An emerging discipline: brain-computer interfaces medicine_Journal of Biomedical Engineering

Authors：

CHEN Yanxiao ^1,2 , ZHANG Zhe ³ , WANG Fan ^1,2 , DING Peng ^1,2 , ZHAO Lei ^2,4 ,  FU Yunfa ^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;
2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China;
3. Faculty of Marxism, Kunming University of Science and Technology, Kunming 650500, P. R. China;
4. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding author：

FU Yunfa, Email: fyf@ynu.edu.cn

Keywords：

Brain-computer interface medicine; Medical applications of brain-computer interface; Translational medicine of brain-computer interface; Ethics for brain-computer interface medicine

DOI：

10.7507/1001-5515.202310028

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With the development of brain-computer interface (BCI) technology and its translational application in clinical medicine, BCI medicine has emerged, ushering in profound changes to the practice of medicine, while also bringing forth a series of ethical issues related to BCI medicine. BCI medicine is progressively emerging as a new disciplinary focus, yet to date, there has been limited literature discussing it. Therefore, this paper focuses on BCI medicine, firstly providing an overview of the main potential medical applications of BCI technology. It then defines the discipline, outlines its objectives, methodologies, potential efficacy, and associated translational medical research. Additionally, it discusses the ethics associated with BCI medicine, and introduces the standardized operational procedures for BCI medical applications and the methods for evaluating the efficacy of BCI medical applications. Finally, it anticipates the challenges and future directions of BCI medicine. In the future, BCI medicine may become a new academic discipline or major in higher education. In summary, this article is hoped to provide thoughts and references for the development of the discipline of BCI medicine.

Citation： CHEN Yanxiao, ZHANG Zhe, WANG Fan, DING Peng, ZHAO Lei, FU Yunfa. An emerging discipline: brain-computer interfaces medicine. Journal of Biomedical Engineering, 2024, 41(4): 641-649. doi: 10.7507/1001-5515.202310028 Copy

1.	伏云发, 王帆, 丁鹏, 等. 脑-计算机接口. 北京: 国防工业出版社, 2023: 631-646.
2.	伏云发, 郭衍龙, 张夏冰, 等. 脑-机接口—革命性的人机交互. 北京: 国防工业出版社, 2020: 52-56.
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4.	Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
5.	Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature, 2023, 620(7976): 1037-1046.
6.	Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology, 2018, 5(5): 651-663.
7.	伏云发, 杨秋红, 徐保磊, 等. 脑-机接口原理与实践. 北京: 国防工业出版社, 2017: 5-6.
8.	Hramov A E, Maksimenko V A, Pisarchik A N. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports, 2021, 918: 1-133.
9.	Almarzouki H Z, Alsulami H, Rizwan A, et al. An internet of medical things-based model for real-time monitoring and averting stroke sensors. Journal of Healthcare Engineering, 2021, 2021: 1233166.
10.	Matarasso A K, Rieke J D, White K, et al. Combined real-time fMRI and real time fNIRS brain computer interface (BCI): training of volitional wrist extension after stroke, a case series pilot study. PLoS One, 2021, 16(5): e0250431.
11.	Pais-Vieira C, Gaspar P, Matos D, et al. Embodiment comfort levels during motor imagery training combined with immersive virtual reality in a spinal cord injury patient. Frontiers in Human Neuroscience, 2022, 16: 909112.
12.	Aurucci G V, Preatoni G, Damiani A, et al. Brain-computer interface to deliver individualized multisensory intervention for neuropathic pain. Neurotherapeutics, 2023, 20(5): 1316-1329.
13.	Nenadic Z. Brain–computer interfaces for human gait restoration. Control Theory and Technology, 2021: 1-13.
14.	Colucci A, Vermehren M, Cavallo A, et al. Brain–computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not?. Neurorehabilitation and Neural Repair, 2022, 36(12): 747-756.
15.	Klein E. Ethics and the emergence of brain-computer interface medicine. Handbook of Clinical Neurology, 2020, 168: 329-339.
16.	张喆, 赵旭, 马艺昕, 等. 脑机接口技术伦理规范考量. 生物医学工程学杂志, 2023, 40(2): 358-364.
17.	Ma Y, Gong A, Nan W, et al. Personalized brain–computer interface and its applications. Journal of Personalized Medicine, 2022, 13(1): 46.
18.	伏云发, 龚安民, 南文雅. 神经反馈原理与实践. 北京: 电子工业出版社, 2021: 33-34.
19.	Andersen R A, Aflalo T. Preserved cortical somatotopic and motor representations in tetraplegic humans. Current Opinion in Neurobiology, 2022, 74: 102547.
20.	Jovanovic L I, Kapadia N, Zivanovic V, et al. Brain-computer interface-triggered functional electrical stimulation therapy for rehabilitation of reaching and grasping after spinal cord injury: a feasibility study. Spinal Cord Series and Cases, 2021, 7(1): 24.
21.	McGeady C, Vučković A, Singh Tharu N, et al. Brain-computer interface priming for cervical transcutaneous spinal cord stimulation therapy: an exploratory case study. Frontiers in Rehabilitation Sciences, 2022, 3: 896766.
22.	Banach K, Małecki M, Rosół M, et al. Brain–computer interface for electric wheelchair based on alpha waves of EEG signal. Bio-Algorithms and Med-Systems, 2021, 17(3): 165-172.
23.	Zhu M, Chen J, Li H, et al. Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network. Neural Computing and Applications, 2021, 33(20): 13965-13980.
24.	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.
25.	伏云发, 龚安民, 陈超, 等. 面向实用的脑-机接口: 缩小研究与实际应用之间的差距. 北京: 电子工业出版社, 2022: 45-47.
26.	吕晓彤, 丁鹏, 李思语, 等. 脑机接口人因工程及应用: 以人为中心的脑机接口设计和评价方法. 生物医学工程学杂志, 2021, 38(2): 210-223.
27.	Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cognitive Neurodynamics, 2023, 17(1): 105-118.
28.	人工智能医疗器械创新合作平台脑机接口研究工作组, 中国信息通信研究院云计算与大数据研究所. 脑机接口技术在医疗健康领域应用白皮书(2023年). 北京: 人工智能医疗器械创新合作平台, 2023.
29.	Vansteensel M J, Klein E, van Thiel G, et al. Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations. Journal of Neurology, 2023, 270(3): 1323-1336.
30.	Merk T, Peterson V, Lipski W J, et al. Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease. Elife, 2022, 11: e75126.
31.	Bergeron D, Iorio-Morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. Journal of Child Neurology, 2023, 38(3-4): 223-238.
32.	Klein E, Ojemann J. Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices. Journal of Neural Engineering, 2016, 13(4): 043001.
33.	Klein E, Brown T, Sample M, et al. Engineering the brain: ethical issues and the introduction of neural devices. Hastings Center Report, 2015, 45(6): 26-35.
34.	Vansteensel M J, Branco M P, Leinders S, et al. Methodological recommendations for studies on the daily life implementation of implantable communication-brain–computer interfaces for individuals with locked-in syndrome. Neurorehabilitation and Neural Repair, 2022, 36(10-11): 666-677.
35.	Xue H, Wang D, Jin M, et al. Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition. Microsystems & Nanoengineering, 2023, 9: 79.
36.	Wang Z, Shi N, Zhang Y, et al. Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces. Nature Communications, 2023, 14(1): 4213.

1. 伏云发, 王帆, 丁鹏, 等. 脑-计算机接口. 北京: 国防工业出版社, 2023: 631-646.
2. 伏云发, 郭衍龙, 张夏冰, 等. 脑-机接口—革命性的人机交互. 北京: 国防工业出版社, 2020: 52-56.
3. 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.
4. Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
5. Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature, 2023, 620(7976): 1037-1046.
6. Cervera M A, Soekadar S R, Ushiba J, et al. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology, 2018, 5(5): 651-663.
7. 伏云发, 杨秋红, 徐保磊, 等. 脑-机接口原理与实践. 北京: 国防工业出版社, 2017: 5-6.
8. Hramov A E, Maksimenko V A, Pisarchik A N. Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports, 2021, 918: 1-133.
9. Almarzouki H Z, Alsulami H, Rizwan A, et al. An internet of medical things-based model for real-time monitoring and averting stroke sensors. Journal of Healthcare Engineering, 2021, 2021: 1233166.
10. Matarasso A K, Rieke J D, White K, et al. Combined real-time fMRI and real time fNIRS brain computer interface (BCI): training of volitional wrist extension after stroke, a case series pilot study. PLoS One, 2021, 16(5): e0250431.
11. Pais-Vieira C, Gaspar P, Matos D, et al. Embodiment comfort levels during motor imagery training combined with immersive virtual reality in a spinal cord injury patient. Frontiers in Human Neuroscience, 2022, 16: 909112.
12. Aurucci G V, Preatoni G, Damiani A, et al. Brain-computer interface to deliver individualized multisensory intervention for neuropathic pain. Neurotherapeutics, 2023, 20(5): 1316-1329.
13. Nenadic Z. Brain–computer interfaces for human gait restoration. Control Theory and Technology, 2021: 1-13.
14. Colucci A, Vermehren M, Cavallo A, et al. Brain–computer interface-controlled exoskeletons in clinical neurorehabilitation: ready or not?. Neurorehabilitation and Neural Repair, 2022, 36(12): 747-756.
15. Klein E. Ethics and the emergence of brain-computer interface medicine. Handbook of Clinical Neurology, 2020, 168: 329-339.
16. 张喆, 赵旭, 马艺昕, 等. 脑机接口技术伦理规范考量. 生物医学工程学杂志, 2023, 40(2): 358-364.
17. Ma Y, Gong A, Nan W, et al. Personalized brain–computer interface and its applications. Journal of Personalized Medicine, 2022, 13(1): 46.
18. 伏云发, 龚安民, 南文雅. 神经反馈原理与实践. 北京: 电子工业出版社, 2021: 33-34.
19. Andersen R A, Aflalo T. Preserved cortical somatotopic and motor representations in tetraplegic humans. Current Opinion in Neurobiology, 2022, 74: 102547.
20. Jovanovic L I, Kapadia N, Zivanovic V, et al. Brain-computer interface-triggered functional electrical stimulation therapy for rehabilitation of reaching and grasping after spinal cord injury: a feasibility study. Spinal Cord Series and Cases, 2021, 7(1): 24.
21. McGeady C, Vučković A, Singh Tharu N, et al. Brain-computer interface priming for cervical transcutaneous spinal cord stimulation therapy: an exploratory case study. Frontiers in Rehabilitation Sciences, 2022, 3: 896766.
22. Banach K, Małecki M, Rosół M, et al. Brain–computer interface for electric wheelchair based on alpha waves of EEG signal. Bio-Algorithms and Med-Systems, 2021, 17(3): 165-172.
23. Zhu M, Chen J, Li H, et al. Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network. Neural Computing and Applications, 2021, 33(20): 13965-13980.
24. 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.
25. 伏云发, 龚安民, 陈超, 等. 面向实用的脑-机接口: 缩小研究与实际应用之间的差距. 北京: 电子工业出版社, 2022: 45-47.
26. 吕晓彤, 丁鹏, 李思语, 等. 脑机接口人因工程及应用: 以人为中心的脑机接口设计和评价方法. 生物医学工程学杂志, 2021, 38(2): 210-223.
27. Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cognitive Neurodynamics, 2023, 17(1): 105-118.
28. 人工智能医疗器械创新合作平台脑机接口研究工作组, 中国信息通信研究院云计算与大数据研究所. 脑机接口技术在医疗健康领域应用白皮书(2023年). 北京: 人工智能医疗器械创新合作平台, 2023.
29. Vansteensel M J, Klein E, van Thiel G, et al. Towards clinical application of implantable brain-computer interfaces for people with late-stage ALS: medical and ethical considerations. Journal of Neurology, 2023, 270(3): 1323-1336.
30. Merk T, Peterson V, Lipski W J, et al. Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease. Elife, 2022, 11: e75126.
31. Bergeron D, Iorio-Morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. Journal of Child Neurology, 2023, 38(3-4): 223-238.
32. Klein E, Ojemann J. Informed consent in implantable BCI research: identification of research risks and recommendations for development of best practices. Journal of Neural Engineering, 2016, 13(4): 043001.
33. Klein E, Brown T, Sample M, et al. Engineering the brain: ethical issues and the introduction of neural devices. Hastings Center Report, 2015, 45(6): 26-35.
34. Vansteensel M J, Branco M P, Leinders S, et al. Methodological recommendations for studies on the daily life implementation of implantable communication-brain–computer interfaces for individuals with locked-in syndrome. Neurorehabilitation and Neural Repair, 2022, 36(10-11): 666-677.
35. Xue H, Wang D, Jin M, et al. Hydrogel electrodes with conductive and substrate-adhesive layers for noninvasive long-term EEG acquisition. Microsystems & Nanoengineering, 2023, 9: 79.
36. Wang Z, Shi N, Zhang Y, et al. Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces. Nature Communications, 2023, 14(1): 4213.

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