5 citations · 7 across the 5 of their papers we have counts for
5 papers
Learning from Brain Topography: A Hierarchical Local-Global Graph-Transformer Network for EEG Emotion Recognition
Yijin Zhou, Fu Li, Yi Niu +3
Understanding how local neurophysiological patterns interact with global brain dynamics is essential for decoding human emotions from EEG signals. However, existing deep learning a…
Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach with Emotional EEG Style Transfer Network
Yijin Zhou, Fu Li, Yang Li +3
Recognizing the pivotal role of EEG emotion recognition in the development of affective Brain-Computer Interfaces (aBCIs), considerable research efforts have been dedicated to this…
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition
Yijin Zhou, Fu Li, Yang Li +5
As the key to realizing aBCIs, EEG emotion recognition has been widely studied by many researchers. Previous methods have performed well for intra-subject EEG emotion recognition.…
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
Yang Li, Ji Chen, Fu Li +7
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning, which may lead to overfitting and learned emotion features lacking generalization. In this p…
Progressive Graph Convolution Network for EEG Emotion Recognition
Yijin Zhou, Fu Li, Yang Li +6
Studies in the area of neuroscience have revealed the relationship between emotional patterns and brain functional regions, demonstrating that dynamic relationships between differe…