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

Abstract:EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pretrained embeddings often retain substantial subject-related variation, placing an additional burden on the downstream classifier, especially in cross-subject settings. To address this issue, we propose Disentangled EFM Fine-Tuning (DEFT), a plug-and-play adapter for EFM fine-tuning. DEFT encourages branch disentanglement of pretrained tokens into task-oriented content and subject-consistent style, then performs downstream prediction from the content branch. It instantiates this factorize-before-classify design with recursive Gaussian refinement, temporal-variance regularization, subject-aware contrastive learning, and reconstruction. Experiments across multiple EEG datasets and four EFM backbones show that DEFT improves matched fine-tuning baselines. Further ablations and analyses support the intended content/style branch organization.

Machine Learning (cs.LG)

arXiv:2610.14497 [cs.LG]

(or arXiv:2610.14497v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.14497 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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