Electroencephalography (EEG) analysis stands at the forefront of neuroscience and artificial intelligence research, where foundation models are reshaping the traditional EEG analysis paradigm by leveraging their powerful representational capacity and cross-modal generalization. However, the rapid proliferation of these techniques has led to a fragmented research landscape, characterized by diverse model roles, inconsistent architectures, and a lack of systematic categorization. To bridge this gap, this study presents the first comprehensive modality-oriented taxonomy for foundation models in EEG analysis, systematically organizing research advances based on output modalities of the native EEG decoding, EEG-text, EEG-vision, EEG-audio, and broader multimodal frameworks. We rigorously analyze each category's research ideas, theoretical foundations, and architectural innovations, while highlighting open challenges such as model interpretability, cross-domain generalization, and real-world applicability in EEG-based systems. By unifying this dispersed field, our work not only provides a reference framework for future methodology development but accelerates the translation of EEG foundation models into scalable, interpretable, and online actionable solutions.

翻译：脑电图分析处于神经科学与人工智能研究的前沿，其中基础模型正凭借其强大的表征能力与跨模态泛化特性，重塑传统的脑电图分析范式。然而，这些技术的快速扩散导致了研究版图的碎片化，表现为模型角色多样、架构缺乏统一以及系统性分类缺失。为弥合这一鸿沟，本研究首次提出了面向模态的脑电图分析基础模型综合分类体系，依据原生脑电解码、脑电-文本、脑电-视觉、脑电-音频及更广泛的多模态框架的输出模态，系统梳理了研究进展。我们深入剖析了各类别的研究思路、理论基础与架构创新，同时着重指出了当前面临的开放挑战，包括模型可解释性、跨领域泛化能力以及基于脑电系统的实际应用可行性。通过整合这一分散的研究领域，本工作不仅为未来方法论发展提供了参考框架，更将推动脑电图基础模型向可扩展、可解释且支持在线部署的解决方案加速转化。