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arXiv Machine Learning By Jiacheng Zhang, Xueyi Zhang, Xin Lu, Siqi Cai, Haizhou Li

OneEEG: A Unified Signal-Visual Multimodal Language Model with Task-Aware Mixture of Projectors for Multi-Task EEG Understanding

Read the original on arXiv Machine Learning →

OneEEG is a unified signal‑visual multimodal language model designed for multi‑task EEG understanding. It jointly learns from numerical EEG data and visual EEG renderings, employing a task‑aware Mixture of Projectors to align diverse EEG features with a large language model’s embedding space. The authors introduce the OneEEG‑Instruct dataset for instruction tuning and the OneEEG‑Bench benchmark to evaluate performance, demonstrating that existing multimodal LLMs struggle with EEG inputs while OneEEG outperforms them across various tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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