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

ABSTRACT: 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. Through computational modeling we established the quality of this dataset

SUBMITTER: Gifford AT 

PROVIDER: S-EPMC9771828 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature