A Survey on Attention-Based Deep Learning Methods for EEG Emotion Recognition

Electroencephalography (EEG)-based emotion recognition has attracted increasing attention in recent years because of its potential applications in brain-computer interfaces, intelligent healthcare, and affective computing. With the rapid development of deep learning, attention mechanisms have gradually become an important component of EEG emotion recognition models, since they are able to improve feature representation by focusing on more informative EEG signals. This paper reviews recent attention-based deep learning methods for EEG emotion recognition, with particular attention to channel attention, temporal attention, and self-attention implemented in Transformer-based architectures. Their main ideas, representative models, as well as their advantages and limitations are discussed and compared. This paper finds that although existing studies have achieved encouraging performance, several challenges, including cross-subject variability, limited EEG datasets, and insufficient model interpretability, still remain. Future research is expected to develop more efficient and interpretable attention mechanisms while exploring larger datasets and multimodal learning frameworks, which may further improve the robustness and practical application of EEG emotion recognition systems.