GRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling
This study addresses the challenge of disentangling graph-structural noise from deterministic contributions in predicting time-varying EEG functional connectivity by proposing a graph-structured residual flow framework. The method decouples conditional mean prediction from stochastic residual transport, pioneering the integration of graph Gaussian sources into rectified flows while explicitly distinguishing transport time from physical time. By encoding dependencies via Laplacian-basis covariance and combining conditional flow matching with graph neural networks, it enables effective dynamic modeling. Furthermore, this work establishes control conditions that differentiate valid residual transport from deterministic improvements, precisely quantifying the value of residual transport and significantly enhancing the interpretability of distributional predictions.