From Static Descriptions to State Dynamics: Multi-Scale Pretraining for EEG Transfer

Electroencephalography (EEG) is a temporal signal whose task-relevant structure spans transient events and sustained rhythms. Conventional temporal models learn these features through task-specific supervision, while EEG foundation models seek broader transfer through large-scale unlabelled pretraining. Many use masked content recovery, which can exploit temporal continuity and spatial redundancy without requiring predictions of subsequent activity. Autoregressive EEG approaches introduce future prediction, but typically supervise the next token or chunk without explicitly coupling fine-resolution and longer-range forecasts from the same state. We introduce MSDE, which jointly predicts future teacher states at micro and macro scales through a causal encoder and a shared transition operator conditioned on elapsed time. With a 5.0M-parameter encoder pretrained on 2,115 hours of EEG, MSDE-10% achieves the second-best average fine-tuning rank across 20 OmniEEG-Bench tasks, behind only MSDE-Full among eight evaluated models. Matched controls favour future prediction over content recovery, and joint supervision exceeds both single-scale variants on 18 tasks. Transfer gains also extend to cross-dataset sleep staging without target adaptation. Frozen-state readouts reveal improved access to task-relevant physiological features, and component interventions link these features to classification gains. Spectrum-preserving shifts on FACED further show dependence on temporal alignment beyond whole-window power.