 This research paper examined the effects of ambient brightness on augmented reality-based brain-computer interfaces, ARBCs, which are used to detect electrical signals from the brain and translate them into commands. The study found that ARBCI performance decreases as ambient brightness increases and that this decrease is more pronounced at higher levels of brightness. Additionally, the researchers developed an algorithm called Ensemble Online Adaptive CCA, EOACCA, that uses iterative learning to improve ARBCI performance in high-brightness environments. This algorithm improved ARBCI performance by 13.91% over other algorithms tested in high-brightness environments. This article was authored by Rui Zhang, Li Junchao, Zeng Shinsu, and others.