综述脑电时空分析新方法，覆盖自监督学习到生成模型
A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications
- 系统梳理自监督学习、图神经网络等新兴分析方法
- 涵盖从脑电信号生成图像/文本的前沿应用
- 适合关注脑机接口与神经科学交叉研究者
近年来，脑电图（EEG）分析在机器学习与人工智能的推动下取得显著进展。本综述聚焦于最新发展，涵盖能够增强脑信号鲁棒表征的自监督学习方法，以及图神经网络（GNN）、基础模型和大语言模型（LLMs）等新兴判别性技术。同时探讨利用EEG数据生成图像或文本的生成技术，为脑活动可视化与解读提供新视角。本文全面概述了这些前沿方法、当前应用场景及其对未来研究与临床实践的深远影响。相关文献与开源资源已整理并持续更新至：https://github.com/wpf535236337/LLMs4TS
原文摘要 · Abstract (English)
In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine learning and artificial intelligence. This survey aims to encapsulate the latest developments, focusing on emerging methods and technologies that are poised to transform our comprehension and interpretation of brain activity. We delve into self-supervised learning methods that enable the robust representation of brain signals, which are fundamental for a variety of downstream applications. We also explore emerging discriminative methods, including graph neural networks (GNN), foundation models, and large language models (LLMs)-based approaches. Furthermore, we examine generative technologies that harness EEG data to produce images or text, offering novel perspectives on brain activity visualization and interpretation. The survey provides an extensive overview of these cutting-edge techniques, their current applications, and the profound implications they hold for future research and clinical practice. The relevant literature and open-source materials have been compiled and are consistently being refreshed at \url{https://github.com/wpf535236337/LLMs4TS}
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