16 citations · 22 across the 27 of their papers we have counts for
10 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…
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…
SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
Jingying Ma, Feng Wu, Yucheng Xing +5
Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in realistic settings remains chall…
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…
LoRAP: Low-Rank Aggregation Prompting for Quantized Graph Neural Networks Training
Chenyu Liu, Haige Li, Luca Rossi
Graph Neural Networks (GNNs) are neural networks that aim to process graph data, capturing the relationships and interactions between nodes using the message-passing mechanism. GNN…
ECHO: Toward Contextual Seq2Seq Paradigms in Large EEG Models
Chenyu Liu, Yuqiu Deng, Tianyu Liu +4
Electroencephalography (EEG), with its broad range of applications, necessitates models that can generalize effectively across various tasks and datasets. Large EEG Models (LEMs) a…
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…
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
Jingying Ma, Feng Wu, Qika Lin +4
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to a…
PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease
Chenyu Liu, Luca Rossi
The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. However, existing multimodal meth…
Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery
Xinliang Zhou, Chenyu Liu, Zhisheng Chen +4
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals acr…