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…
Spatio-Temporal Progressive Attention Model for EEG Classification in Rapid Serial Visual Presentation Task
Yang Li, Wei Liu, Tianzhi Feng +6
As a type of multi-dimensional sequential data, the spatial and temporal dependencies of electroencephalogram (EEG) signals should be further investigated. Thus, in this paper, we…
Adaptive Progressive Attention Graph Neural Network for EEG Emotion Recognition
Tianzhi Feng, Chennan Wu, Yi Niu +5
In recent years, numerous neuroscientific studies demonstrate that specific areas of the brain are connected to human emotional responses, with these regions exhibiting variability…
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…
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition
Yang Li, Boxun Fu, Fu Li +2
The existed methods for electroencephalograph (EEG) emotion recognition always train the models based on all the EEG samples indistinguishably. However, some of the source (trainin…