One-Step Latent-free EEG Generation with Mean-Field Flow

EEG datasets remain limited in size and diversity, leaving substantial waveform variation across subjects, sessions, and recording conditions underrepresented in training. Generative modeling offers a way to address this limitation, but the standard approaches for image or language generation may not transfer directly to EEG. Latent compression may discard amplitude structure, phase relationships, and transient events. Standard self-attention provides no explicit prior for shared cortical sources underlying cross-electrode correlations. Multi-step samplers require repeated inference, increasing the generation cost. In this work, we propose Mean-Field Flow (MFF), a one-step, latent-free method for raw EEG generation. MFF operates directly on raw EEG and uses mean-field token coupling to model channel and temporal interactions through separate shared low-dimensional fields. We train MFF with an average-velocity objective via ${\ell }_1$ regression, reducing sampling to a single forward pass. Across 14 public EEG benchmark datasets, MFF achieves the lowest average TS-FID, reducing it by 27.8% relative to the strongest baseline. It also improves downstream brain-computer interface (BCI) classification accuracy by 2.80 percentage points on average, and its generated samples more closely match real EEG in Hjorth parameters, skewness, and scalp topographies. We hope that this study will motivate broader exploration of generative modeling for electrophysiological signals.