Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations Article Swipe

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Christos Zangos , Danish Ebadulla , Thomas C. Sprague , Ambuj K. Singh ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2505.01670

This work introduces a novel approach to fMRI-based visual image reconstruction using a subject-agnostic common representation space. We show that the brain signals of the subjects can be aligned in this common space during training to form a semantically aligned common brain. This is leveraged to demonstrate that aligning subject-specific lightweight modules to a reference subject is significantly more efficient than traditional end-to-end training methods. Our approach excels in low-data scenarios. We evaluate our methods on different datasets, demonstrating that the common space is subject and dataset-agnostic.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2505.01670
PDF: https://arxiv.org/pdf/2505.01670
OA Status: green
OpenAlex ID: https://openalex.org/W4415027758

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OpenAlex ID: https://openalex.org/W4415027758

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DOI: https://doi.org/10.48550/arxiv.2505.01670

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Title: Efficient Multi Subject Visual Reconstruction from fMRI Using Aligned Representations

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-05-03

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Authors: Christos Zangos, Danish Ebadulla, Thomas C. Sprague, Ambuj K. Singh

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Landing page: https://arxiv.org/abs/2505.01670

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PDF URL: https://arxiv.org/pdf/2505.01670

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2505.01670

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Cited by: 0

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