A Large and Rich EEG Dataset for Modeling Human Visual Object Recognition
Alessandro Gifford, Radoslaw Cichy, Freie Universität Berlin, Germany; Kshitij Dwivedi, Gemma Roig, Goethe Universität Frankfurt, Germany
Posters 1 Poster
Pacific Ballroom H-O
Thu, 25 Aug, 19:30 - 21:30 Pacific Time (UTC -7)
The human brain achieves object recognition by reformatting visual representations along multiple stages of nonlinear operations in the visual ventral stream. Currently, these representations are most well predicted by deep neural networks (DNNs). However, such models require massive amounts of data to properly train, and to the present day there is a lack of brain datasets that extensively sample the neural dynamics of visual object recognition. Here, we collected a large and rich dataset of high temporal resolution electroencephalography (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. We highlighted its suitability to train randomly initialized DNNs end-to-end to encode the EEG responses to arbitrary input images. We release this dataset as a tool to foster research in visual neuroscience and computer vision. The dataset is available on OSF: https://osf.io/3jk45/.