Study of Binocular Different-Frequency Coding SSVEP-Based Augmented Reality Brain-Computer Interface
Liu Peishuai1, Ke Yufeng1*#, Du Jiale2, Ming Dong1#
1(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China) 2(College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China)
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.
刘培帅, 柯余峰, 杜佳乐, 明东. 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[J]. 中国生物医学工程学报, 2023, 42(3): 266-273.
Liu Peishuai, Ke Yufeng, Du Jiale, Ming Dong. Study of Binocular Different-Frequency Coding SSVEP-Based Augmented Reality Brain-Computer Interface. Chinese Journal of Biomedical Engineering, 2023, 42(3): 266-273.
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