2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
HEEGNet: Hyperbolic Embeddings for EEG
Shanglin Li, Shiwen Chu, Okan Koç +4
Electroencephalography (EEG)-based brain-computer interfaces facilitate direct communication with a computer, enabling promising applications in human-computer interactions. Howeve…
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…
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…
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…