Learning aligned EEG representations with subject-specific encoders

Abstract:Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts. We study whether task supervision and architecture alone can learn subject-aligned representations. We replace a shared EEG encoder with subject-specific encoders followed by a common classifier, and compare this hybrid model with standard EEGNet, AttentionBaseNet, and CTNet baselines with Euclidean Alignment (EA) on four motor-imagery datasets. EA improves shared encoders by recentering subject covariances, but the hybrid encoder largely internalises this role: validation-loss curves and latent-distance analyses change little when EA is removed. Subject-specific heads increase class distinctiveness and place each subject close to its own latent manifold, improving most subjects while leaving a method-sensitive subset. These results support subject-specific encoders as a learned alignment mechanism for EEG decoding and identify head selection for unseen subjects as the remaining bottleneck.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2606.16462 [cs.LG]

(or arXiv:2606.16462v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.16462 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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