首份脑电基础模型分类综述，梳理多模态分析框架
Foundation Models for Cross-Domain EEG Analysis Application: A Survey
- 按输出模态构建脑电分析模型分类体系
- 系统归纳跨域泛化与可解释性挑战
- 适合脑机接口与神经科学研究者参考
脑电图（EEG）分析处于神经科学与人工智能研究前沿，基础模型正凭借其强大的表征能力和跨模态泛化能力重塑传统分析范式。然而，这些技术的快速兴起导致研究格局碎片化，表现为模型角色多样、架构不统一、缺乏系统分类。为此，本文首次提出面向模态的脑电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进行系统梳理。我们深入分析各类别研究思路、理论基础与架构创新，同时指出模型可解释性、跨域泛化能力以及真实场景应用等关键挑战。通过整合分散领域，本工作为未来方法开发提供参考框架，并加速脑电基础模型向可扩展、可解释、在线可用解决方案的转化。
原文摘要 · Abstract (English)
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.
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