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Yu-Qiu Deng

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#machine learning Preprint Oct 2026

DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models

Disentangled EFM Fine-Tuning (DEFT) is proposed, a plug-and-play adapter for EFM fine-tuning that improves matched fine-tuning baselines and encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style.

Yu-Qiu Deng, Muyun Jiang, Zong-Peng Zhang et al. · 0 citations

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