Generating realistic brain waves by modeling how they actually flow

EEG data is scarce and sensitive, making synthetic generation valuable for research. Existing methods treat EEG generation as discrete denoising tasks, which misses the continuous, flowing nature of brain signals. Just EEG Transformer (JET) uses flow matching to model EEG as smooth trajectories through noise space, adding constraints that preserve spectral structure and signal statistics. On three benchmarks, it reduces TS-FID error by over 40% and better captures the actual structural properties of neural dynamics.