Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …

The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …

Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …

Multichannel electroencephalogram (EEG) is an array signal that represents brain neural
networks and can be applied to characterize information propagation patterns for different …

Although multimodal physiological data from the central and peripheral nervous systems
can objectively respond to human emotional states, the individual differences caused by non …

Nowadays, many deep models have been presented to recognize emotions using
electroencephalogram (EEG) signals. These deep models are computationally intensive, it …

With increasing social globalization, interaction between people from different cultures has
become more frequent. However, there are significant differences in the expression and …

Large-scale pre-training has shown great potential to enhance models on downstream tasks
in vision and language. Develo** similar techniques for scalp electroencephalogram …