用大模型统一处理脑电多任务分析，自动完成从检测到报告全流程。
EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models
- 基于大语言模型调度多种工具，实现脑电分析任务的自动规划与执行。
- 在公开数据集上验证，可支持灵活、可解释的多任务脑电分析。
- 适合临床与科研场景，尤其适用于需要持续推理的复杂脑电研究。
可扩展且通用的脑活动分析对推动临床诊断和认知研究至关重要。脑电图（EEG）作为一种高时间分辨率的非侵入性技术，被广泛用于脑状态分析。然而，现有大多数EEG模型针对特定任务定制，限制了其在真实场景中多任务、连续推理的应用。本文提出EEGAgent，一个通用框架，利用大语言模型（LLMs）调度和规划多种工具，自动完成脑电相关任务。该框架具备脑电基础信息感知、时空探索、事件检测、用户交互及报告生成等核心功能。我们设计了一个包含预处理、特征提取、事件检测等工具的工具箱，已在公开数据集上验证其有效性。结果表明，EEGAgent能实现灵活且可解释的脑电分析，展现出在真实临床应用中的潜力。
原文摘要 · Abstract (English)
Scalable and generalizable analysis of brain activity is essential for advancing both clinical diagnostics and cognitive research. Electroencephalography (EEG), a non-invasive modality with high temporal resolution, has been widely used for brain states analysis. However, most existing EEG models are usually tailored for individual specific tasks, limiting their utility in realistic scenarios where EEG analysis often involves multi-task and continuous reasoning. In this work, we introduce EEGAgent, a general-purpose framework that leverages large language models (LLMs) to schedule and plan multiple tools to automatically complete EEG-related tasks. EEGAgent is capable of performing the key functions: EEG basic information perception, spatiotemporal EEG exploration, EEG event detection, interaction with users, and EEG report generation. To realize these capabilities, we design a toolbox composed of different tools for EEG preprocessing, feature extraction, event detection, etc. These capabilities were evaluated on public datasets, and our EEGAgent can support flexible and interpretable EEG analysis, highlighting its potential for real-world clinical applications.
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