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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2.	Graimann B, Allison B Z, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin, Heidelberg: Springer Science & Business Media, 2010.
3.	伏云發, 王帆, 丁鵬, 等. 腦-計算機接口. 北京: 國防工業出版社, 2023: 631-646.
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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.
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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.
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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.	呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.

1. 伏云發, 郭衍龍, 張夏冰, 等. 腦-機接口-革命性的人機交互. 北京: 國防工業出版, 2020.
2. Graimann B, Allison B Z, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin, Heidelberg: Springer Science & Business Media, 2010.
3. 伏云發, 王帆, 丁鵬, 等. 腦-計算機接口. 北京: 國防工業出版社, 2023: 631-646.
4. Ramsey N F, Millán J R. Brain-computer interfaces. Amsterdam: Elsevier, 2020.
5. 伏云發, 楊秋紅, 徐保磊, 等. 腦-機接口原理與實踐. 北京: 國防工業出版社, 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. 伏云發, 龔安民, 南文雅. 神經反饋原理與實踐. 北京: 電子工業出版社, 2021: 33-34.
8. Collura T F. Technical foundations of neurofeedback. New York: Routledge, 2014.
9. 伏云發, 龔安民, 陳超, 等. 面向實用的腦-機接口: 縮小研究與實際應用之間的差距. 北京: 電子工業出版社, 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. 羅建功, 丁鵬, 龔安民, 等. 腦機接口技術的應用、產業轉化和商業價值. 生物醫學工程學雜志, 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. 呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.

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An emerging major: brain-computer interface major

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