wireless brain-machine interface for real-time speech synthesis[J]. PLoS One, 2009, 4(12): e8218.
49	BOUCHARD K E, MESGARANI N, JOHNSON K, et al. Functional organization of human sensorimotor cortex for speech articulation[J]. Nature, 2013, 495(7441): 327-332.
50	WILLETT F R, AVANSINO D T, HOCHBERG L R, et al. High-performance brain-to-text communication via handwriting[J]. Nature, 2021, 593(7858): 249-254.
51	HOGGAN E, BREWSTER S A, JOHNSTON J. Investigating the effectiveness of tactile feedback for mobile touchscreens[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. April 5-10, 2008, Florence, Italy. New York: ACM, 2008: 1573-1582.
52	PRASAD G, HERMAN P, COYLE D, et al. Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study[J]. Journal of Neuroengineering and Rehabilitation, 2010, 7: 60.
53	AJIBOYE A B, WILLETT F R, YOUNG D R, et al. Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: a proof-of-concept demonstration[J]. The Lancet, 2017, 389(10081): 1821-1830.
54	DALY J J, WOLPAW J R. Brain-computer interfaces in neurological rehabilitation[J]. The Lancet Neurology, 2008, 7(11): 1032-1043.
55	WODLINGER B, DOWNEY J E, TYLER-KABARA E C, et al. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations[J]. Journal of Neural Engineering, 2015, 12(1): 016011.
56	MILLER K J, SCHALK G, FETZ E E, et al. Cortical activity during motor execution, motor imagery, and imagery-based online feedback[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(9): 4430-4435.
57	MORAN D W, SCHWARTZ A B. Motor cortical representation of speed and direction during reaching[J]. Journal of Neurophysiology, 1999, 82(5): 2676-2692.
58	KAREN A, MOXON, . Brain-machine interfaces beyond neuroprosthetics[J]. Neuron, 2015, 86(1): 55-67.
59	GALLEGO J A, PERICH M G, CHOWDHURY R H, et al. Long-term stability of cortical population dynamics underlying consistent behavior[J]. Nature Neuroscience, 2020, 23(2): 260-270.
60	CHESTEK C A, BATISTA A P, SANTHANAM G, et al. Single-neuron stability during repeated reaching in macaque premotor cortex[J]. The Journal of Neuroscience, 2007, 27(40): 10742-10750.
61	FLINT R D, WRIGHT Z A, SCHEID M R, et al. Long term, stable brain machine interface performance using local field potentials and multiunit spikes[J]. Journal of Neural Engineering, 2013, 10(5): 056005.
62	WESSBERG J, STAMBAUGH C R, KRALIK J D, et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in Primates[J]. Nature, 2000, 408(6810): 361-365.
63	SHEN L, ALEXANDER G E. Preferential representation of instructed target location versus limb trajectory in dorsal premotor area[J]. Journal of Neurophysiology, 1997, 77(3): 1195-1212.
64	GANGULY K, DIMITROV D F, WALLIS J D, et al. Reversible large-scale modification of cortical networks during neuroprosthetic control[J]. Nature Neuroscience, 2011, 14(5): 662-667.
65	GOLUB M D, SADTLER P T, OBY E R, et al. Learning by neural reassociation[J]. Nature Neuroscience, 2018, 21(4): 607-616.
66	GANGULY K, CARMENA J M. Emergence of a stable cortical map for neuroprosthetic control[J]. PLoS Biology, 2009, 7(7): e1000153.
67	PETERS A J, CHEN S X, KOMIYAMA T. Emergence of reproducible spatiotemporal activity during motor learning[J]. Nature, 2014, 510(7504): 263-267.
68	JAROSIEWICZ B, CHASE S M, FRASER G W, et al. Functional network reorganization during learning in a brain-computer interface paradigm[J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(49): 19486-19491.

[1]	施茜, 吴园园, 杨洋. DNA纳米技术与合成生物学[J]. 合成生物学, 2022, 3(2): 302-319.
[2]	钱秀娟, 陈琳, 章文明, 周杰, 董维亮, 信丰学, 姜岷. 人工多细胞体系设计与构建研究进展[J]. 合成生物学, 2020, 1(3): 267-284.