Evaluate geometry-based sources beyond the reported applications

Determine whether geometry-based source distributions improve generative modeling on clinical tasks beyond the Mumtaz-MDD depression-detection task and in continuous-time diffusion models.

Background

The paper introduces a graph-Matérn source prior for flow-matching models of multi-channel signals. The prior uses sensor coordinates to construct a local graph and assigns spatially structured Gaussian variance through the graph Laplacian spectrum. Experiments show improvements in spectral fidelity and downstream classification augmentation for the evaluated EEG setting, as well as gains on MEG, intracranial EEG, and traffic-sensor data.

The authors explicitly identify two untested extensions: applying geometry-based source distributions to additional clinical tasks and evaluating them in continuous-time diffusion models. These questions remain unresolved because the reported downstream evaluation is limited to the Mumtaz-MDD classification task and the primary generative framework is flow matching rather than continuous-time diffusion.