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20242026
most citedLEL: Lipschitz Continuity Constrained Ensemble Learning for Efficient EEG-Based Intra-subject Emotion Recognition

3 citations · 4 across the 7 of their papers we have counts for

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cs.CV2026

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…

cs.CV2026

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…

cs.CV20251 cited

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…

cs.CV20253 cited

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…

cs.CV20251 cited

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…

cs.CV2024

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…