OneEEG：统一数值与视觉 EEG 表示的多任务脑电大模型

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Abstract:Electroencephalography (EEG) is widely used in clinical assessment, brain-computer interfaces, and cognitive-state analysis, yet EEG modeling remains fragmented across task paradigms. Recent multimodal large language models (MLLMs) provide a promising interface for flexible multimodal reasoning, but their ability to understand EEG inputs remains limited. In this work, we propose OneEEG, a unified EEG-MLLM framework for multi-task EEG understanding. By training on our curated OneEEG-Instruct, a multi-task EEG instruction-tuning dataset, OneEEG jointly models numerical EEG representations and visual EEG representations, and introduces a task-aware Mixture of Projectors (MoP) to align heterogeneous EEG features across different task families with the LLM embedding space. We further establish OneEEG-Bench, a benchmark for evaluating current MLLMs and our method on multi-task EEG understanding. Experiments show that existing MLLMs have limited ability to interpret EEG inputs, while OneEEG achieves stronger performance by integrating numerical and visual EEG representations with task-aware EEG-to-LLM projection. Extensive ablations and analyses further reveal the effects of visual EEG renderings, MoP variants, router behavior, and feature dispersion across EEG tasks.

Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2610.35355 [cs.LG]
	(or arXiv:2610.35355v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.35355 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Jiacheng Zhang [view email]
[v1] Mon, 26 Oct 2026 12:46:40 UTC (2,417 KB)