AxisBrain: Cross-Axis Consistency for Token-Wise Residual Modulation in EEG Foundation Models

Electroencephalography (EEG) foundation models aim to learn transferable representations across tasks, subjects, and recording configurations. Recent approaches often pursue improvements through model scaling, complex pretraining pipelines, or anatomical priors. These strategies can introduce additional computational costs, training dependencies, or structural metadata requirements. We introduce AxisBrain, which uses cross-axis consistency to guide token-wise residual refinement while limiting these additional costs and requirements. At each layer, learned-query pooling across channels and temporal mean pooling within each channel produce complementary context descriptors. The sigmoid-transformed cosine similarity between their projected descriptors yields a bounded consistency score that modulates the recombined spatial and temporal attention response at each channel–time token. The 7.46M-parameter encoder is trained end to end using masked raw EEG patch reconstruction, without separately pretrained components or explicit anatomical metadata. Across ten datasets spanning seven task families, AxisBrain achieves a mean balanced accuracy of 75.67%, compared with literature-reported means of 73.25% for CBraMod and 75.42% for PATCHCODE-Large. Controlled ablations further indicate that both the learned modulation scores and their token-wise assignments contribute to performance. These results support cross-axis consistency as an effective basis for token-wise residual refinement in EEG foundation models.