A large and rich EEG dataset for modeling human visual object recognition
Dataset motivation and summary
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 THINGS database. We release this dataset as a tool to foster research in visual neuroscience and computer vision.
Additional dataset information
For information regarding the experimental paradigm and the EEG recording protocol and the dataset validation through computational modeling analyses please refer to our paper.
Additional data and resources
For additional data and resources visit our OSF project, where you can find:
- A detailed description of the raw EEG data files
- The preprocessed EEG data
- The stimuli images
- The EEG resting state data
If you use any of our data, please cite our paper.
Cracking the neural code of human object vision
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