EEGAtlas: Adaptive Topology Learning for Generalizable EEG Representation Learning

Electroencephalography (EEG) supports applications from brain–computer interfaces to sleep monitoring. Generalizable representations are needed to transfer across subjects and paradigms, but informative channel relations vary with neural state. Existing encoders infer these relations from content alone or impose input-invariant spatial priors. EEGAtlas introduces a Dynamic Channel Relation Atlas that derives geometric bases from encoder-slot coordinates and selects a content-conditioned top-2 mixture for each sample and temporal patch, together with a shared relation. The resulting biases augment content attention. A Spatial–Temporal–Feature Alternating Block integrates this mechanism with temporal aggregation and token-wise feature transformation. Across five subject-independent frozen-probe tasks spanning event-related potentials, motor imagery, and sleep staging, EEGAtlas outperforms pretrained REVE, LaBraM, EEGPT, and CBraMod on Balanced Accuracy, Cohen's Kappa, and task-specific F1 or AUROC. Its Balanced Accuracy gains average 2.53 percentage points over the strongest task-wise baselines and reach 3.43 points on BCIC-2A. Dynamic relations also outperform static geometric relations in all three evaluated ablations, supporting content-conditioned spatial modeling. Code is available at https://anonymous.4open.science/r/EEGAtlas-ICLR2027.