The modeling hypothesis, supported by retrieval behavior, suggests that computational representations aligning with salient and structurally meaningful content better match macroscopic neural responses than fine-grained image details.
This work introduced Dynamic Cognitive Prior Generation (DCPG), a new framework, which can be learned and is prototype based to adaptively generate priors instead of heuristic augmentations, and can be used to significantly increase the accuracy of inter-subject retrieval on the THINGS-EEG dataset with an accuracy impr...
By aligning EEG with layer-wise neural visibility rather than fixed high-level semantics, the proposed framework improves both retrieval accuracy and image reconstruction in EEG-based visual decoding.
BrainFocus is proposed, a reliable EEG-guided efficient VLM framework for VQA that improves VQA accuracy and demonstrates that EEG can guide efficient VLM inference even when its semantic decoding is imperfect.
Brain-to-image retrieval seeks to identify the visual stimulus that elicited a non-invasive neural response. Candidate images are typically represented by pretrained vision models, whose internal representations vary in abstraction across depth. Existing methods usually train the neural encoder to recover a fixed final...
Proposed Progressive Contrastive Alignment (ProCA), a unified and model-agnostic framework for adaptive neural-semantic alignment that progressively refines class-level contrastive supervision from frozen vision-language priors to EEG-aware semantic relations, and introduces structure-consistent interpolation to constr...
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