A novel Graph Convolutional Pre-trained Contrastive Cross-Fusion (GPC-CF) framework, which integrates EEG and fMRI data within a pre-training and fine-tuning model to establish a powerful paradigm for multimodal brain signal fusion.
Rui-Zhe Zhong, Hai-Sheng Li, Min Zuo et al.· IEEE Access· 0 citations
A reproducible and explainable deep learning framework, termed the multidimensional feature adaptive gating network (MDFAG-Net), for sMRI-based AD classification and neuroimaging analysis, which demonstrated competitive classification performance and outperformed representative two-dimensional, three-dimensional, and m...
Yi-Peng Zhou, Lin Zhu, Zhen-Qiao Liu et al.· Journal of Visualized Experi...· 0 citations
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary information about brain function, and have shown significant promise in detecting functional abnormalities in various brain disorders. However, the distinct signal characteristics and disparate representational spaces of...
The Collaborative Graph Attention Multi-Task Network (CoGA-MTN), a deep learning framework that integrates multi-scale structural features to jointly predict task-based functional connectivity (FC) and identify potential disease-related network alterations, is introduced.
A self-supervised masked autoencoder pre-training on multiple public EEG datasets demonstrates that TF-JointMAE effectively improves the robustness of EEG representations, providing potential auxiliary support for AD-spectrum classification and clinical decision-making.
Min Zuo, Hong Zhu, Zhen-Qiao Liu et al.· Cognitive Neurodynamics· 0 citations
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