Advances in EEG-based emotion recognition: Challenges, methodologies, and future directions

Emotion recognition plays a pivotal role in affective computing and human-computer interaction, especially in the fields of mental health care, auxiliary medicine, and intelligent system design. As a non-invasive and time-sensitive neural signal, electroencephalogram (EEG) has become an important means of emotion recognition research. However, due to its susceptibility to noise and individual differences, EEG-based emotion recognition still faces major challenges. This review systematically summarizes the latest progress in EEG-based emotion recognition, sorts out the research paradigm of EEG-based emotion recognition, including public datasets, signal preprocessing techniques, feature extraction methods, and recognition models, and focuses on the end-to-end modeling advantages of deep learning methods in this field in recent years. Through a comparative analysis of representative literature, this study concludes that although deep learning models have promoted the development of this field, their generalization ability, interpretability, and applicability in real-world scenarios are still limited. In addition, current EEG datasets are often limited by small sample size, lack of diversity, and inconsistent labeling standards. In summary, future research should focus on cross-subject recognition techniques, small sample learning strategies, and the development of real-time, deployable emotion recognition systems. These directions are expected to bridge the gap between academic research and practical applications and further promote the advancement of EEG-based emotion recognition technology.