aiqbench analysis
This research investigates whether architectural modifications, specifically subject-specific encoders, can inherently learn aligned representations for cross-subject EEG decoding without relying heavily on domain adaptation techniques like Euclidean Alignment. The study finds that while these hybrid models improve within-subject class separation and reduce the need for explicit alignment methods, they introduce challenges for direct parameter transfer to unseen subjects, suggesting a trade-off between representation fidelity and generalizability. Ultimately, the work demonstrates that architectural pressure alone can drive meaningful representation learning in neural decoding tasks, offering a complementary approach to standard domain adaptation strategies.
Learning aligned EEG representations with subject-specific encoders
arXiv:2606.16462v3 Announce Type: replace-cross 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 three motor-imagery datasets and one motor-execution dataset. EA improves shared encoders by recentering subject covariances, whereas the hybrid encoder reduces reliance on EA: removing EA has little effect on validation-loss dynamics or latent-space organization, and both hybrid variants consistently outperform non-aligned shared baselines. Subject-specific heads increase class distinctiveness and place each subject close to its own latent manifold while improving within-subject class separation. However, on cross-subject classification, subject-specific heads hinder direct parameter transfer to unseen subjects, motivating quantitative head selection and a brief calibration session. Although decoding gains depend on the dataset and backbone, our main findings concern that the sole use of architecture pressure promotes representation learning and alignment in a direction complementary to domain adaptation methods such as Euclidean Alignment. A per-subject low-rank adapter of only 2Cr parameters recover the full encoder's accuracy across five backbones and ranks $r=1$ to 16, so the per-subject module can be compressed by two to three orders of magnitude.
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 three motor-imagery datasets and one motor-execution dataset. EA improves shared encoders by recentering subject covariances, whereas the hybrid encoder reduces reliance on EA: removing EA has little effect on validation-loss dynamics or latent-space organization, and both hybrid variants consistently outperform non-aligned shared baselines. Subject-specific heads increase class distinctiveness and place each subject close to its own latent manifold while improving within-subject class separation. However, on cross-subject classification, subject-specific heads hinder direct parameter transfer to unseen subjects, motivating quantitative head selection and a brief calibration session. Although decoding gains depend on the dataset and backbone, our main findings concern that the sole use of architecture pressure promotes representation learning and alignment in a direction complementary to domain adaptation methods such as Euclidean Alignment. A per-subject low-rank adapter of only 2Cr parameters recover the full encoder's accuracy across five backbones and ranks $r=1$ to 16, so the per-subject module can be compressed by two to three orders of magnitude.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.16462 [cs.LG]
(or arXiv:2606.16462v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.16462 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 313 words
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