IEEE Trans Neural Syst Rehabil Eng. 2023;31:1796-1806. doi: 10.1109/TNSRE.2023.3260842.
Augmented reality-based brain-computer interface (AR-BCI) system is one of the important ways to promote BCI technology outside of the laboratory due to its portability and mobility, but its performance in real-world scenarios has not been fully studied. In the current study, we first investigated the effect of ambient brightness on AR-BCI performance. 5 different light intensities were set as experimental conditions to simulate typical brightness in real scenes, while the same steady-state visual evoked potentials (SSVEP) stimulus was displayed in the AR glass. The data analysis results showed that SSVEP can be evoked under all 5 light intensities, but the response intensity became weaker when the brightness increased. The recognition accuracies of AR-SSVEP were negatively correlated to light intensity, the highest accuracies were 89.35% with FBCCA and 83.33% with CCA under 0 lux light intensity, while they decreased to 62.53% and 49.24% under 1200 lux. To solve the accuracy loss problem in high ambient brightness, we further designed a SSVEP recognition algorithm with iterative learning capability, named ensemble online adaptive CCA (eOACCA). The main strategy is to provide initial filters for high-intensity data by iteratively learning low-light-intensity AR-SSVEP data. The experimental results showed that the eOACCA algorithm had significant advantages under higher light intensities ( 600 lux). Compared with FBCCA, the accuracy of eOACCA under 1200 lux was increased by 13.91%. In conclusion, the current study contributed to the in-depth understanding of the performance variations of AR-BCI under different lighting conditions, and was helpful in promoting the AR-BCI application in complex lighting environments.
基于增强现实的脑机接口（AR-BCI）系统由于其便携性和移动性，是促进实验室外 BCI 技术发展的重要途径之一，但其实验室外场景下的性能尚未得到充分研究。在当前研究中，我们首先研究了环境亮度对 AR-BCI 性能的影响。设置了 5 种不同的光强作为实验条件，以模拟真实场景中的典型亮度，同时在 AR 眼镜中显示相同的稳态视觉诱发电位（SSVEP）刺激。数据分析结果表明，在所有 5 种光强下都可以诱发出 SSVEP，但随着亮度的增加，响应强度变弱。AR-SSVEP 的识别准确率与光强呈负相关，在 0 勒克斯光强下，FBCCA 和 CCA 的最高准确率分别为 89.35%和 83.33%，而在 1200 勒克斯下则降至 62.53%和 49.24%。为了解决高环境亮度下的准确率下降问题，我们进一步设计了一种具有迭代学习能力的 SSVEP 识别算法，命名为集成在线自适应 CCA（eOACCA）。主要策略是通过迭代学习低光强 AR-SSVEP 数据，为高强度数据提供初始滤波器。实验结果表明，eOACCA 算法在较高光强（600 勒克斯）下具有显著优势。与 FBCCA 相比，eOACCA 在 1200 勒克斯下的准确率提高了 13.91%。总之，本研究有助于深入了解 AR-BCI 在不同光照条件下的性能变化，有助于推动 AR-BCI 在复杂光照环境中的应用。
