Structured Visual Target Learning For Cross-Subject eeg-to-image retrieval
Cross-subject EEG-to-image retrieval requires a neural represen- tation trained on source subjects to remain aligned with a visual embedding space for an unseen subject. Whereas existing methods primarily focus on the EEG side, we address this problem from the perspective of the visual target. Our approach preserves the spatial information of the Perception Encoder, converts its patch grid into a compact set of learned visual views, and aggregates them for each image with a block-structured, content-dependent router. The target is learned jointly with the EEG encoder through contrastive learning with MMD regularization across source subjects. For deployment, we propose a training-free rep...
| Authors | Salini Yadav, Taveena Lotey, Mickaël Coustaty et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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