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

The human brain achieves visual object recognition through multiple stages of 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 THINGS database (https://things-initiative.org/). We release this dataset as a tool to foster research in visual neuroscience and computer vision.<br>For information regarding the experimental paradigm and the EEG recording protocol please refer to our paper (https://doi.org/10.1101/2022.03.15.484473).<br><br>A detailed description of the raw EEG data files, the preprocessed EEG data and the stimuli images can be found on OSF (https://doi.org/10.17605/OSF.IO/3JK45). <br>If you use any of our data, please cite our paper preprint (https://doi.org/10.1101/2022.03.15.484473) and our dataset (https://doi.org/10.25452/figshare.plus.18470912).