Img2brain: Predicting the neural responses to visual stimuli of naturalistic scenes using machine learning

<p>The data for this project is part of the <a href="https://doi.org/10.1038/s41593-021-00962-x">Natural Scenes Dataset</a> (NSD), a massive dataset of 7T fMRI responses to images of natural scenes coming from the <a href="https://cocodataset.org/#home">COCO dataset</a>. The training dataset&nbsp;consists of brain responses measured at 10.000 brain locations (voxels) to 8857 images (in jpg format) for one subject. The 10.000 voxels are distributed around the visual pathway and may encode perceptual and semantic features in different proportions. The test dataset comprises 984 images (in jpg format), and the goal is to predict the brain responses to these images.</p> <p>The zip file&nbsp;contains the following folders:</p> <p>1. <strong>trainingIMG</strong>: contains the training images (8857) in jpg format. The numbering corresponds to the order of the rows in the brain response matrix.</p> <p>2. <strong>testIMG</strong>: contains test images (984) in jpg format.</p> <p>3. <strong>trainingfMRI</strong>: contains a npy file with the fMRI responses measured at 10000 brain locations (voxels) to the training images. The matrix has 8857 rows (one for each image) and 10000 columns (one for each voxel).</p>