22 citations · 34 across the 14 of their papers we have counts for
15 papers
HDND: Hierarchical Dynamic Neural Decoding for Multilingual Word/Character Retrieval from Non-Invasive Brain Recordings
Yueyang Li, Shuran Chen, Wai Ting Siok +1
While deep learning has enabled language decoding from intracranial brain recordings, extending this capability to non-invasive recordings remains an unresolved challenge. Decoding…
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…
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 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.…