用大模型驱动多智能体，自动解析脑信号任务
BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding
- 用大模型做大脑信号分析的指挥官，分派任务给专业小助手
- 能自动完成复杂分析流程，可靠性高于传统方法
- 适合医学研究者和脑机接口开发者快速上手
脑机接口与脑信号理解对临床健康和下一代人机交互至关重要。然而，当前分析范式因缺乏自主智能而难以在真实场景中推广：一方面技术门槛高，需大量专业知识；另一方面系统僵化、任务单一，无法执行复杂的长周期工作流。为推动脑信号理解的普及，我们受大语言模型启发，提出 BrainAgent——一个由大模型驱动的多智能体框架，可将自然语言指令转化为可执行的端到端处理流程。该框架采用分层架构，由中央协调器调度多个专业子智能体完成自适应任务分解与执行。此外，我们构建了系统性评估基准，用于衡量智能体在脑信号分析中的表现。实验证明，BrainAgent能有效自动化复杂工作流，具备更高可靠性，标志着脑信号理解向普惠化迈出关键一步。
原文摘要 · Abstract (English)
Brain-Computer Interfaces (BCIs) and brain signal understanding are pivotal for clinical health and next-generation interactions. Despite this significance, its widespread adoption in real-world scenarios remains restricted, primarily because current analytical paradigms lack sufficient agentic intelligence. First, existing methodologies impose prohibitive technical barriers, requiring extensive specialized expertise. Second, they remain inherently static and task-specific, failing to execute the complex, long-horizon workflows essential for real-world deployment. To accelerate the democratization of brain signal understanding, we draw inspiration from Large Language Models (LLMs) to introduce BrainAgent, an LLM-driven multi-agent framework designed to ground abstract natural language intent into rigorous, executable, and end-to-end processing pipelines. BrainAgent employs a hierarchical architecture where a central supervisor orchestrates specialized sub-agents for adaptive task decomposition and execution. Furthermore, we establish a comprehensive, systematic benchmark for evaluating agentic systems in brain signal analysis. Empirical results demonstrate that BrainAgent effectively automates complex workflows with superior reliability, marking a paradigm shift toward democratized brain signal understanding.
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