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（EEG）分析处于神经科学与人工智能研究的前沿，其中基础模型凭借其强大的表征能力与跨模态泛化能力，正在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格局的碎片化，表现为模型角色多样、架构不一致以及缺乏系统分类。为弥合这一差距，本研究首次提出了面向脑电图分析基础模型的全面模态分类体系，基于原生脑电图解码、脑电图-文本、脑电图-视觉、脑电图-音频以及更广泛的多模态框架的输出模态，系统性地梳理了研究进展。我们深入分析了每一类别的研究思路、理论基础与架构创新，同时强调了模型可解释性、跨领域泛化以及基于脑电图的系统在现实世界中的适用性等开放挑战。通过整合这一分散的研究领域，我们的工作不仅为未来方法学发展提供了参考框架，而且加速了脑电图基础模型向可扩展、可解释且可在线执行的解决方案的转化。