Research progress and prospect of collaborative brain-computer interface for group brain collaboration_Journal of Biomedical Engineering

Authors：

ZHANG Lixin ¹ , CHEN Xiaocui ¹ ,  CHEN Long ² , GU Bin ¹ , WANG Zhongpeng ¹ , MING Dong ^1,2

1. Biomedical Engineering, School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, P.R.China;
2. Biomedical Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, P.R.China;

Corresponding author：

CHEN Long, Email: cagor@tju.edu.cn

Keywords：

collaborative brain-computer interface; motor imagery; feature fusion; decision fusion; group size

DOI：

10.7507/1001-5515.202007059

Video：

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Abstract Full text Figures/Tables Video References Cited by

As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31% vs. 56.34%), and was significantly higher than that of the average single user (77.31% vs. 44.90%). The research in this paper proves that the cBCI collaboration strategy can effectively improve the MI-BCI classification performance, which lays the foundation for MI-cBCI research and its future application.

Citation： ZHANG Lixin, CHEN Xiaocui, CHEN Long, GU Bin, WANG Zhongpeng, MING Dong. Research progress and prospect of collaborative brain-computer interface for group brain collaboration. Journal of Biomedical Engineering, 2021, 38(3): 409-416. doi: 10.7507/1001-5515.202007059 Copy

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21.	陶学文, 奕伟波, 陈龙, 等. 复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法. 机械工程学报, 2019, 55(11): 131-137.
22.	陈龙, 张磊, 王仲朋, 等. 功能性电刺激对运动想象皮层活动的影响研究. 仪器仪表学报, 2019, 40(2): 75-81.
23.	Valeriani D, Poli R, Cinel C. Enhancement of group perception via a collaborative brain-computer interface. IEEE Trans Biomed Eng, 2016, 64(6): 1238-1248.
24.	Kurvers R H J M, Herzog S M, Hertwig R, et al. Boosting medical diagnostics by pooling independent judgments. Proceedings of the National Academy of Sciences, 2016, 113(31): 8777-8782.

1. Clerc M. Brain computer interfaces, principles and practise. Biomed Eng Online, 2013, 12(1): 1-4.
2. 赵欣, 陈志堂, 王坤, 等. 运动想象脑-机接口新进展与发展趋势. 中国生物医学工程学报, 2019, 38(1): 84-93.
3. Scherer R, Müller G R, Neuper C, et al. An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate. IEEE Trans Biomed Eng, 2004, 51(6): 979-984.
4. Carlson T, Millan J D R. Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Autom Mag, 2013, 20(1): 65-73.
5. Meng Jianjun, Zhang Shuying, Bekyo A, et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci Rep, 2016, 6(1): 1-15.
6. 郑玉甫, 许敏鹏, 明东. 脑-机接口操控效果差异及其预测研究综述. 中国生物医学工程学报, 2018, 37(6): 749-755.
7. Kang E S, Kim B C, Suk H I. An empirical suggestion for collaborative learning in motor imagery-based BCIs// 2016 4th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2016: 1-3.
8. Zhou Yijie, Gu Bin, Dai Tingfei, et al. A multiuser collaborative strategy for MI-BCI system// 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP). Shanghai: IEEE, 2018: 1-5.
9. 尹锐, 曹勇, 姜劲, 等. 基于运动想象脑-机接口的外骨骼控制系统研究. 航天医学与医学工程, 2020, 33(3): 24-29.
10. 陈睿, 伏云发. 基于 EEG 握力变化及想象单次识别研究. 南京大学学报(自然科学), 2020, 56(2): 159-166.
11. 骆金晨, 姜月, 胡秀枋, 等. 基于多特征融合的多分类运动想象脑电信号识别研究. 生物信息学, 2020, 18(3): 176-185.
12. McGeady C, Vuckovic A, Puthusserypady S. A hybrid MI-SSVEP based brain computer interface for potential upper limb neurorehabilitation: a pilot study// 2019 7th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2019: 1-6.
13. Ko L W, Ranga S S K, Komarov O, et al. Development of single-channel hybrid BCI system using motor imagery and SSVEP. J Healthc Eng, 2017(2017): 1-7.
14. Yi Weibo, Qiu Shuang, Wang Kun, et al. Enhancing performance of a motor imagery based brain-computer interface by incorporating electrical stimulation-induced SSSEP. J Neural Eng, 2016, 14(2): 026002.
15. Kim S K, Kim L. Identifying error features in a MI-BCI system using microstates// 2020 8th International Winter Conference on Brain-Computer Interface (BCI). Gangwon: IEEE, 2020: 1-3.
16. Wu Xiaopei, Zhou Bangyan, Lv Zhao, et al. To explore the potentials of independent component analysis in brain-computer interface of motor imagery. IEEE J Biomed Health, 2020, 24(3): 775-787.
17. Lee D Y, Jeong J H, Shim K H, et al. Decoding movement imagination and execution from EEG signals using BCI-transfer learning method based on ration network// ICASSP 2020—2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona: IEEE, 2020: 1354-1358.
18. Matran-Fernandez A, Poli R. Collaborative brain-computer interfaces for target localisation in rapid serial visual presentation// 2014 6th Computer Science and Electronic Engineering Conference (CEEC). Colchester: IEEE, 2014: 127-132.
19. Bhattacharyya S, Valeriani D, Cinel C, et al. Collaborative brain-computer interfaces to enhance group decisions in an outpost surveillance task// 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin: IEEE, 2019: 3099-3102.
20. Wang Yijun, Jung T-P. A collaborative brain-computer interface for improving human performance. PloS ONE, 2011, 6(5): 1-6.
21. 陶学文, 奕伟波, 陈龙, 等. 复合肢体想象动作脑-机接口中黎曼核支持向量机递归特征筛选方法. 机械工程学报, 2019, 55(11): 131-137.
22. 陈龙, 张磊, 王仲朋, 等. 功能性电刺激对运动想象皮层活动的影响研究. 仪器仪表学报, 2019, 40(2): 75-81.
23. Valeriani D, Poli R, Cinel C. Enhancement of group perception via a collaborative brain-computer interface. IEEE Trans Biomed Eng, 2016, 64(6): 1238-1248.
24. Kurvers R H J M, Herzog S M, Hertwig R, et al. Boosting medical diagnostics by pooling independent judgments. Proceedings of the National Academy of Sciences, 2016, 113(31): 8777-8782.

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Research progress and prospect of collaborative brain-computer interface for group brain collaboration

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