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arXiv Machine Learning By Yubo Wang, Jingying Ma, Xinliang Zhou, Yangxuan Zhou, Jiquan Wang, Sha Zhao, Yiyuan Yang, Yi Ding, Ziyu Jia, Chenyu Liu, Cuntai Guan

ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models

Read the original on arXiv Machine Learning →

ZeroMAG is a zero‑shot multimodal adapter generation framework that extends frozen EEG foundation models to heterogeneous multimodal recordings using only unlabeled target data. It constructs a configuration‑invariant adapter and generates adapter weights in a latent space learned from source adapters, without target‑side optimization. Across six held‑out target datasets and three EFM backbones, ZeroMAG improves balanced accuracy by 7.22 percentage points over EEG‑only inference and 4.89 points over direct weight regression, approaching supervised multimodal adaptation.

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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