68 citations · 83 across the 16 of their papers we have counts for
7 papers · 1 filter
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…
ZeroMAG: Zero-Shot Multimodal Adapter Generation for Plug-and-Play EEG Foundation Models
Yubo Wang, Jingying Ma, Xinliang Zhou +8
EEG foundation models (EFMs) capture reusable knowledge from large-scale EEG data, while many EEG recordings also include companion physiological signals that provide complementary…
EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
Runhe Zhou, Shanglin Li, Guanxiang Huang +5
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical potential…
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…