BrainFlow: Depression Detection via Trajectory Deviation on Generative EEG Velocity Fields

Electroencephalography (EEG)-based depression detection predominantly relies on discriminative representation learning that learns decision boundaries between healthy and depressed individuals. However, substantial inter-subject variability may entangle disease-related patterns with cohort-specific variations, limiting generalization and obscuring individual deviations from healthy EEG. To address these limitations, we propose BrainFlow, a generation-based framework that learns healthy EEG velocity fields from heterogeneous datasets to establish a transferable normative reference. Yet absolute mismatch to the learned healthy dynamics can remain unreliable, since even healthy subjects may exhibit considerable residuals due to individual variability, distribution shifts, and imperfect generative modeling. We therefore introduce Trajectory Deviation, which compares unseen subjects with locally matched healthy trajectories and corrects their transport residuals using corresponding healthy references. The resulting deviations are further calibrated into physiologically interpretable Flow-Energy representations for supervised depression detection. Extensive experiments demonstrate the effectiveness of BrainFlow under subject-independent evaluation settings.