Learning aligned EEG representations with subject-specific encoders
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 sha
Record details
Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026
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ethics.ai (15 June 2026), “Learning aligned EEG representations with subject-specific encoders,” evidence record 965, https://ethics.ai/record/965 (originally published by arXiv).
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