1 citations · 1 across the 4 of their papers we have counts for
4 papers
Diffusion-Guided Pretraining for Brain Graph Foundation Models
Xinxu Wei, Rong Zhou, Lifang He +1
With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connect…
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders
Xinxu Wei, Kanhao Zhao, Yong Jiao +2
As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While mo…
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
Xinxu Wei, Kanhao Zhao, Yong Jiao +3
Effectively utilizing extensive unlabeled high-density EEG data to improve performance in scenarios with limited labeled low-density EEG data presents a significant challenge. In t…
Multi-modal Cross-domain Self-supervised Pre-training for fMRI and EEG Fusion
Xinxu Wei, Kanhao Zhao, Yong Jiao +4
Neuroimaging techniques including functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) have shown promise in detecting functional abnormalities in various br…