Research Progress on Artificial Intelligence and Brain–Computer Interfaces in the Emotion Recognition of Patients with Depression

Depression represents a significant public health concern on a global scale. Conventional diagnostic approaches rely on subjective scales, which have limitations, including low consistency and difficulty in early identification. In recent years, the integration of artificial intelligence (AI) and brain‒computer interface (BCI) technologies has led to the development of new solutions for mood recognition in patients with depression. This review examines emotion recognition in depression via multimodal data, including EEG signals, eye tracking, and facial expression analysis. This highlights the application and performance of deep learning methods, such as the EEGNet and LSTM-CNN parallel models. The study also discusses multimodal fusion techniques, including graph neural networks and dynamic weighted fusion. Research indicates that AI and BCI technologies enable objective quantification, real-time monitoring, and personalised intervention. These capabilities significantly increase the accuracy and robustness of depression recognition. Nevertheless, this field continues to face challenges, including ethical controversies and the limited generalizability of models. Future work should prioritise algorithmic improvements, facilitate clinical translation, and strengthen ethical frameworks to support broader implementation.