68 citations · 76 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
EEG-DLite: Dataset Distillation for Efficient Large EEG Model Training
Yuting Tang, Weibang Jiang, Shanglin Li +5
Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable…
LEAF: Language-EEG Aligned Foundation Model for Brain-Computer Interfaces
Muyun Jiang, Shuailei Zhang, Zhenjie Yang +9
Recent advances in electroencephalography (EEG) foundation models, which capture transferable EEG representations, have greatly accelerated the development of brain-computer interf…
BrainPro: Process-conditioned Pretraining with Topographic Retrieval for Large EEG Models
Yi Ding, Muyun Jiang, Weibang Jiang +6
EEG foundation models are pretrained on heterogeneous corpora, but two challenges remain underexplored: (1) experimental protocols provide mental-process cues that are rarely used…
EmT: A Novel Transformer for Generalized Cross-subject EEG Emotion Recognition
Yi Ding, Chengxuan Tong, Shuailei Zhang +4
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial a…