WDANet: Wasserstein Distribution Inspired Dynamic Adversarial Network for EEG-Based Cross-Domain Depression Recognition

Researchers have long sought objective and quantifiable methods for recognizing depression. Electroencephalography (EEG) signals, which reflect brain activities objectively, have emerged as a promising tool for this purpose. However, the practical application of EEG signals faces significant challenges arising from distribution variability across different datasets and subjects. In addition, conventional methods often struggle to effectively capture information related to dynamic transformations in distributions. To address these issues, we propose a Wasserstein distribution-inspired dynamic adversarial network (WDANet) for EEG-based depression recognition. Specifically, WDANet includes a global discriminator that focuses on the marginal distribution of EEG features, a local discriminator that concentrates on the conditional distribution of EEG features, and a Wasserstein distribution discriminator that utilizes Wasserstein distributions derived from various processed EEG features. The experimental results show that WDANet achieved classification accuracies of 83.33%, 75.52%, 73.93%, 76.04%, and 70.94% in cross-subject, cross-dataset experiments conducted on three datasets, demonstrating its effectiveness and superiority compared to state-of-the-art methods. These results support our claim that WDANet enhances the accuracy and interpretability of depression recognition, providing insights and new research directions for the integration of neuroscience and artificial intelligence technologies.