BioBridge: Bridging Brain and Vision Representations with Biological Inductive Biases

EEG-based visual neural decoding seeks to infer visual content from neural responses and supports applications such as image retrieval. However, progress is limited by the scarcity of paired EEG and image data and by the substantial mismatch between high-fidelity digital images and biological visual representations shaped by retinotopic organization and subject-dependent neuroanatomy. To address these challenges, we propose BioBridge, a biologically guided brain–vision alignment framework that incorporates structured physiological inductive biases into representation learning. Specifically, Adaptive Blur with Visual Priors uses retinotopic weighting to transform visual inputs into perceptually structured representations, thereby reducing the discrepancy between digital stimuli and biological perception. Biomimetic Visual Feature Extraction then learns complementary visual representations at multiple levels that reflect hierarchical cortical processing and improve robustness to subject variability. Finally, Multi-Level Bidirectional Contrastive Learning jointly aligns EEG and visual representations at different semantic levels through symmetric contrastive objectives in a shared embedding space. Experiments demonstrate that BioBridge achieves 80.5% Top-1 accuracy and 97.6% Top-5 accuracy in zero-shot retrieval from EEG to images, substantially outperforming previous methods and exhibiting strong generalization across subjects and experimental settings. Code is available at https://anonymous.4open.science/r/BioBridge-227/.