Subject-Coordinate Canonicalization: Learning Reusable Offsets for EEG Foundation Model Adaptation

Electroencephalography (EEG) foundation models often generalize poorly to unseen subjects, but the structure of subject-dependent variation in their representations remains unclear. Across five EEG foundation models, we compare shared class structure with subject-specific translations and low-rank deformations. Held-out prototype and sample-level reconstruction analyses identify a substantial translation-like component, motivating adaptation through a reusable subject-level offset. Estimating this offset from unlabeled data is challenging because context statistics also reflect class composition. We propose Subject-Coordinate Canonicalization (SCC), which learns to estimate an offset from a small unlabeled context set and subtracts it from disjoint query representations. A permutation-invariant estimator aggregates sample-wise offset proposals, while class-balanced supervision on source subjects reduces sensitivity to context class composition. Leave-one-subject-out response alignment further encourages the corrected representations to preserve task-relevant structure shared across subjects. At inference, the estimated offset is cached and reused, requiring neither target labels nor gradient-based parameter updates. Experiments on three EEG benchmarks spanning emotion recognition, motor imagery, and sleep staging show improvements over source-only models and the evaluated adaptation baselines across all five foundation models. These results demonstrate that learned subject-level translation correction enables effective cross-subject adaptation.