BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding

2026-06-24

AI Summary

This work addresses the high technical barriers and limited generalizability of existing brain signal analysis methods, which hinder their application in complex, long-duration real-world tasks. To overcome these limitations, the authors propose a large language model–driven hierarchical multi-agent framework that enables intent-driven, end-to-end automated analysis through semantic alignment between natural language instructions and brain signal processing pipelines. The framework features modular sub-agents with a coordination mechanism that supports adaptive task decomposition and execution. Additionally, the study introduces the first agent-based benchmark specifically designed for evaluating brain signal analysis systems. Experimental results demonstrate that the proposed approach significantly outperforms current methods in both reliability and generalizability, thereby advancing the democratization of brain signal analysis.

Abstract

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.

Problem

• Brain-Computer Interfaces
• brain signal understanding
• agentic intelligence
• technical barriers
• long-horizon workflows

Innovation

• Large Language Model
• Multi-Agent Framework
• Brain-Computer Interface
• Autonomous Signal Processing
• Agentic Intelligence