TAHLP: A Topology-Aware Hierarchical Latent Prediction Foundation Model for Cross-Electrode Motor Imagery

Motor imagery (MI) is a core electroencephalography (EEG) brain-computer interface paradigm, yet developing a single MI foundation model that transfers across heterogeneous electrode montages remains challenging. Generic EEG foundation models pre-trained on multi-paradigm mixtures may learn representations that do not align well with MI's frequency- and region-specific sensorimotor rhythms. Existing electrode-position encodings often rely on hand-defined channel orders or learn geometry-to-feature mappings that may not fully capture the electrode-coordinate topology. To overcome these challenges, we propose TAHLP, a Topology-Aware Hierarchical Latent Prediction foundation model for cross-electrode MI, pre-trained via hierarchical latent-prediction self-distillation, where a student predicts the multi-level cumulative residuals of an exponential moving average (EMA) teacher's continuous latent features using a weighted cosine loss. This objective is self-supervised and optimized in a single stage, requiring no codebook, negative pairs, or labels, which simplifies optimization and reduces reliance on large labeled datasets for pre-training. To explicitly model electrode-coordinate topology, we introduce interleaved 3D electrode rotary position embedding, a parameter-free rotary transform on adjacent query/key feature-dimension pairs, with angles from each electrode’s 3D scalp coordinates. By construction, attention scores depend on relative 3D electrode displacement, not absolute position or channel order/count. Extensive experiments across four public MI benchmarks demonstrate that TAHLP consistently achieves state-of-the-art performance while using less than 8.8% as much pre-training data as generic EEG foundation models do. Code and models will be publicly available.