mlsanityData to 7 Oct 2026

Progress Towards Decoding Visual Imagery via fNIRS

arXiv 2406.07662 ↗First posted 11 Jun 2024eess.IV cs.AI cs.CVcited by 1

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Abstract (arXiv)

We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval accuracy with 1-cm resolution, compared to 93% on the full-resolution fMRI, and 20% with 2-cm resolution. With simulations and high-density tomography, we found that time-domain fNIRS can achieve 1-cm resolution, compared to 2-cm resolution for continuous-wave fNIRS. Lastly, we share designs for a prototype time-domain fNIRS device, consisting of a laser driver, a single photon detector, and a time-to-digital converter system.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 44 references that are in the map. First posted on arXiv 11 Jun 2024.

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7 of 44 references.

References · 7

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  4. 2023Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priorscited by 156
  5. 2022Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decodingcited by 142
  6. 2024MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Datacited by 105
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