GeoLPE: Geometry-Aware Latent Prediction for EEG Foundation Modeling

Electroencephalography (EEG) foundation models learn reusable representations across datasets and tasks, yet heterogeneous electrode montages complicate spatial transfer, while waveform reconstruction may emphasize acquisition-specific signal details. We introduce GeoLPE, a geometry-aware latent-prediction framework for EEG foundation modeling. GeoLPE combines continuous absolute spatiotemporal encoding with explicit relative inter-electrode geometry and learns target representations through structured joint-embedding prediction. We pretrain GeoLPE on 97 public EEG dataset entries and evaluate it on eight EEG datasets spanning motor imagery, event-related potentials (ERP), code-modulated visual evoked potentials (c-VEP), and cognitive workload. Among the evaluated models, GeoLPE achieves the highest cross-dataset mean balanced accuracy under all four evaluation protocols, covering cross-subject and within-subject settings with both full fine-tuning and linear probing. It reaches 69.70% under cross-subject full fine-tuning, compared with 67.54% for the strongest baseline average. The pretrained encoder is adapted to electrocorticography (ECoG) finger-trajectory prediction, achieving a mean Pearson correlation of $0.566$. Ablations support the contributions of latent prediction, auxiliary objectives, and continuous geometric modeling, while representation analyses demonstrate improved transferability of pretrained representations across participants and train–test partitions. These results provide a foundation for scalable, efficient, and generalizable EEG modeling.