The research status and development trends of brain-computer interfaces in medicine_Journal of Biomedical Engineering

Authors：

CHEN Qi ¹ , YUAN Tianwei ² , ZHANG Liwen ² , GONG Jin ³ , FU Lu ⁴ , HAN Xue ⁴ ,  RUAN Meihua ² , YU Zhenhang ¹

1. China National Center for Biotechnology Development, Beijing 100039, P. R. China;
2. Shanghai Information Center for Life Sciences, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, P. R. China;
3. Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, P. R. China;
4. Chinese Neuroscience Society, Shanghai 200032, P. R. China;

Corresponding author：

Keywords：

Brain-computer interfaces; Medical application; Research status; Development trends

DOI：

10.7507/1001-5515.202303038

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Brain-computer interfaces (BCIs) have become one of the cutting-edge technologies in the world, and have been mainly applicated in medicine. In this article, we sorted out the development history and important scenarios of BCIs in medical application, analyzed the research progress, technology development, clinical transformation and product market through qualitative and quantitative analysis, and looked forward to the future trends. The results showed that the research hotspots included the processing and interpretation of electroencephalogram (EEG) signals, the development and application of machine learning algorithms, and the detection and treatment of neurological diseases. The technological key points included hardware development such as new electrodes, software development such as algorithms for EEG signal processing, and various medical applications such as rehabilitation and training in stroke patients. Currently, several invasive and non-invasive BCIs are in research. The R&D level of BCIs in China and the United State is leading the world, and have approved a number of non-invasive BCIs. In the future, BCIs will be applied to a wider range of medical fields. Related products will develop shift from a single mode to a combined mode. EEG signal acquisition devices will be miniaturized and wireless. The information flow and interaction between brain and machine will give birth to brain-machine fusion intelligence. Last but not least, the safety and ethical issues of BCIs will be taken seriously, and the relevant regulations and standards will be further improved.

Citation： CHEN Qi, YUAN Tianwei, ZHANG Liwen, GONG Jin, FU Lu, HAN Xue, RUAN Meihua, YU Zhenhang. The research status and development trends of brain-computer interfaces in medicine. Journal of Biomedical Engineering, 2023, 40(3): 566-572. doi: 10.7507/1001-5515.202303038 Copy

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17.	Lim C G, Soh C P, Lim S S Y, et al. Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial. Child Adolesc Psychiatry Ment Health, 2023, 17(1): 15.
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21.	Ganzer P D, Colachis S C 4th, Schwemmer M A, et al. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell, 2020, 181(4): 763-773.
22.	Benabid A L, Costecalde T, Eliseyev A, et al. An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol, 2019, 18(12): 1112-1122.
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24.	Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
25.	Beauchamp M S, Oswalt D, Sun P, et al. Dynamic stimulation of visual cortex produces form vision in sighted and blind humans. Cell, 2020, 181(4): 774-783.e5.
26.	FDA. Regulatory overview for neurological devices. (2021-05-20)[2023-02-20]. https: //www.fda.gov/medical-devices/neurological-devices/regulatory-overview-neurological-devices.
27.	FDA. Regulatory Science for Neurological Devices. (2021-05-20)[2023-02-20]. https: //www.fda.gov/medical-devices/neurological-devices/regulatory-science-neurological-devices.
28.	Valeriani D, Cecotti H, Thelen A, et al. Editorial: Translational brain-computer interfaces: From research labs to the market and back. Front Hum Neurosci, 2023, 17: 1152466.
29.	Gao X, Wang Y, Chen X, et al. Interface, interaction, and intelligence in generalized brain–computer interfaces. Trends Cogn Sci, 2021, 25(8): 671-684.
30.	Mitchell P, Lee S C M, Yoo P E, et al. Assessment of safety of a fully implanted endovascular brain-computer interface for severe paralysis in 4 patients: the stentrode with thought-controlled digital switch (switch) study. JAMA Neurol, 2023, 80(3): 270-278.
31.	Rubin D B, Ajiboye A B, Barefoot L, et al. Interim safety profile from the feasibility study of the braingate neural interface system. Neurology, 2023, 100(11): e1177-e1192.

1. Farwell L A, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol, 1988, 70(6): 510-523.
2. Kennedy P R, Bakay R A, Moore M M, et al. Direct control of a computer from the human central nervous system. IEEE Trans Rehabil Eng, 2000, 8(2): 198-202.
3. Hochberg L R, Serruya M D, Friehs G M, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature, 2006, 442(7099): 164-171.
4. Flesher S N, Downey J E, Weiss J M, et al. A brain-computer interface that evokes tactile sensations improves robotic arm control. Science, 2021, 372(6544): 831-836.
5. Aflalo T, Kellis S, Klaes C, et al. Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science, 2015, 348(6237): 906-910.
6. Katzschmann R K, DelPreto J, MacCurdy R, et al. Exploration of underwater life with an acoustically controlled soft robotic fish. Sci Robot, 2018, 3(16): eaar3449.
7. Brunner C, Birbaumer N, Blankertz B, et al. BNCI Horizon 2020: towards a roadmap for the BCI community. Brain-Comput Interfa, 2015, 2(1): 1-10.
8. Xie S X, Yu Z C, Lv Z H. Multi-disease prediction based on deep learning: A survey. Comput Model Eng Sci, 2021, 128(2): 489-522.
9. Zhang C, Kim Y K, Eskandarian A. EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification. J Neural Eng, 2021, 18(4). DOI: 10.1088/1741-2552/abed81.
10. Lv Z, Qiao L, Wang Q, et al. Advanced machine-learning methods for brain-computer interfacing. IEEE/ACM Trans Comput Biol Bioinform, 2021, 18(5): 1688-1698.
11. Sadiq M T, Yu X, Yuan Z. Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces. Expert Syst Appl, 2021, 164: 114031.
12. He H, Wu D. Transfer learning for brain–computer interfaces: a euclidean space data alignment approach. IEEE Trans Biomed Eng, 2020, 67(2): 399-410.
13. 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. Lancet, 2017, 389(10081): 1821-1830.
14. Pandarinath C, Nuyujukian P, Blabe C H, et al. High performance communication by people with paralysis using an intracortical brain-computer interface. Elife, 2017, 6: e18554.
15. Lin P J, Zhai X, Li W, et al. A transferable deep learning prognosis model for predicting stroke patients’ recovery in different rehabilitation trainings. IEEE J Biomed Health Inform, 2022, 26(12): 6003-6011.
16. Wang D X, Ng N, Seger S E, et al. Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement. Cereb Cortex, 2023: bhad105.
17. Lim C G, Soh C P, Lim S S Y, et al. Home-based brain-computer interface attention training program for attention deficit hyperactivity disorder: a feasibility trial. Child Adolesc Psychiatry Ment Health, 2023, 17(1): 15.
18. Swann N C, Hemptinne C D, Miocinovic S, et al. Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson’s disease. J Neurosurg, 2018, 128(2): 605-616.
19. Yeo P S, Nguyen T N, Ng M P E, et al. Evaluation of the implementation and effectiveness of community-based brain-computer interface cognitive group training in healthy community-dwelling older adults: randomized controlled implementation trial. JMIR Form Res, 2021, 5(4): e25462.
20. Biasiucci A, Leeb R, Iturrate I, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun, 2018, 9(1): 2421.
21. Ganzer P D, Colachis S C 4th, Schwemmer M A, et al. Restoring the sense of touch using a sensorimotor demultiplexing neural interface. Cell, 2020, 181(4): 763-773.
22. Benabid A L, Costecalde T, Eliseyev A, et al. An exoskeleton controlled by an epidural wireless brain-machine interface in a tetraplegic patient: a proof-of-concept demonstration. Lancet Neurol, 2019, 18(12): 1112-1122.
23. 吴雅兰, 柯溢能, 卢绍庆. 浙江大学完成国内首例植入式脑机接口临床转化研究. (2020-01-17)[2023-02-20]. http: //www.news.zju.edu.cn/2020/0117/c24345a1957263/page.htm.
24. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
25. Beauchamp M S, Oswalt D, Sun P, et al. Dynamic stimulation of visual cortex produces form vision in sighted and blind humans. Cell, 2020, 181(4): 774-783.e5.
26. FDA. Regulatory overview for neurological devices. (2021-05-20)[2023-02-20]. https: //www.fda.gov/medical-devices/neurological-devices/regulatory-overview-neurological-devices.
27. FDA. Regulatory Science for Neurological Devices. (2021-05-20)[2023-02-20]. https: //www.fda.gov/medical-devices/neurological-devices/regulatory-science-neurological-devices.
28. Valeriani D, Cecotti H, Thelen A, et al. Editorial: Translational brain-computer interfaces: From research labs to the market and back. Front Hum Neurosci, 2023, 17: 1152466.
29. Gao X, Wang Y, Chen X, et al. Interface, interaction, and intelligence in generalized brain–computer interfaces. Trends Cogn Sci, 2021, 25(8): 671-684.
30. Mitchell P, Lee S C M, Yoo P E, et al. Assessment of safety of a fully implanted endovascular brain-computer interface for severe paralysis in 4 patients: the stentrode with thought-controlled digital switch (switch) study. JAMA Neurol, 2023, 80(3): 270-278.
31. Rubin D B, Ajiboye A B, Barefoot L, et al. Interim safety profile from the feasibility study of the braingate neural interface system. Neurology, 2023, 100(11): e1177-e1192.

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The research status and development trends of brain-computer interfaces in medicine

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