Technical principle and clinical application of brain-computer interface

doi:10.3877/cma.j.issn.2095-123X.2023.04.008

Abstract

Abstract:

Some neurological diseases can cause the signal "bridge" between the brain and limbs to be broken, leaving serious dysfunction. Therefore, how to restore the function of this "bridge" has always been a hot-topic in the field of nervous system. Brain-computer interface (BCI) is based on the process of connecting the brain with machinery to realize the control of machinery by the brain. BCI technology has become a research hotspot in the fields of neuroscience and computer technology, which plays an important role in the field of nervous system, especially in the aspects of neural rehabilitation, prosthetics, robotics, disease diagnosis and treatment related to neuroscience. With the development of information technology, opportunities and challenges coexist in BCI technology. This article reviews the application of BCI in the field of neurological diseases.

Key words: Brain-computer interface, Nervous system diseases, Consciousness, Signal, Application

Qing Xiao, Cheng Wang, Kun Zhou, Yigong Wei. Technical principle and clinical application of brain-computer interface[J]. Chinese Journal of Brain Diseases and Rehabilitation(Electronic Edition), 2023, 13(04): 241-245.

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[1]	张亚卓.中国脑计划与神经外科发展[J].中华神经外科杂志, 2017, 33(1): 1-3. DOI: 10.3760/cma.j.issn.1001-2346.2017.01.001.
[2]	Kaongoen N, Choi J, Jo S. Speech-imagery-based brain-computer interface system using ear-EEG[J]. J Neural Eng, 2021, 18(1): 016023. DOI: 10.1088/1741-2552/abd10e.
[3]	Khalil K, Asgher U, Ayaz Y. Novel fnirs study on homogeneous symmetric feature-based transfer learning for brain-computer interface[J]. Sci Rep, 2022, 12(1): 3198. DOI: 10.1038/s41598-022-06805-4.
[4]	Wandelt SK, Kellis S, Bjånes DA, et al. Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human[J]. Neuron, 2022, 110(11): 1777-1787.e3. DOI: 10.1016/j.neuron.2022.03.009.
[5]	Martini ML, Oermann EK, Opie NL, et al. Sensor modalities for brain-computer interface technology: a comprehensive literature review[J]. Neurosurgery, 2020, 86(2): E108-E117. DOI: 10.1093/neuros/nyz286.
[6]	Bowen JI, Liang Z, Yuan X, et al. Recent advances in wireless epicortical and intracortical neuronal recording systems[J]. Science China Information Sciences, 2022, 65(4): 1-18. DOI: 10.1007/s11432-021-3373-1.
[7]	Li Y, Wang Y, Cui H. Posterior parietal cortex predicts upcoming movement in dynamic sensorimotor control[J]. Proc Natl Acad Sci U S A, 2022, 119(13): e2118903119. DOI: 10.1073/pnas.2118903119.
[8]	Lee SH, Thunemann M, Lee K, et al. Scalable thousand channel penetrating microneedle arrays on flex for multimodal and large area coverage brainmachine interfaces[J]. Adv Funct Mater, 2022, 32(25): 2112045. DOI: 10.1002/adfm.202112045.
[9]	Jiang Y, Zhang Z, Wang YX, et al. Topological supramolecular network enabled high-conductivity, stretchable organic bioelectronics[J]. Science, 2022, 375(6587): 1411-1417. DOI: 10.1126/science.abj7564.
[10]	徐如祥.脑芯片-脑机接口治疗技术进展[J].中华脑科疾病与康复杂志(电子版), 2020, 10(6): 383-384. DOI: 10.3877/cma.j.issn.2095-123X.2020.06.016.
[11]	Arts LPA, van den Broek EL. The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time-frequency analysis[J]. Nat Comput Sci, 2022, 2: 47-58. DOI: 10.1038/s43588-021-00183-z.
[12]	Kumar N, Michmizos KP. A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity[J]. Sci Rep, 2022, 12(1): 1101. DOI: 10.1038/s41598-022-05079-0.
[13]	Gong ZQ, Gao P, Jiang C, et al. Dream: a toolbox to decode rhythms of the brain system[J]. Neuroinformatics, 2021, 19(3): 529-545. DOI: 10.1007/s12021-020-09500-9.
[14]	Wang T, Chen Y, Cui H. From parametric representation to dynamical system: Shifting views of the motor cortex in motor control[J]. Neurosci Bull, 2022, 38(7): 796-808. DOI: 10.1007/s12264-022-00832-x.
[15]	Kolkhorst H, Veit J, Burgard W, et al. A robust screen-free brain-computer interface for robotic object selection[J]. Front Robot AI, 2020, 7: 38. DOI: 10.3389/frobt.2020.00038.
[16]	吴毅.脑损伤后精准康复治疗技术的研究进展[J].中华物理医学与康复杂志, 2021, 43(11): 1040-1043. DOI: 10.3760/cma.j.issn.0254-1424.2021.11.020.
[17]	郑淑悦,王培,刘艳,等.脑机接口训练对脑卒中患者肢体功能障碍干预效果的Meta分析[J].中国实用护理杂志, 2021, 37(22): 1747-1753. DOI: 10.3760/cma.j.cn211501-20200526-02482.
[18]	刘霞,张萍,李云杰,等.基于运动想象的脑机接口技术运用于脑卒中瘫痪患者脑功能激活和神经网络重塑的研究进展[J].中华神经科杂志, 2021, 54(10): 1089-1093. DOI: 10.3760/cma.j.cn113694-20210202-00085.
[19]	Tremmel C, Herff C, Sato T, et al. Estimating cognitive workload in an interactive virtual reality environment using EEG[J]. Front Hum Neurosci, 2019, 13: 401. DOI: 10.3389/fnhum.2019.00401.
[20]	马珂,徐会友,江继鹏,等.脑机接口技术在创伤性脑损伤神经功能修复中的应用研究进展[J].中华创伤杂志, 2018, 34(8): 754-758. DOI: 10.3760/cma.j.issn.1001-8050.2018.08.015.
[21]	Heelan C, Lee J, O'Shea R, et al. Decoding speech from spike-based neural population recordings in secondary auditory cortex of non-human primates[J]. Commun Biol, 2019, 2: 466. DOI: 10.1038/s42003-019-0707-9.
[22]	Deo DR, Willett FR, Avansino DT, et al. Translating deep learning to neuroprosthetic control[J]. bioRxiv, 2023, Preprint. DOI: 10.1101/2023.04.21.537581.
[23]	Wang W, Jiang Y, Zhong D, et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin[J]. Science, 2023, 380(6646): 735-742. DOI: 10.1126/science.ade0086.
[24]	Chaudhary U, Vlachos I, Zimmermann JB, et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback trainin[J]. Nat Commun, 2022, 13(1): 1236. DOI: 10.1038/s41467-022-28859-8.
[25]	Wang X, Sun X, Gan D, et al. Bioadhesive and conductive hydrogel-integrated brain-machine interfaces for conformal and immune-evasive contact with brain tissue[J]. Matter, 2022, 5(4): 1204-1223. DOI: 10.1016/j.matt.2022.01.012.
[26]	蒋鸿杰,郑喆,朱君明.闭环式脑深部电刺激术在帕金森病治疗中的研究进展[J].中华神经外科杂志, 2019, 35(7): 743-746. DOI: 10.3760/cma.j.issn.1001-2346.2019.07.024.
[27]	Yang Y, He Q, Dang Y, et al. Long-term functional outcomes improved with deep brain stimulation in patients with disorders of consciousness[J]. Stroke Vasc Neurol, 2023, Online ahead of print. DOI: 10.1136/svn-2022-001998.
[28]	Yang Y, Qiao S, Sani OG, et al. Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation[J]. Nat Biomed Eng, 2021, 5(4): 324-345. DOI: 10.1038/s41551-020-00666-w.
[29]	Tervo AE, Nieminen JO, Lioumis P, et al. Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback[J]. Brain Stimul, 2022, 15(2): 523-531. DOI: 10.1016/j.brs.2022.01.016.
[30]	Zhao S, Yang J, Wang J, et al. A 0.99-to-4.38 uJ/class event-driven hybrid neural network processor for full-spectrum neural signal analyses[J]. IEEE Trans Biomed Circuits Syst, 2023, 17(3): 598-609. DOI: 10.1109/TBCAS.2023.3268502.
[31]	Putkinen V, Nazari-Farsani S, Seppälä K, et al. Decoding music-evoked emotions in the auditory and motor cortex[J]. Cereb Cortex, 2021, 31(5): 2549-2560. DOI: 10.1093/cercor/bhaa373.
[32]	Somers B, Long CJ, Francart T. EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors[J]. Sci Rep, 2021, 11(1): 5383. DOI: 10.1038/s41598-021-84829-y.
[33]	Anbarasan R, Gomez Carmona D, Mahendran R. Human taste-perception: brain computer interface (BCI) and its application as an engineering tool for taste-driven sensory studies[J]. Food Engineering Reviews, 2022, 14(3): 408-434. DOI: 10.1007/s12393-022-09308-0.
[34]	Yanagisawa T, Fukuma R, Seymour B, et al. BCI training to move a virtual hand reduces phantom limb pain: a randomized crossover trial[J]. Neurology, 2020, 95(4): e417-e426. DOI: 10.1212/WNL.0000000000009858.
[35]	Cheng G, Ehrlich SK, Lebedev M, et al. Neuroengineering challenges of fusing robotics and neuroscience[J]. Sci Robot, 2020, 5(49): eabd1911. DOI: 10.1126/scirobotics.abd1911.
[36]	Botvinik-Nezer R, Holzmeister F, Camerer CF, et al. Variability in the analysis of a single neuroimaging dataset by many teams[J]. Nature, 2020, 582(7810): 84-88. DOI: 10.1038/s41586-020-2314-9.
[37]	Xing XX, Xu T, Jiang C, et al. Connectome computation system: 2015-2021 updates[J]. Sci Bull (Beijing), 2022, 67(5): 448-451. DOI: 10.1016/j.scib.2021.11.021.
[38]	Kang YN, Chou N, Jang JW, et al. A 3D flexible neural interface based on a microfluidic interconnection cable capable of chemical delivery[J]. Microsyst Nanoeng, 2021, 7: 66. DOI: 10.1038/s41378-021-00295-6.

类型	原理	优点	缺点	其他
头皮脑电图	通过头皮、颅骨获取神经元细胞外电位变化	时间分辨率高，价格低廉，便捷，损伤小	获取信号质量差	目前应用最广泛
脑磁图	通过颅外记录树突产生的细胞内磁信号变化	对细胞内信号敏感度强，不易受外界电场干扰，时空高分辨率	设备笨重，价格昂贵，操作不方便	操作时需排除电磁干扰
皮质脑电图	将电极置入脑表面直接采集脑表面活动电信号	信号质量强，稳定性高，时空分辨率高	有创操作，有异物排除风险，有增加颅内感染风险	涉及伦理问题
皮质内神经元电位记录	将电极精确置入脑深部灰质神经元捕捉活动电信号	时空分辨率更高，总体优于皮质脑电图	技术要求更高，有创操作，有异物排除风险，有增加颅内感染风险	涉及伦理问题
功能磁共振	通过电磁场变化来记录神经活动区域代谢（血流量及血氧含量）变化	空间分辨率高	时间分辨率低，易受血流延迟效应影响	对快速交流脑-机接口不适用
近红外光谱	利用光谱信号衰减转换为含氧血红蛋白相对含量监测脑代谢信号	时间分辨率高，费用低，操作方便	穿透力受限，深部神经元信号质量差，受血流延迟效应影响	新的神经成像模态

类型	原理	优点	缺点	其他
头皮脑电图	通过头皮、颅骨获取神经元细胞外电位变化	时间分辨率高，价格低廉，便捷，损伤小	获取信号质量差	目前应用最广泛
脑磁图	通过颅外记录树突产生的细胞内磁信号变化	对细胞内信号敏感度强，不易受外界电场干扰，时空高分辨率	设备笨重，价格昂贵，操作不方便	操作时需排除电磁干扰
皮质脑电图	将电极置入脑表面直接采集脑表面活动电信号	信号质量强，稳定性高，时空分辨率高	有创操作，有异物排除风险，有增加颅内感染风险	涉及伦理问题
皮质内神经元电位记录	将电极精确置入脑深部灰质神经元捕捉活动电信号	时空分辨率更高，总体优于皮质脑电图	技术要求更高，有创操作，有异物排除风险，有增加颅内感染风险	涉及伦理问题
功能磁共振	通过电磁场变化来记录神经活动区域代谢（血流量及血氧含量）变化	空间分辨率高	时间分辨率低，易受血流延迟效应影响	对快速交流脑-机接口不适用
近红外光谱	利用光谱信号衰减转换为含氧血红蛋白相对含量监测脑代谢信号	时间分辨率高，费用低，操作方便	穿透力受限，深部神经元信号质量差，受血流延迟效应影响	新的神经成像模态

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