Brain-computer interface: from lab to real scene_Journal of Biomedical Engineering

Authors：

 YAO Dezhong ^1,2,3

1. School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R.China;
2. Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu 611731, P.R.China;
3. Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu 611731, P.R.China;

Corresponding?author：

YAO Dezhong, Email: dyao@uestc.edu.cn

Keywords：

brain-computer interface; real scene application; challenges for future

DOI：

10.7507/1001-5515.202105091

Video：

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Abstract Full text Figures/Tables Video References Cited by

Brain-computer interface (BCI) can be summarized as a system that uses online brain information to realize communication between brain and computer. BCI has experienced nearly half a century of development, although it now has a high degree of awareness in the public, but the application of BCI in the actual scene is still very limited. This collection invited some BCI teams in China to report their efforts to promote BCI from laboratory to real scene. This paper summarizes the main contents of the invited papers, and looks forward to the future of BCI.

Citation： YAO Dezhong. Brain-computer interface: from lab to real scene. Journal of Biomedical Engineering, 2021, 38(3): 405-408. doi: 10.7507/1001-5515.202105091 Copy

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2.	Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng, 1980, 8(4-6): 339-349.
3.	Nowlis D P, Kamiya J. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970, 6(4): 476-484.
4.	Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157-180.
5.	堯德中. 腦-機接口: 從神奇到現實轉變. 中國生物醫學工程學報, 2014, 33(6): 641-643.
6.	Johns Hopkins University. Quadriplegic patient uses brain signals to feed himself with two advanced prosthetic arms[EB/OL]. (2020-12-29) [2021-05-28]. https: //medicalxpress. com/news/2020-12-quadriplegic-patient-brain-advanced-prosthetic. html.
7.	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.
8.	Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
9.	張力新, 陳小翠, 陳龍, 等. 基于運動想象腦-機接口的協同策略研究. 生物醫學工程學雜志, 2021, 38(3): 409-416.
10.	左詞立, 毛盈, 劉倩倩, 等. 不同復雜度漢字模式下運動想象腦機接口性能研究. 生物醫學工程學雜志, 2021, 38(3): 417-424, 454.
11.	李嘉瑩, 趙麗, 邊琰, 等. 電刺激輔助下肢運動想象特征分類以增強康復訓練研究. 生物醫學工程學雜志, 2021, 38(3): 425-433.
12.	Xie J, Peng M, Lu J, et al. Enhancement of event-related desynchronization in motor imagery based on transcranial electrical stimulation. Front Hum Neurosci, 2021, 15: 635351.
13.	田貴鑫, 丁鵬, 龔安民, 等. 腦機接口中運動想象的執行與能力的評估和提高方法. 生物醫學工程學雜志, 2021, 38(3): 434-446.
14.	汪佳衡, 王躍明, 姚林. 基于濾波器組長短時記憶網絡的腦電信號情緒識別. 生物醫學工程學雜志, 2021, 38(3): 447-454.
15.	蔡梓良, 郭苗苗, 楊新生, 等. 基于最大分類器差異域對抗方法的跨被試腦電情緒識別研究. 生物醫學工程學雜志, 2021, 38(3): 455-462.
16.	孫勁松, 肖曉琳, 孟佳圓, 等. 腦-機接口中錯誤相關電位的解碼算法研究. 生物醫學工程學雜志, 2021, 38(3): 463-472.
17.	張銳, 劉家俊, 陳明明, 等. 基于小波變換—集合經驗模態分解的單通道腦電信號眼電偽跡自動去除研究. 生物醫學工程學雜志, 2021, 38(3): 473-482.
18.	陳小剛, 李坤. 基于增強現實腦機接口和計算機視覺的機械臂控制系統. 生物醫學工程學雜志, 2021, 38(3): 483-491.
19.	黃海云, 蔡躍新, 馮學技, 等. 基于腦電信號的耳鳴患者靜息態頻譜圖及注意力研究. 生物醫學工程學雜志, 2021, 38(3): 492-497.
20.	劉蒙蒙, 徐桂芝, 于洪麗, 等. 經顱直流電刺激下腦卒中患者康復期腦功能網絡特性研究. 生物醫學工程學雜志, 2021, 38(3): 498-506, 511.
21.	高上凱, 呂寶糧, 張麗清. 腦-計算機交互研究前沿. 上海: 上海交通大學出版社, 2021.
22.	秦云, 劉鐵軍, 堯德中. 腦器交互學——腦與外界協同的新學科. 生物醫學工程學雜志, 2021, 38(3): 507-511.
23.	Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
24.	Gao D, Long S, Yang H, et al. SWS brain-wave music may improve the quality of sleep: An EEG study. Front Neurosci, 2020, 14: 67.
25.	Ma T, Li H, Yang H, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.
26.	Zhu X, Li P, Li C, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Signal Process Control, 2019, 49: 396-403.

1. Fetz E E. Operant conditioning of cortical unit activity. Science, 1969, 163(3870): 955-958.
2. Schmidt E M. Single neuron recording from motor cortex as a possible source of signals for control of external devices. Ann Biomed Eng, 1980, 8(4-6): 339-349.
3. Nowlis D P, Kamiya J. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology, 1970, 6(4): 476-484.
4. Vidal J J. Toward direct brain-computer communication. Annu Rev Biophys Bioeng, 1973, 2: 157-180.
5. 堯德中. 腦-機接口: 從神奇到現實轉變. 中國生物醫學工程學報, 2014, 33(6): 641-643.
6. Johns Hopkins University. Quadriplegic patient uses brain signals to feed himself with two advanced prosthetic arms[EB/OL]. (2020-12-29) [2021-05-28]. https: //medicalxpress. com/news/2020-12-quadriplegic-patient-brain-advanced-prosthetic. html.
7. 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.
8. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol, 2002, 113(6): 767-791.
9. 張力新, 陳小翠, 陳龍, 等. 基于運動想象腦-機接口的協同策略研究. 生物醫學工程學雜志, 2021, 38(3): 409-416.
10. 左詞立, 毛盈, 劉倩倩, 等. 不同復雜度漢字模式下運動想象腦機接口性能研究. 生物醫學工程學雜志, 2021, 38(3): 417-424, 454.
11. 李嘉瑩, 趙麗, 邊琰, 等. 電刺激輔助下肢運動想象特征分類以增強康復訓練研究. 生物醫學工程學雜志, 2021, 38(3): 425-433.
12. Xie J, Peng M, Lu J, et al. Enhancement of event-related desynchronization in motor imagery based on transcranial electrical stimulation. Front Hum Neurosci, 2021, 15: 635351.
13. 田貴鑫, 丁鵬, 龔安民, 等. 腦機接口中運動想象的執行與能力的評估和提高方法. 生物醫學工程學雜志, 2021, 38(3): 434-446.
14. 汪佳衡, 王躍明, 姚林. 基于濾波器組長短時記憶網絡的腦電信號情緒識別. 生物醫學工程學雜志, 2021, 38(3): 447-454.
15. 蔡梓良, 郭苗苗, 楊新生, 等. 基于最大分類器差異域對抗方法的跨被試腦電情緒識別研究. 生物醫學工程學雜志, 2021, 38(3): 455-462.
16. 孫勁松, 肖曉琳, 孟佳圓, 等. 腦-機接口中錯誤相關電位的解碼算法研究. 生物醫學工程學雜志, 2021, 38(3): 463-472.
17. 張銳, 劉家俊, 陳明明, 等. 基于小波變換—集合經驗模態分解的單通道腦電信號眼電偽跡自動去除研究. 生物醫學工程學雜志, 2021, 38(3): 473-482.
18. 陳小剛, 李坤. 基于增強現實腦機接口和計算機視覺的機械臂控制系統. 生物醫學工程學雜志, 2021, 38(3): 483-491.
19. 黃海云, 蔡躍新, 馮學技, 等. 基于腦電信號的耳鳴患者靜息態頻譜圖及注意力研究. 生物醫學工程學雜志, 2021, 38(3): 492-497.
20. 劉蒙蒙, 徐桂芝, 于洪麗, 等. 經顱直流電刺激下腦卒中患者康復期腦功能網絡特性研究. 生物醫學工程學雜志, 2021, 38(3): 498-506, 511.
21. 高上凱, 呂寶糧, 張麗清. 腦-計算機交互研究前沿. 上海: 上海交通大學出版社, 2021.
22. 秦云, 劉鐵軍, 堯德中. 腦器交互學——腦與外界協同的新學科. 生物醫學工程學雜志, 2021, 38(3): 507-511.
23. Yao D, Zhang Y, Liu T, et al. Bacomics: a comprehensive cross area originating in the studies of various brain-apparatus conversations. Cogn Neurodyn, 2020, 14(4): 425-442.
24. Gao D, Long S, Yang H, et al. SWS brain-wave music may improve the quality of sleep: An EEG study. Front Neurosci, 2020, 14: 67.
25. Ma T, Li H, Yang H, et al. The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing. J Neurosci Methods, 2017, 275: 80-92.
26. Zhu X, Li P, Li C, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Signal Process Control, 2019, 49: 396-403.

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