6 citations · 12 across the 10 of their papers we have counts for
11 papers
Bridging scalp and intracranial EEG in BCI via pretrained neural representations and geometric constraint embedding
Yihang Dong, Changhong Jing, Shuqiang Wang
Electroencephalography (EEG) has become one of the key modalities underpinning brain-computer interfaces (BCIs) due to its high temporal resolution, rapid responsiveness, non-invas…
Aligning Shared and Routed Experts for Cross-Subject EEG Generalization
Zhi Zhang, Yan Liu, Zhejing Hu +7
Cross-subject EEG generalization is challenging due to substantial heterogeneity across subjects. Existing methods typically learn either a shared subject-invariant model or multip…
PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Changhong Jing, Yan Liu, Shuqiang Wang +5
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the…
Brain Network Analysis Based on Fine-tuned Self-supervised Model for Brain Disease Diagnosis
Yifei Tang, Hongjie Jiang, Changhong Jing +2
Functional brain network analysis has become an indispensable tool for brain disease analysis. It is profoundly impacted by deep learning methods, which can characterize complex co…
Generative AI Enables Structural Brain Network Construction from fMRI via Symmetric Diffusion Learning
Qiankun Zuo, Bangjun Lei, Wanyu Qiu +3
Mapping from functional connectivity (FC) to structural connectivity (SC) can facilitate multimodal brain network fusion and discover potential biomarkers for clinical implications…
BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease
Tong Zhou, Chen Ding, Changhong Jing +8
The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain stru…
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
Changwei Gong, Changhong Jing, Ye Li +3
The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits dev…
Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction
Yongcheng Zong, Changhong Jing, Qiankun Zuo
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains…
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
Junren Pan, Shuqiang Wang
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied…
Feature-selected Graph Spatial Attention Network for Addictive Brain-Networks Identification
Changwei Gong, Changhong Jing, Junren Pan +1
Functional alterations in the relevant neural circuits occur from drug addiction over a certain period. And these significant alterations are also revealed by analyzing fMRI. Howev…
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning
Changwei Gong, Changhong Jing, Yanyan Shen +1
Dynamic community detection has been prospered as a powerful tool for quantifying changes in dynamic brain network connectivity patterns by identifying strongly connected sets of n…