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.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Yuqiu Deng","user_id":0},{"type":"text","value":"Muyun Jiang","user_id":0},{"type":"text","value":"Zongpeng Zhang","user_id":0},{"type":"user_nicename","value":"Mingqing Xiao","user_id":"44246"},{"type":"user_nicename","value":"Yansen Wang","user_id":"41380"},{"type":"text","value":"Peiliang Gong","user_id":0},{"type":"text","value":"Xinliang Zhou","user_id":0},{"type":"text","value":"Yi Ding","user_id":0},{"type":"text","value":"Ziyu Jia","user_id":0},{"type":"text","value":"Cuntai Guan","user_id":0},{"type":"text","value":"Chenyu 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