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：人工智能与脑电图技术的紧密结合在人工智能时代显著推动了人机交互技术的发展。与传统脑电系统不同，基于人工智能的脑电系统的可解释性和鲁棒性变得尤为关键。可解释性阐明了人工智能模型的内部工作机制，从而能够赢得用户的信任；鲁棒性则反映了人工智能在面对攻击和干扰时的可靠性，这对于敏感且脆弱的脑电信号至关重要。因此，脑电系统中人工智能的可解释性与鲁棒性日益受到关注，相关研究近年来取得了重大进展。然而，目前仍缺乏覆盖该领域最新进展的综述。本文首次系统综述了面向脑电系统的可解释性与鲁棒性人工智能技术。具体而言，我们首先提出了可解释性的分类体系，将其分为反向传播、扰动和本质可解释三类方法；随后将鲁棒性机制分为噪声与伪迹、人类变异性、数据采集不稳定性及对抗攻击四类。最后，我们指出了脑电系统中可解释性与鲁棒性人工智能面临的若干关键未解决挑战，并进一步探讨了其未来发展方向。