3 papers
cs.LG2026
DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
cs.LG2026
DLink: Distilling Layer-wise and Dominant Knowledge from EEG Foundation Models
Jingyuan Wang, Zhihao Jia, Chenyu Liu +7
EEG foundation models (EFMs) achieve strong cross-subject and cross-task generalization through large-scale pretraining and downstream fine-tuning. Through empirical analysis, we o…
cs.LG2025
Exact Fit Attention in Node-Holistic Graph Convolutional Network for Improved EEG-Based Driver Fatigue Detection
Meiyan Xu, Qingqing Chen, Duo Chen +7
EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural netw…