22 citations · 57 across the 18 of their papers we have counts for
19 papers
SUP-MCRL: Subject-aware Unified Pseudo-feature Coded Multimodal Contrastive Representation Learning for EEG Visual Decoding
Shengyu Gong, Weiming Zeng, Yueyang Li +4
Non-invasive brain-computer interfaces exhibit significant performance degradation when moving from controlled laboratory stimuli to real-world natural images. This degradation occ…
TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution
Zijian Kang, Weiming Zeng, Yueyang Li +4
Low-density EEG is more suitable for wearable and IoT-based brain sensing, but sparse electrode sampling often lacks sufficient spatial information to characterize cross-regional n…
STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding
Jiahe Meng, Weiming Zeng, Yueyang Li +5
Electroencephalography (EEG) visual decoding remains challenging due to the modality gap between low-SNR neural signals and highly structured vision--language spaces, making direct…
Region-aware Spatiotemporal Modeling with Collaborative Domain Generalization for Cross-Subject EEG Emotion Recognition
Weiwei Wu, Yueyang Li, Yuhu Shi +7
Cross-subject EEG-based emotion recognition (EER) remains challenging due to strong inter-subject variability, which induces substantial distribution shifts in EEG signals, as well…
FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition
Yueyang Li, Shengyu Gong, Weiming Zeng +2
Electroencephalography (EEG) serves as a reliable and objective signal for emotion recognition in affective brain-computer interfaces, offering unique advantages through its high t…
LEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition
Shengyu Gong, Yueyang Li, Zijian Kang +6
Accurate and efficient recognition of emotional states is critical for human social functioning, and impairments in this ability are associated with significant psychosocial diffic…
Hypergraph Multi-Modal Learning for EEG-based Emotion Recognition in Conversation
Zijian Kang, Yueyang Li, Shengyu Gong +6
Emotional Recognition in Conversation (ERC) is valuable for diagnosing health conditions such as autism and depression, and for understanding the emotions of individuals who strugg…
Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Yueyang Li, Lei Chen, Wenhao Dong +9
Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding, and integrating multimodal neur…
STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosis
Wenhao Dong, Yueyang Li, Weiming Zeng +4
Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity dis…
Neural-MCRL: Neural Multimodal Contrastive Representation Learning for EEG-based Visual Decoding
Yueyang Li, Zijian Kang, Shengyu Gong +5
Decoding neural visual representations from electroencephalogram (EEG)-based brain activity is crucial for advancing brain-machine interfaces (BMI) and has transformative potential…
Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications
Lei Wang, Weiming Zeng, Kai Long +5
Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring e…
EEG Emotion Copilot: Optimizing Lightweight LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation
Hongyu Chen, Weiming Zeng, Chengcheng Chen +9
In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged…
A novel brain registration model combining structural and functional MRI information
Baolong Li, Yuhu Shi, Lei Wang +2
Although developed functional magnetic resonance imaging (fMRI) registration algorithms based on deep learning have achieved a certain degree of alignment of functional area, they…
STANet: A Novel Spatio-Temporal Aggregation Network for Depression Classification with Small and Unbalanced FMRI Data
Wei Zhang, Weiming Zeng, Hongyu Chen +6
Accurate diagnosis of depression is crucial for timely implementation of optimal treatments, preventing complications and reducing the risk of suicide. Traditional methods rely on…
Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas
Yu Feng, Weiming Zeng, Yifan Xie +8
Background: Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions…
A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directions
Yueyang Li, Weiming Zeng, Wenhao Dong +8
Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and cli…
MHNet: Multi-view High-order Network for Diagnosing Neurodevelopmental Disorders Using Resting-state fMRI
Yueyang Li, Weiming Zeng, Wenhao Dong +6
Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN)…
You Only Acquire Sparse-channel (YOAS): A Unified Framework for Dense-channel EEG Generation
Hongyu Chen, Weiming Zeng, Luhui Cai +6
High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often impeded by the costliness and lack of portability of equipment. In contrast, generating dens…
MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction
Luhui Cai, Weiming Zeng, Hongyu Chen +7
Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data.…