A large dataset of human EEG responses to short naturalistic videos for studying dynamic visual event processing
Abstract
Vision neuroscience has experienced a surge in the collection and use of large-scale datasets of brain responses to naturalistic images. However, static images lack the temporal dimension essential for understanding how vision is solved in the brain during dynamic real life settings. To facilitate the study of the neural correlates of dynamic visual event perception, we introduce the EEG Moments Dataset (EMD). EMD consists of 128-channel EEG responses and eye-tracking recordings of 6 human participants viewing 1,102 short naturalistic videos (3-second long; with audio track) while maintaining central fixation. We show that EMD's EEG responses well encode stimulus-related information, exhibit a temporal correspondence with the video stimuli, and have a rich representational content revealed by brain encoding models based on different feature spaces. Furthermore, complemented by the BOLD Moments Dataset (BMD) - an existing large-scale dataset of human functional magnetic resonance imaging (fMRI) responses for the same videos - EMD enables spatio-temporally resolved investigations of brain responses to dynamic visual events. We release EMD's EEG and eye-tracking data in both raw and preprocessed format, along with the 1,102 video stimuli, and rich stimulus metadata. Finally, we provide an interactive code tutorial to familiarize with EMD's preprocessed data, stimuli, and stimulus metadata.
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BibTeX
@misc{gifford2026large,
title = {{A large dataset of human EEG responses to short naturalistic videos for studying dynamic visual event processing}},
author = {Alessandro T. Gifford and Pablo Oyarzo and Anne W. Zonneveld and Christina Sartzetaki and Iris I. A. Groen and Radoslaw M. Cichy},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2608.28768},
eprint = {2608.28768},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28768},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - A large dataset of human EEG responses to short naturalistic videos for studying dynamic visual event processing
AU - Alessandro T. Gifford
AU - Pablo Oyarzo
AU - Anne W. Zonneveld
AU - Christina Sartzetaki
AU - Iris I. A. Groen
AU - Radoslaw M. Cichy
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2608.28768
UR - https://arxiv.org/abs/2608.28768
AB - Vision neuroscience has experienced a surge in the collection and use of large-scale datasets of brain responses to naturalistic images. However, static images lack the temporal dimension essential for understanding how vision is solved in the brain during dynamic real life settings. To facilitate the study of the neural correlates of dynamic visual event perception, we introduce the EEG Moments Dataset (EMD). EMD consists of 128-channel EEG responses and eye-tracking recordings of 6 human participants viewing 1,102 short naturalistic videos (3-second long; with audio track) while maintaining central fixation. We show that EMD's EEG responses well encode stimulus-related information, exhibit a temporal correspondence with the video stimuli, and have a rich representational content revealed by brain encoding models based on different feature spaces. Furthermore, complemented by the BOLD Moments Dataset (BMD) - an existing large-scale dataset of human functional magnetic resonance imaging (fMRI) responses for the same videos - EMD enables spatio-temporally resolved investigations of brain responses to dynamic visual events. We release EMD's EEG and eye-tracking data in both raw and preprocessed format, along with the 1,102 video stimuli, and rich stimulus metadata. Finally, we provide an interactive code tutorial to familiarize with EMD's preprocessed data, stimuli, and stimulus metadata.
ER -