AFoB: Adaptive Foveated Blur with Dual-Region Preservation for EEG-Image Retrieval

Brain-signal-based image retrieval commonly aligns EEG or MEG recordings with pretrained visual representations, making the construction of the image-side target an important design choice. Variability in brain responses and informative content beyond the image center motivate two complementary questions: how much visual detail should be retained, and where should it be preserved? We propose Adaptive Foveated Blur (AFoB), a perception-inspired framework that combines response-conditioned blur with dual-region detail preservation. A joint predictor takes brain-signal and fixed-blur reference-image embeddings and estimates the background-blur strength and concentration parameters controlling the extents of two preserved regions. One region remains centered on the image, while the second is located at the centroid of a low-level saliency map computed from the clean image without a learned localization module. A differentiable soft mask combines the two regions and blends the original image with its blurred counterpart. A frozen CLIP encoder maps the resulting image to a target for contrastive brain–image alignment, through which the brain encoder and foveation predictor are jointly trained. On 200-way zero-shot retrieval, AFoB improves Top-1 / Top-5 accuracy over UBP from 50.2% / 80.0% to 53.8% / 84.3% in the intra-subject setting and from 12.2% / 32.0% to 17.1% / 40.1% in the inter-subject setting on THINGS-EEG, with a proportionally larger gain in the latter, and from 25.6% / 55.6% to 39.6% / 69.8% intra-subject on THINGS-MEG. Analysis of the predicted parameters on THINGS-EEG reveals variation across images within individual subjects, rather than a single transformation per subject. AFoB provides a computational framework for response-conditioned visual abstraction and motivates further research connecting selective information preservation with models of human visual cognition.