MSFSNet: Multi-Source Few-Shot Adaptation Network for Cross-Subject Depression Recognition from EEG Signals

Depression is a prevalent mental disorder with severe socio-economic implications, and its early identification and intervention are crucial for mitigating disease progression. However, existing machine learning and deep learning-based approaches for depression recognition exhibit limited generalization across individuals, making them less adaptable to new subjects and restricting their practical applications. To address this issue, we propose a cross-subject depression recognition method based on Multi-Source Few-Shot Adaptation (MSFSA) using electroencephalography (EEG). The proposed method integrates multi-source domain adaptation and ensemble learning strategies. Specifically, the multi-source domain adaptation module employs an alternating training mechanism combining unsupervised domain adaptation and few-shot adaptation, reducing the model's dependency on specific subjects. Meanwhile, ensemble learning improves model robustness and stability by aggregating multiple model predictions, reducing the impact of individual model biases and enhancing classification reliability. Experiments were conducted on the public MODMA EEG dataset, comprising 53 subjects (24 patients with major depressive disorder and 29 healthy controls). With a theoretical chance level of 50% for the cross-subject classification setting, the results demonstrate that, compared with traditional machine learning methods, existing EEG-based depression recognition models, and advanced domain adaptation algorithms, leveraging the Alpha and low-Gamma band features as the key contributing factors, the proposed method achieves a significant improvement in accuracy, reaching 87.12%, which outperforms the state-of-the-art HEMAsNet (80.67%) and WDANet (70.94%) on the same dataset under the 10-fold cross-subject validation protocol. These findings indicate that the proposed approach effectively reduces subject dependency in EEG-based depression recognition and provides a promising solution for improving cross-subject adaptability.