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arXiv · 2604.10617

Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding

Mohammad Moradi·Morteza Moradi·Marco Grassia·Giuseppe Mangioni
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Abstract

Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in preserving object-level structure and semantic fidelity. Many existing approaches overlook the spatial arrangement of salient objects, leading to conceptually inconsistent outputs. We propose a saliency-driven decoding framework that employs graph-informed saliency priors to translate structural cues from brain signals into spatial masks. These masks, together with semantic information extracted from embeddings, condition a diffusion model to guide image regeneration, helping preserve object conformity while maintaining natural scene composition. In contrast to pipelines that invoke multiple diffusion stages, our approach relies on a single frozen model, offering a more lightweight yet effective design. Experiments show that this strategy improves both conceptual alignment and structural similarity to the original stimuli, while also introducing a new direction for efficient, interpretable, and structurally grounded brain decoding.

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Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni. 2026-04-12. Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding. https://arxiv.org/abs/2604.10617

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