CogEEG: Cognition-Guided Hyperbolic Learning for Cross-Subject EEG–Visual Retrieval

Cross-subject EEG–visual retrieval requires learning from source subjects while generalizing to unseen subjects. Existing methods typically perform subject alignment on a global uniform representation. However, such a level-agnostic paradigm overlooks the heterogeneity across cognitive levels (i.e., different levels exhibit distinct EEG–visual correspondence patterns and varying degrees of cross-subject distribution shift), thereby limiting cross-subject generalization. To address this limitation, we propose CogEEG, a Cognition-guided hyperbolic learning framework for cross-subject EEG–visual retrieval from global uniform alignment to cognitive-level-aware adaptive alignment. Specifically, CogEEG builds multi-level EEG representations from brain-region priors and captures their hierarchy via level-specific hyperbolic geometry and geodesic cross-level interaction. These representations are further aligned with multi-level visual features to establish structured EEG–visual correspondences. Building on the hierarchical representations, CogEEG dynamically adjusts domain-adversarial alignment across cognitive levels based on visual reliability and subject discriminability, enabling level-aware cross-subject alignment. This alignment reduces level-specific subject shifts while improving generalization to unseen subjects. Extensive experiments show that CogEEG achieves superior performance on cross-subject EEG–visual retrieval.