用大模型分析脑电图，打通语言与大脑信号的桥梁
Large Language Models for EEG: A Comprehensive Survey and Taxonomy
- 用大模型提取脑电信号特征，实现语义理解
- 支持从脑电生成文字、图像和3D物体
- 适合做脑机接口与神经疾病诊断的研究者
大语言模型（LLMs）与脑电图（EEG）研究的融合正推动神经解码、脑机接口（BCIs）和情感计算的新方向。本文系统综述并构建了相关进展的结构化分类体系，涵盖四大领域：（1）基于LLM思想的脑电表征学习基础模型；（2）脑电到语言的解码；（3）跨模态生成，包括图像与3D物体合成；（4）临床应用与数据集管理工具。研究表明，通过微调、少样本和零样本学习适配的基于Transformer的架构，使脑电模型能够完成自然语言生成、语义解析和辅助诊断等复杂任务。本综述梳理了建模策略、系统设计与应用场景，为未来通过语言模型连接自然语言处理与神经信号分析提供基础资源。
原文摘要 · Abstract (English)
The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding, brain-computer interfaces (BCIs), and affective computing. This survey offers a systematic review and structured taxonomy of recent advancements that utilize LLMs for EEG-based analysis and applications. We organize the literature into four domains: (1) LLM-inspired foundation models for EEG representation learning, (2) EEG-to-language decoding, (3) cross-modal generation including image and 3D object synthesis, and (4) clinical applications and dataset management tools. The survey highlights how transformer-based architectures adapted through fine-tuning, few-shot, and zero-shot learning have enabled EEG-based models to perform complex tasks such as natural language generation, semantic interpretation, and diagnostic assistance. By offering a structured overview of modeling strategies, system designs, and application areas, this work serves as a foundational resource for future work to bridge natural language processing and neural signal analysis through language models.
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