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Brain-IT Data (NSD Dataset Mirror)

This repository is a mirror of the NSD (Natural Scenes Dataset) data required by the Brain-IT project, for fMRI-to-image reconstruction research.

⚠️ For research use only. This repository is a data mirror only and does not own the data. Please comply with the usage licenses of each data source.

Original Sources

Data Source
Brain-IT original repository https://github.com/WeizmannVision/brainit-fmri.git
Brain-IT paper Brain-IT: Image Reconstruction from fMRI via Brain-Interaction Transformer (ICLR 2026, arXiv:2510.25976)
NSD dataset (fMRI betas / stimuli / experiment metadata) Website https://naturalscenesdataset.org , public AWS S3 bucket https://natural-scenes-dataset.s3.amazonaws.com
NSD paper Allen et al., A massive 7T fMRI dataset to bridge cognitive neuroscience and AI, Nature Neuroscience 2022
COCO dataset (unlabeled images, not included in this repo) Website https://cocodataset.org , unlabeled2017 download http://images.cocodataset.org/zips/unlabeled2017.zip
Algonauts ROI masks Public ROI masks from the Algonauts Challenge (shared via Google Drive)

Directory Structure

brain-it-data/
├── experiment/              # NSD experiment metadata
│   ├── nsd_expdesign.mat
│   └── nsd_stim_info_merged.csv
├── imgs/
│   └── nsd_stimuli.hdf5     # Stimulus images (~73k images, ~37GB)
├── roi_masks/               # Algonauts ROI masks (sub1 ~ sub8)
│   ├── sub1/ ...
│   └── sub8/
└── raw_fmri/                # fMRI beta maps (subj01 ~ subj08, fsaverage space)
    ├── subj01/ ...
    └── subj08/

Data Notes

  • raw_fmri: Beta maps for 8 subjects (subj01–08), with session counts 40 / 40 / 32 / 30 / 40 / 32 / 40 / 30, totaling 568 .mgh files (betas_fithrf_GLMdenoise_RR, fsaverage space, ~468MB per lh/rh file).
  • imgs: Contains only nsd_stimuli.hdf5 (stimulus images). The 123k COCO unlabeled2017 images are not included in this repository (they exceed the repo file-count limit); download them from the COCO website.
  • Download scripts are in the original repository under data/scripts/run_all_downloads (options: --subjects, --inference-only, --skip-fmri, etc.).

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