Given the rapid development of brain–computer interfaces, emotion identification based on
EEG signals has emerged as a new study area with tremendous importance in recent years …

Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …

Convolutional neural networks (CNNs) have achieved better performance than traditional
algorithms in electroencephalogram (EEG)-based emotion recognition tasks in recent years …

Recently, the combination of neural network and attention mechanism is widely employed
for electroencephalogram (EEG) emotion recognition (EER) and has achieved remarkable …

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion
recognition based on EEG has turned into a critical branch in the field of artificial …

With the rapid development of Human-computer interaction, automatic emotion recognition
based on multichannel electroencephalography (EEG) signals has attracted much attention …

Automatic emotion recognition based on EEG is an important issue in Brain-Computer
Interface (BCI) applications. In this paper, baseline signals were taken into account to …

In recent years, deep learning (DL) techniques, and in particular convolutional neural
networks (CNNs), have shown great potential in electroencephalograph (EEG)-based …

Recently, emotion recognition technology based on electroencephalogram (EEG) signals is
widely used in areas such as human–computer interaction and disease diagnosis …

Human emotion recognition could greatly contribute to human–computer interaction with
promising applications in artificial intelligence. One of the challenges in recognition tasks is …