5 papers
Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis
Hairui Chen, Yanwu Yang, Jianfeng Cao +3
Brain networks exhibit a modular community structure that varies across individuals and neurological conditions. However, existing self-supervised learning (SSL) methods often over…
MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding
Guoqing Cai, Kai Zeng, Shoulin Huang +1
Deep Riemannian networks provide a powerful framework for Electroencephalography (EEG) decoding, but their practical applications are severely constrained. Accurately decoding EEG…
State-Flow Coordinated Representation for MI-EEG Decoding
Guoqing Cai, Shoulin Huang, Ting Ma
Motor Imagery (MI) Electroencephalography (EEG) signals contain two crucial and complementary types of information: state information, which captures the global context of the task…
BrainMass: Advancing Brain Network Analysis for Diagnosis with Large-scale Self-Supervised Learning
Yanwu Yang, Chenfei Ye, Guinan Su +6
Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-col…
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Jiesi Hu, Yanwu Yang, Xutao Guo +2
Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging sce…