Depression is a serious mental disorder, and timely detection and treatment are crucial. Electroencephalography (EEG), as a noninvasive tool directly reflecting brain activity, is suitable for objective depression detection. However, due to difficulty in acquiring depression EEG data, public datasets suffer from limited sample sizes and insufficient exploitation of features, limiting current approaches in training sufficiency and generalization. To this end, this paper proposes an EEG collaborative diffusion framework (EEGCo‐Diff) to address small‐sample learning and sufficient feature utilization. Specifically, this study constructs a multicondition guided diffusion model conditioned on Riemannian manifold features and ground‐truth labels to generate high‐quality EEG samples while maintaining geometric consistency of the original distribution, effectively alleviating sample scarcity and distribution shift. Second, this study proposes a CNN interaction transformer network (CITNet) that fuses multilayer convolution and a transformer to model local details and global temporal dependencies and uses an oscillation‐aware module (OAM) to highlight key channel features. EEGCo‐Diff couples geometry‐prior‐driven data generation with structured temporal modeling, significantly improving discriminative power and generalization in small‐sample, heterogeneous settings. On two public EEG depression datasets, our method achieves accuracies of 89.44% and 95.03%, outperforming the strongest baselines by 8.34% and 1.03%, respectively, and establishing state‐of‐the‐art performance.
Huang et al. (Thu,) studied this question.