Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914466945302528 |
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| author | Moradi, Mohammad Moradi, Morteza Grassia, Marco Mangioni, Giuseppe |
| author_facet | Moradi, Mohammad Moradi, Morteza Grassia, Marco Mangioni, Giuseppe |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10617 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding Moradi, Mohammad Moradi, Morteza Grassia, Marco Mangioni, Giuseppe Image and Video Processing Computer Vision and Pattern Recognition Multimedia 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. |
| title | Brain-Grasp: Graph-based Saliency Priors for Improved fMRI-based Visual Brain Decoding |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2604.10617 |