collaborators

6 papers

cs.CV2024

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…

cs.CV2024

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…

eess.IV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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)…