Mind World Models: Joint Learning of Motor EEG Representations and Action-Conditioned World Dynamics

Motor decoding from electroencephalography (EEG) supports non-invasive brain–computer interfaces, yet existing methods either rely on costly cue-based trials and coarse categorical labels or use pretraining objectives that do not directly capture how movement unfolds, limiting their ability to learn transferable motor representations. To this end, we propose the Mind World Model (MWM), a framework that learns motor EEG representations from scalable naturalistic EEG and egocentric video without action annotations. MWM treats EEG observed up to the current frame as the latent action of a world model: a mind model encodes the EEG, and a world model uses it to predict the representation of a future frame, yielding a movement-grounded pretraining objective. We further introduce the Lateralization-Aware Transformer (LAT), which uses frame averaging to separate shared bilateral from side-specific lateral features, encoding hemispheric correspondence to reduce the burden of learning left–right relationships from data. MWM pretraining produces two models: a mind model for downstream motor decoding and a world model that generalizes from EEG-derived actions to real-world action signals, which also offers a scalable and flexible path to collect egocentric data for embodied AIs. Across 9 classification and 7 regression tasks, the mind model improves task-averaged balanced accuracy by 5.9% and Pearson correlation by 16.2% relative to the strongest single baseline in each task family. Compared with an inverse-dynamics latent-action baseline, the world model reduces L1 error by 23.2% within recordings and 11.0% on held-out subjects.