Latent-Space Predictive Learning for EEG Foundation Models via Geometric Modeling and Masking

EEG foundation models aim to learn general-purpose neural representations that can transfer across heterogeneous downstream tasks. However, existing models often rely heavily on end-to-end fine-tuning, while their frozen representations remain substantially less effective, indicating limited representation transferability. We identify three key limitations in current pretraining paradigms: a focus on low-level waveform reconstruction rather than transferable contextual representation learning, insufficient integration of electrode geometry into inter-channel modeling, and local information leakage under random masking. To address these limitations, we propose **EEG-GPRL**, a **G**eometry-aware **P**redictive **R**epresentation **L**earning framework that learns transferable contextual EEG representations by predicting teacher representations in latent space. EEG-GPRL incorporates Geometry-aware Attention (GeoAttn) to couple channel interactions with relative electrode geometry. We propose Spatiotemporal Gaussian Field Masking (ST-GFM) to construct correlated masked contexts to reduce local information leakage. VISReg is used to prevent representational collapse. Across nine datasets spanning six EEG decoding paradigms, EEG-GPRL achieves the highest balanced accuracy on eight datasets under both KNN and frozen probing, demonstrating strong transferability across heterogeneous tasks. Ablation studies further validate the complementary contributions of its individual components.