CFBA: Contour-preserving Foveated Blur Alignment for Neural Visual Decoding

Decoding visual content from brain signals helps characterize human perception and supports brain–computer interfaces. However, existing approaches to visual supervision adaptation do not explicitly combine contour preservation with spatially varying detail suppression, potentially limiting neural–visual alignment. To address this limitation, we propose Contour-preserving Foveated Blur Alignment (CFBA), which combines explicit preservation of estimated contour pixels with spatially varying suppression of surrounding detail. EGNet predictions are refined through morphological closing and contour-length filtering to construct a contour mask. An exponential radial profile blends the original image with its Gaussian-blurred counterpart, retaining more original detail near the image center. Restoring the masked contour pixels produces the supervision target. A Residual Projection Encoder (RPE) aligns neural recordings with frozen CLIP ResNet-50 features of this target through symmetric contrastive learning. A separate ATM–ViT-H-14 branch learns alignment under original-image supervision. The RPE supports retrieval and classification. For reconstruction, a diffusion prior converts the ATM embedding into the primary condition, while a linear projection trained through the generation objective maps the RPE embedding into an auxiliary condition. IP-Adapters inject both conditions into a pretrained SDXL generator. Extensive experiments on THINGS-EEG and THINGS-MEG demonstrate that CFBA outperforms state-of-the-art methods across retrieval, classification, and reconstruction. The code is available at https://anonymous.4open.science/r/CFBA-D9CD.