Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, People's Republic of China.
University of Chinese Academy of Sciences, Beijing, People's Republic of China.
J Neural Eng. 2021 Apr 20;18(5). doi: 10.1088/1741-2552/abf397.
. Steady-state visual evoked potential (SSVEP)-brain-computer interfaces (BCIs) can cause much visual discomfort if the users use the SSVEP-BCIs for a long time. As an alternative scheme to reduce users' visual fatigue, this study proposes a new stimulation paradigm (termed as steady state peripheral visual evoked potential, abbreviated as SSPVEP) which makes full use of peripheral vision. The electroencephalography (EEG) signals are classifiable which means this proposed stimulation paradigm can be used in BCI system with the aid of the latest hybrid signal processing approach.. Under the SSPVEP stimulation paradigm, 20 targets are mounted on 20 frequencies and other targets are set between two targets with flicker stimuli coding. In order to ensure the classification accuracy of SSPVEP signal detection under the proposed stimulation paradigm, two optimization schemes are proposed for the detection stage of the conventional ensemble task-related component analysis (ETRCA) algorithm. The first optimization scheme uses nonlinear correlation coefficient at the detection part for the first time to improve the classification accuracy of the system. The second optimization scheme usescorrection to enhance the time domain features of the SSPVEP signals, and uses Manhattan distance for the final detection.. According to the response waveforms of the EEG signals generated under the SSPVEP stimulation paradigm and the results of the questionnaire on user's comfort level to the two stimulation paradigms (SSPVEP paradigm and conventional SSVEP paradigm), the proposed stimulation paradigm brings less visual fatigue. The comparison results indicate that the proposed detection methods (ETRCA +correction + Manhattan distance, ETRCA + Spearman correlation) can greatly improve the classification accuracy compared with the individual template canonical correlation analysis method and conventional ETRCA method based on Pearson correlation.. The SSPVEP stimulation paradigm reduces users' visual fatigue via using peripheral vision, which provides a new design idea for SSVEP stimulation paradigm aimed at visual comfort.
. 稳态视觉诱发电位（SSVEP）-脑机接口（BCI）如果用户长时间使用 SSVEP-BCI，可能会引起很大的视觉不适。作为一种减少用户视觉疲劳的替代方案，本研究提出了一种新的刺激范式（称为稳态周边视觉诱发电位，简称 SSPVEP），充分利用周边视觉。可对脑电信号进行分类，这意味着该提出的刺激范式可以在混合信号处理方法的帮助下应用于 BCI 系统。. 在 SSPVEP 刺激范式下，20 个目标安装在 20 个频率上，其他目标设置在两个目标之间，闪烁刺激编码。为了确保在提出的刺激范式下 SSVEP 信号检测的分类准确性，针对传统集合任务相关成分分析（ETRCA）算法的检测阶段提出了两种优化方案。第一种优化方案首次在检测部分使用非线性相关系数，以提高系统的分类准确性。第二种优化方案使用校正来增强 SSVEP 信号的时域特征，并使用曼哈顿距离进行最终检测。. 根据在 SSPVEP 刺激范式下生成的 EEG 信号的响应波形和用户对两种刺激范式（SSPVEP 范式和传统 SSVEP 范式）舒适度的问卷结果，提出的刺激范式带来的视觉疲劳较小。比较结果表明，与基于皮尔逊相关的单个模板典型相关分析方法和传统 ETRCA 方法相比，所提出的检测方法（ETRCA+校正+曼哈顿距离，ETRCA+斯皮尔曼相关）可以大大提高分类准确性。. SSPVEP 刺激范式通过使用周边视觉减轻用户的视觉疲劳，为旨在提高视觉舒适度的 SSVEP 刺激范式提供了新的设计思路。
