A large and rich EEG dataset for modeling human visual object recognition

<h2>Dataset motivation and summary</h2><p dir="ltr">The human brain achieves visual object recognition through multiple stages of linear and nonlinear transformations operating at a millisecond scale. To predict and explain these rapid transformations, computational neuroscientists employ machine learning modeling techniques. However, state-of-the-art models require massive amounts of data to properly train, and to the present day there is a lack of vast brain datasets which extensively sample the temporal dynamics of visual object recognition. Here we collected a large and rich dataset of high temporal resolution EEG responses to images of objects on a natural background. This dataset includes 10 participants, each with 82,160 trials spanning 16,740 image conditions coming from the <a href="https://things-initiative.org/" target="_blank">THINGS database</a>. We release this dataset as a tool to foster research in visual neuroscience and computer vision.</p><h2>Useful material</h2><h3>Additional dataset information</h3><p dir="ltr">For information regarding the experimental paradigm, the EEG recording protocol and the dataset validation through computational modeling analyses please refer to our <a href="https://doi.org/10.1016/j.neuroimage.2022.119754" target="_blank">paper</a>.</p><h3>Additional dataset resources</h3><p dir="ltr">Please visit the <a href="https://www.alegifford.com/projects/2022/eeg_dataset/" rel="noreferrer" target="_blank">dataset page</a> for the paper, dataset tutorial, code and more.</p><h3>OSF</h3><p dir="ltr">For additional data and resources visit our <a href="https://doi.org/10.17605/OSF.IO/3JK45" target="_blank">OSF project</a>, where you can find:</p><ul><li>A detailed description of the raw EEG data files</li><li>The preprocessed EEG data</li><li>The stimuli images</li><li>The EEG resting state data</li></ul><h2>Citations</h2><p dir="ltr">If you use any of our data, please cite our <a href="https://doi.org/10.1016/j.neuroimage.2022.119754" target="_blank">paper</a>.</p>