Zero-Shot Generation of the Next Moment of Brain Activity by Predicting Its Distribution

EEG is a continuous, highly stochastic signal dominated by broadband noise and non-stationary fluctuations, so a pointwise autoregressive model chases noise it cannot reliably estimate. We therefore regard brain activity as intrinsically stochastic: the next moment of EEG is governed by a probability distribution. We propose GenBrain, a generative autoregressive model of EEG that predicts this distribution directly and generates brain activity by sampling from it. Grounded in the computational-neuroscience account of EEG as rhythmic oscillations riding on an aperiodic $1/f$ background, we parameterize the distribution with Rayleigh amplitudes, von Mises phases, phase-amplitude coupling, and a $1/f$ spectral slope, so that every predicted parameter carries a neurophysiological meaning. To capture transient bursts and spikes beyond this parameterization, a dual-head design pairs distribution prediction with zero-initialized raw-signal reconstruction; both heads share a block-causal Transformer backbone and train jointly, while neurophysiological priors enter as differentiable soft regularizers. Pretrained on 14 public EEG corpora and evaluated zero-shot on 10 held-out datasets spanning six paradigms, GenBrain attains lower teacher-forced negative log-likelihood than marginal, persistence, and autoregressive baselines on all 10 datasets (32–62% relative improvement, up to 7.0 nats below the strongest baseline); probability-integral-transform residuals localize where the parameterized family's tails fall short; and autoregressive generation improves spectral fidelity on 9 of 10 datasets (PSD correlation up to 0.94) while holding amplitude calibration at or below the real–real floor on 8 of 10. GenBrain recasts the modeling of brain activity as distribution prediction, offering a novel solution to the brain's intrinsic stochasticity.