DualBrain: Modeling Bidirectional Spatiotemporal Dependencies in EEG Foundation Models

Electroencephalography (EEG) foundation models have recently emerged to overcome the limitations of task-specific decoders, pretraining general-purpose representations that transfer across diverse downstream tasks. However, these architectures do not explicitly maintain both spatial-to-temporal and temporal-to-spatial processing orders as separate pathways within each stage. DualBrain makes this distinction explicit and combines the resulting representations. As a result, existing models fall short of capturing both aspects of spatiotemporal dependency: the temporal evolution of spatial patterns, and the spatial distribution and interaction of temporal patterns. We introduce DualBrain, an EEG foundation model that explicitly models these bidirectional spatiotemporal dependencies through a Multi-view Joint Dynamics Encoder (MJDE), comprising a topographic dynamics branch and a temporal dynamics branch that respectively capture the temporal evolution of spatial patterns and the spatial interactions of temporal patterns, with their complementary representations adaptively integrated through gated fusion. To further ground the model in the physical geometry of the scalp, DualBrain incorporates Spherical Harmonic Positional Encoding (SHPE), which jointly represents electrode positions across both angular dimensions using spherical harmonic bases, enabling structured, montage-independent positional grounding at multiple spatial scales. We pretrain DualBrain on TUH-EEG, the largest currently available single EEG dataset, using a masked autoencoding objective, and evaluate it on 7 tasks across 11 datasets spanning heterogeneous channel configurations and recording conditions. DualBrain achieves the best average performance compared to existing EEG foundation models, with particularly strong gains on seizure detection and mental state identification. These results suggest that explicitly modeling bidirectional spatiotemporal dependencies is a key ingredient for building general-purpose EEG representations.