1 citations · 1 across the 4 of their papers we have counts for
8 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…
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…
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…
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…