TopoMontage: Topology-Preserved Cross-Montage Alignment for Universal EEG Representation Learning

Electroencephalography (EEG) pretraining across datasets remains difficult because recordings are often acquired under heterogeneous montages, with substantial variation in cap layouts, channel availability, channel ordering, and reference schemes. Such inconsistencies weaken spatial correspondence at the sensor level and limit the transferability of learned representations. We present TopoMontage, a self-supervised framework for EEG representation learning that explicitly models cross-montage correspondence through topology-preserving alignment. Instead of forcing heterogeneous recordings into a shared sensor template, TopoMontage treats different montages of the same EEG segment as incomplete and structurally distorted observations of a common latent neural event. Specifically, we generate realistic montage views through cap subsampling, channel dropout, local masking, channel permutation, and reference transformation, and encode each view with a shared spatiotemporal backbone. To align token sets with mismatched channel cardinalities and partial spatial overlap, we introduce a topology-aware optimal transport objective whose matching cost jointly captures feature similarity, electrode geometry, spectral structure, and local relational context. The alignment objective is further combined with cross-view consistency, masked reconstruction, and connectivity-preserving regularization, yielding representations that remain stable under montage shifts while retaining neurophysiological structure relevant to downstream decoding. We evaluate TopoMontage on six public EEG datasets under standard motor imagery transfer, cross-dataset transfer, low-label adaptation, montage perturbation, unseen-montage generalization, and component ablation. Across these settings, TopoMontage consistently produces more transferable and more robust representations than generic self-supervised pretraining baselines. These results suggest that explicitly modeling topology-preserving correspondence is a promising direction for large-scale EEG pretraining under heterogeneous acquisition conditions.