TopoJEPA: Topology-Grounded Joint-Embedding Prediction for EEG Foundation Models

Self-supervised EEG foundation models based on masked autoencoding have shown strong potential for learning transferable representations from large-scale unlabeled data. However, reconstruction objectives inherently emphasize electrode-level signal details, which may not constitute the most transferable structure for downstream EEG decoding. This raises a fundamental question: what should an EEG model predict to capture the intrinsic organization of neural dynamics? We introduce TopoJEPA, a Topology-Grounded Joint-Embedding Predictive Architecture built on the principle of Grounding Before Prediction. Instead of directly predicting raw signals or unconstrained latent representations, TopoJEPA first constructs a target space aligned with EEG's intrinsic spatiotemporal organization and then learns predictive dynamics within this space. Specifically, Topological Target Grounding (TTG) maps heterogeneous electrode configurations onto a unified hierarchical topology and learns hemisphere- and region-level representations through reconstruction. To prevent reconstruction-specific signal details from dominating the learned target space, a Reconstruction-Specific Branch isolates such residual information in an auxiliary pathway while retaining reconstruction fidelity. Building on the grounded representations established by TTG, Topology-Grounded Autoregressive Prediction (TGAP) then models their spatiotemporal evolution in joint-embedding space. Across ten datasets covering six representative EEG decoding tasks, TopoJEPA achieves competitive performance against existing self-supervised and EEG foundation-model baselines, with particularly strong linear-probing performance across multiple tasks. These results demonstrate the effectiveness of constructing a stable, topology-grounded target space before predictive learning, enabling transferable EEG representations that capture meaningful spatiotemporal structure while reducing reliance on reconstruction-specific signal details. Code is available at https://anonymous.4open.science/r/TopoJEPA-F8B3.