An emerging major: brain-computer interface major_Journal of Biomedical Engineering

Authors：

YANG Hengyuan ^1,2 , LI Tianwen ^2,3 , ZHAO Lei ^2,3 , CHEN Xiaogang ⁴ , PAN Jiahui ⁵ ,  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 Science, Kunming University of Science and Technology, Kunming 650500, P. R. China;
4. Institute of Biomedical Engineering, Chinese Academy of Medical Sciences, Tianjin 300192, P. R. China;
5. School of Artificial Intelligence, South China Normal University, Foshan, Guangdong 528225, P. R. China;

Corresponding author：

Keywords：

Brain-computer interface major; Training program; Curriculum structure; Multidisciplinary nature

DOI：

10.7507/1001-5515.202409050

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Brain-computer interface (BCI) is a revolutionizing technology that disrupts traditional human-computer interaction by establishing direct communication and control between the brain and computer, bypassing the peripheral nervous and muscular systems. With the rapid advancement of BCI technology, growing application demands, and an increasing need for specialized BCI professionals, a new academic major—BCI major—has gradually emerged. However, few studies to date have discussed the interdisciplinary nature and training framework of this emerging major. To address this gap, this paper first introduced the application demands of BCI, including the demand for BCI technology in both medical and non-medical fields. The paper also described the interdisciplinary nature of the BCI major and the urgent need for specialized professionals in this field. Subsequently, a training program of the BCI major was presented, with careful consideration of the multidisciplinary nature of BCI research and development, along with recommendations for curriculum structure and credit distribution. Additionally, the facing challenges of the construction of the BCI major were analyzed, and suggested strategies for addressing these challenges were offered. Finally, the future of the BCI major was envisioned. It is hoped that this paper will provide valuable reference for the development and construction of the BCI major.

Citation： YANG Hengyuan, LI Tianwen, ZHAO Lei, CHEN Xiaogang, PAN Jiahui, FU Yunfa. An emerging major: brain-computer interface major. Journal of Biomedical Engineering, 2024, 41(6): 1257-1264. doi: 10.7507/1001-5515.202409050 Copy

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14.	Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 2012, 485(7398): 372-375.
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16.	Ramsey N F, Crone N E. Speech-enabling brain implants pass milestones. Nature, 2023, 620(7976): 954-955.
17.	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.
18.	Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
19.	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.
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22.	Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain–computer interface. Proc Natl Acad Sci, 2015, 112(44): E6058-E6067.
23.	Zhang S, Gao X. The effect of visual stimuli noise and fatigue on steady-state visual evoked potentials. J Neural Eng, 2019, 16(5): 056023.
24.	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.
25.	Deng X, Yu Z L, Lin C, et al. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J Neural Eng, 2020, 17(4): 045005.
26.	Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
27.	Jin J, Bai G, Xu R, et al. A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces. J Neural Eng, 2024, 21(3): 036057.
28.	Yang B, Ma J, Qiu W, et al. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control, 2022, 78: 103855.
29.	Li Y, Yang B, Wang Z, et al. EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task. Front Neurosci, 2023, 17: 1133834.
30.	Kohli V, Tripathi U, Chamola V, et al. A review on Virtual Reality and Augmented Reality use-cases of Brain Computer Interface based applications for smart cities. Microprocess Microsyst, 2022, 88: 104392.
31.	Vasiljevic G A M, Cunha de Miranda L. The CoDIS taxonomy for brain-computer interface games controlled by electroencephalography. Int J Hum-Comput Int, 2024, 40(15): 3908-3935.
32.	Prapas G, Glavas K, Tzimourta K D, et al. Mind the move: Developing a brain-computer interface game with left-right motor imagery. Information, 2023, 14(7): 354.
33.	Jamil N, Belkacem A N, Ouhbi S, et al. Cognitive and affective brain–computer interfaces for improving learning strategies and enhancing student capabilities: A systematic literature review. IEEE Access, 2021, 9: 134122-134147.
34.	Borghini G, Astolfi L, Vecchiato G, et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev, 2014, 44: 58-75.
35.	Powers J C, Bieliaieva K, Wu S, et al. The human factors and ergonomics of P300-based brain-computer interfaces. Brain Sci, 2015, 5(3): 318-354.
36.	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.

1. 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, 2020.
2. Graimann B, Allison B Z, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin, Heidelberg: Springer Science & Business Media, 2010.
3. 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.
4. Ramsey N F, Millán J R. Brain-computer interfaces. Amsterdam: Elsevier, 2020.
5. 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.
6. Wolpaw J R, Wolpaw E W. Brain-computer interfaces: something new under the sun// Wolpaw J R, Wolpaw E W. Brain-computer interfaces: principles and practice. Oxford: Oxford University Press, 2012, 14: 3-12.
7. 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.
8. Collura T F. Technical foundations of neurofeedback. New York: Routledge, 2014.
9. 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.
10. Allison B Z, Dunne S, Leeb R, et al. Towards practical brain-computer interfaces: bridging the gap from research to real-world applications. Berlin, Heidelberg: Springer Science & Business Media, 2012.
11. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
12. 罗建功, 丁鹏, 龚安民, 等. 脑机接口技术的应用、产业转化和商业价值. 生物医学工程学杂志, 2022, 39(2): 405-415.
13. Chen Yanxiao, Wang Fan, Li Tianwen, et al. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces. Front Neurosci, 2024, 18: 1449208.
14. Hochberg L R, Bacher D, Jarosiewicz B, et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 2012, 485(7398): 372-375.
15. 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.
16. Ramsey N F, Crone N E. Speech-enabling brain implants pass milestones. Nature, 2023, 620(7976): 954-955.
17. 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.
18. Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
19. 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.
20. Walter W G, Cooper R, Aldridge V J, et al. Contingent negative variation: an electric sign of sensori-motor association and expectancy in the human brain. Nature, 1964, 203(4943): 380-384.
21. Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2(1): 157-180.
22. Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain–computer interface. Proc Natl Acad Sci, 2015, 112(44): E6058-E6067.
23. Zhang S, Gao X. The effect of visual stimuli noise and fatigue on steady-state visual evoked potentials. J Neural Eng, 2019, 16(5): 056023.
24. 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.
25. Deng X, Yu Z L, Lin C, et al. Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation. J Neural Eng, 2020, 17(4): 045005.
26. Pan J, Xie Q, Qin P, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain, 2020, 143(4): 1177-1189.
27. Jin J, Bai G, Xu R, et al. A cross-dataset adaptive domain selection transfer learning framework for motor imagery-based brain-computer interfaces. J Neural Eng, 2024, 21(3): 036057.
28. Yang B, Ma J, Qiu W, et al. The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network. Biomed Signal Process Control, 2022, 78: 103855.
29. Li Y, Yang B, Wang Z, et al. EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task. Front Neurosci, 2023, 17: 1133834.
30. Kohli V, Tripathi U, Chamola V, et al. A review on Virtual Reality and Augmented Reality use-cases of Brain Computer Interface based applications for smart cities. Microprocess Microsyst, 2022, 88: 104392.
31. Vasiljevic G A M, Cunha de Miranda L. The CoDIS taxonomy for brain-computer interface games controlled by electroencephalography. Int J Hum-Comput Int, 2024, 40(15): 3908-3935.
32. Prapas G, Glavas K, Tzimourta K D, et al. Mind the move: Developing a brain-computer interface game with left-right motor imagery. Information, 2023, 14(7): 354.
33. Jamil N, Belkacem A N, Ouhbi S, et al. Cognitive and affective brain–computer interfaces for improving learning strategies and enhancing student capabilities: A systematic literature review. IEEE Access, 2021, 9: 134122-134147.
34. Borghini G, Astolfi L, Vecchiato G, et al. Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci Biobehav Rev, 2014, 44: 58-75.
35. Powers J C, Bieliaieva K, Wu S, et al. The human factors and ergonomics of P300-based brain-computer interfaces. Brain Sci, 2015, 5(3): 318-354.
36. 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.

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An emerging major: brain-computer interface major

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