Yuqiu Deng

EEG foundation model fine-tuning gains a plug-and-play adapter, DEFT. Pretrained embeddings previously retained substantial subject-related variation, adding burden to the downstream classifier in cross-subject settings. DEFT splits pretrained tokens into a task-oriented content branch and a subject-consistent style branch, then predicts from the content branch, using recursive Gaussian refinement, temporal-variance regularization, subject-aware contrastive learning, and reconstruction to instantiate this factorize-before-classify design. Across multiple EEG datasets and four foundation model backbones it improves matched fine-tuning baselines, and ablations support the intended content/style branch organization.

EEG foundation model fine-tuning gains a plug-and-play adapter, DEFT. Pretrained embeddings previously retained substantial subject-related variation, adding burden to the downstream classifier in cross-subject settings. DEFT splits pretrained tokens into a task-oriented content branch and a subject-consistent style branch, then predicts from the content branch, using recursive Gaussian refinement, temporal-variance regularization, subject-aware contrastive learning, and reconstruction to instantiate this factorize-before-classify design. Across multiple EEG datasets and four foundation model backbones it improves matched fine-tuning baselines, and ablations support the intended content/style branch organization.