基于增强现实的双眼异频编码稳态视觉诱发电位脑-机接口研究

doi:10.3969/j.issn.0258-8021.2023.03.002

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Abstract In recent years, to improve the flexibility and portability of the BCI system, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) and argument reality (AR) technology are combined. However, the performance of AR-BCI based on the traditional SSVEP paradigm is generally low. AR devices can project to both eyes separately. Based on this feature, we proposed a binocular different-frequency coding SSVEP paradigm to stimulate both eyes with different frequencies. This method improved the information content of SSVEP. Fourteen subjects participated in the experiment. Task related component analysis (TRCA) algorithm was used for SSVEP recognition, and the performance of binocular different-frequency coding and binocular same-frequency coding in an AR environment were compared. The spectrum characteristics, signal-to-noise ratio, and spectrum entropy of SSVEP signals under two conditions were analyzed. The classification accuracy of binocular different-frequency coding visual stimulation with different EEG signals time lengths 1, 2, and 3 s could reach 90.9%, 93.9%, and 95.0%, respectively. The classification accuracy of binocular same-frequency coding visual stimulation with different EEG signals time lengths 1, 2, and 3 s could reach 81.1%, 87.8%, and 90.1%, respectively. When the time length was less than or equal to 1 s, the classification accuracy of binocular different-frequency coding visual stimulation was significantly higher than that of binocular same-frequency coding visual stimulation (0.5 s: t (13)=4.562, P<0.01, Cohen's d=1.219; 1 s: t (13)=2.737, P<0.05, Cohen's d=0.732). According to the results of feature analysis, there was no significant difference in the signal-to-noise ratio between the two conditions (t(13)=-1.014, P>0.05, Cohen's d=-0.271), while the spectrum entropy of binocular different-frequency coded signal was significantly higher than that of binocular same-frequency coded signal (t(13)=-2.968, P<0.05, Cohen's d=-0.793). In conclusion, the performance of the AR-BCI system could be improved by the binocular different-frequency coded stimulation.

Key words： brain-computer interface (BCI) augment reality (AR) steady state visual evoked potential (SSVEP)

Received: 23 February 2022

PACS:

R318

Corresponding Authors: ^* E-mail:clarenceke@tju.edu.cn

About author:: ^# Member, Chinese Society of Biomedical Engineering

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	Liu Peishuai
	Ke Yufeng
	Du Jiale
	Ming Dong

Cite this article:

Liu Peishuai,Ke Yufeng,Du Jiale, et al. Study of Binocular Different-Frequency Coding SSVEP-Based Augmented Reality Brain-Computer Interface[J]. Chinese Journal of Biomedical Engineering, 2023, 42(3): 266-273.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.03.002 OR http://cjbme.csbme.org/EN/Y2023/V42/I3/266

[1] Wolpaw JR, Birbaumer N, Heetderks WJ, et al. Brain-computer interface technology: a review of the first international meeting [J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(2): 164-173.
[2] Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control [J]. Clinical Neurophysiology, 2002, 113(6): 767-791.
[3] Sellers EW, Donchin E. A P300-based brain-computer interface: initial tests by ALS patients [J]. Clinical Neurophysiology, 2006, 117(3): 538-548.
[4] Zhang Yangsong, Guo Daqing, Li Fali, et al. Correlated component analysis for enhancing the performance of SSVEP-based brain-computer interface [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26(5): 948-956.
[5] Wang Haiqing, Zhang Yu, Waytowich NR, et al. Discriminative feature extraction via multivariate linear regression for SSVEP-based BCI [J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(5): 532-541.
[6] Chang MH, Baek HJ, Lee SM, et al. An amplitude-modulated visual stimulation for reducing eye fatigue in SSVEP-based brain-computer interfaces [J]. Clinical Neurophysiology, 2014, 125(7): 1380-1391.
[7] Wang Meng, Li Renjie, Zhang Ruofan, et al. A wearable SSVEP-based BCI system for quadcopter control using head-mounted device [J]. IEEE Access, 2018, 6(99): 26789-26798.
[8] Ke Yufeng, Liu Pengxiao, An Xingwei, et al. An online SSVEP-BCI system in an optical see-through augmented reality environment [J]. Journal of Neural Engineering, 2020, 17(1): 016066.
[9] Faller J, Leeb R, Pfurtscheller G, et al. Avatar navigation in virtual and augmented reality environments using an SSVEP BCI[C]// Proceedings of the 1st International Conference on Apllied Bionics and Biomechanics. Venice: ICABB, 2010: 1-4.
[10] Si-Mohammed H, Petit J, Jeunet C, et al. Towards BCI-based interfaces for augmented reality: feasibility, design and evaluation [J]. IEEE Transactions on Visualization and Computer Graphics, 2020, 26(3): 1608-1621.
[11] Wang Yao, Zhang Xiang, Li Kun, et al. Humanoid robot control system based on AR-SSVEP [C] //Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence. New York: ACM, 2020: 529-533.
[12] 陈小刚,李坤.基于增强现实脑机接口和计算机视觉的机械臂控制系统 [J]. 生物医学工程学杂志, 2021, 38(3): 483-491.
[13] Zhao Xincan, Du Yulin, Zhang Rui. A CNN-based multi-target fast classification method for AR-SSVEP [J]. Computers in Biology and Medicine, 2021, 141: 105042.
[14] 陈志凯,高上凯,高小榕. 基于SSVEP的时分式双眼竞争刺激系统 [J]. 生命科学仪器, 2013, 11 (6): 9-14.
[15] 覃玉荣,时文健,黄钰清,等.双眼双频同时刺激下大脑α波同步研究 [J]. 信号处理, 2019, 35 (1): 132-140.
[16] 孙高鹏. 稳态视觉诱发电位型脑机接口的优化的研究[D]. 南京: 东南大学, 2018.
[17] Nakanishi M, Wang Yijun, Chen Xiaogang, et al. Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis [J]. IEEE Transactions on Biomedical Engineering, 2018, 65(1): 104-112.
[18] Chen Xiaogang, Wang Yijun, Gao Shangkai, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface [J]. Journal of Neural Engineering, 2015, 12(4): 046008.
[19] Liu Bingchaun, Huang Xiaoshan, Wang Yijun, et al. BETA: a large benchmark database toward SSVEP-BCI application [J]. Frontiers in Neuroscience, 2020, 14:627.
[20] 冯静雯,赖虹宇,邓伟,等. 精神分裂症和抑郁症患者静息态脑电功率谱熵的对照研究 [J]. 中国生物医学工程学报, 2019, 38(4): 385-391.
[21] Liang Liyan, Yang Chen, Wang Yijun, et al. High-frequency SSVEP stimulation paradigm based on dual frequency modulation [C] //2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin: IEEE, 2019: 6184-6187.
[22] Shyu KK, Lee PL, Liu Yuju, et al. Dual-frequency steady-state visual evoked potential for brain computer interface [J]. Neuroscience Letters, 2010, 483(1): 28-31.