The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has substantially advanced human-computer interaction (HCI) technologies in the AI era. Different from traditional EEG systems, the interpretability and robustness of AI-based EEG systems are becoming particularly crucial. The interpretability clarifies the inner working mechanisms of AI models and thus can gain the trust of users. The robustness reflects the AI's reliability against attacks and perturbations, which is essential for sensitive and fragile EEG signals. Thus the interpretability and robustness of AI in EEG systems have attracted increasing attention, and their research has achieved great progress recently. However, there is still no survey covering recent advances in this field. In this paper, we present the first comprehensive survey and summarize the interpretable and robust AI techniques for EEG systems. Specifically, we first propose a taxonomy of interpretability by characterizing it into three types: backpropagation, perturbation, and inherently interpretable methods. Then we classify the robustness mechanisms into four classes: noise and artifacts, human variability, data acquisition instability, and adversarial attacks. Finally, we identify several critical and unresolved challenges for interpretable and robust AI in EEG systems and further discuss their future directions.

翻译：人工智能（AI）与脑电图（EEG）的紧密耦合显著推动了AI时代人机交互（HCI）技术的发展。与传统脑电系统不同，基于AI的脑电系统的可解释性和鲁棒性变得尤为重要。可解释性阐明了AI模型的内部工作机制，从而能够赢得用户的信任；鲁棒性则反映了AI抵御攻击和扰动的可靠性，这对敏感脆弱的脑电信号至关重要。因此，脑电系统中AI的可解释性与鲁棒性日益受到关注，其研究近年来已取得重大进展。然而，目前尚无综述全面涵盖该领域的最新进展。本文首次系统综述并总结了面向脑电系统的可解释与鲁棒AI技术。具体而言，我们首先提出可解释性的分类体系，将其分为三类：反向传播法、扰动法和内在可解释法；随后将鲁棒性机制归为四类：噪声与伪迹、个体差异、数据采集不稳定性以及对抗攻击。最后，我们识别了脑电系统中可解释与鲁棒AI面临的若干关键且未解决的挑战，并进一步探讨了其未来发展方向。