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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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NeuroSync: Generalized Brain Decoding of Visual Stimuli Across Subjects.

Muhammad Kashif, Matteo Ferrante, Nicola Toschi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
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    Summary

    This study introduces a novel framework for cross-subject brain decoding, reconstructing images from fMRI data by integrating structural and semantic information. The method achieves state-of-the-art results, advancing brain decoding capabilities.

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    Area of Science:

    • Neuroscience
    • Computer Vision
    • Machine Learning

    Background:

    • Cross-subject neural variability and hierarchical visual processing present challenges for brain decoding.
    • Existing methods struggle to generalize across individuals and capture complex visual information.

    Purpose of the Study:

    • To develop a novel cross-subject brain decoding framework for reconstructing images from functional Magnetic Resonance Imaging (fMRI) data.
    • To integrate structural and semantic information for enhanced decoding accuracy and generalizability.

    Main Methods:

    • Utilized diffusion models and contrastive learning to align neural representations with visual and textual embeddings.
    • Employed a composite neural module for harmonizing cross-subject fMRI signals into a unified latent space.
    • Implemented a dual-pathway architecture with Variational Deep VAE (VDVAE) for structural reconstruction and IP-Adapter with BERT for semantic alignment.

    Main Results:

    • Achieved state-of-the-art performance on the Natural Scenes Dataset (NSD) with SSIM: 0.379 (structural) and EffNet-B: 0.571, SwAV: 0.225 (semantic).
    • Demonstrated robust generalizability across four subjects, outperforming previous studies.
    • Provided insights into distributed neural encoding mechanisms.

    Conclusions:

    • The proposed framework successfully integrates structural and semantic information for advanced cross-subject brain decoding.
    • This methodological contribution enhances the understanding of hierarchical visual processing and neural representation.
    • The study advances the feasibility of cross-subject brain decoding and offers a comprehensive framework for interpreting brain activity related to visual stimuli.