BrainDuo: Dual-Domain Tokenization and Pretraining for EEG Foundation Models

Electroencephalography (EEG) foundation models use large-scale pretraining to learn a general-purpose EEG encoder that can be adapted to diverse brain-decoding tasks. Although current EEG foundation models benefit from large-scale self-supervised pretraining, learning transferable representations from high-dimensional, non-stationary EEG signals remains challenging. Existing approaches can struggle to jointly preserve time series and spectral information and capture dependencies at multiple spatiotemporal scales, due to inherent limitations in tailoring tokenizer design and pretraining strategies to the characteristics of EEG signals. To address these challenges, we introduce BrainDuo, an EEG foundation model that combines a dual-domain spectrotemporal codebook with discrete and continuous supervision to learn local and long-range dependencies among EEG tokens. Our BrainDuo features three novel components, including Duo Codebook, Duo Latent and DuoView Pretraining to learn effective, robust EEG representation. Specifically, Duo Codebook learns a shared spectrotemporal vocabulary, Duo Latent transfers both discrete codes and continuous embeddings into pretraining, and DuoView Pretraining applies cyclic and clustered attention to encode the temporal context along each channel and the topographic context among neighboring electrodes. We further incorporate brain-computer interface (BCI)-native skills into downstream tasks to help EEG foundation models exploit task-specific prior knowledge for various BCI decoding. Extensive experiments across 10 representative EEG tasks and 14 datasets demonstrate that BrainDuo outperforms strong EEG foundation baselines across general EEG benchmarks and moves closer to the models performance specialized in BCI tasks. These results highlight the potential of leveraging a hierarchical, flexible design to learn general-purpose EEG representations, demonstrating BrainDuo’s versatility as a backbone for diverse EEG decoding and BCI applications.