A streamable large-scale clinical EEG dataset for Deep Learning

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictio...

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Bibliographic Details
Published in2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2022; pp. 1058 - 1061
Main Authors Truong, Dung, Sinha, Manisha, Venkataraju, Kannan Umadevi, Milham, Michael, Delorme, Arnaud
Format Conference Proceeding Journal Article
LanguageEnglish
Published United States IEEE 01.07.2022
Subjects
Biological system modeling
Biomedical Research
Brain - diagnostic imaging
Computer vision
Deep Learning
EEG
Electroencephalography
Electrophysiology
Humans
Neuroimaging
Neuroinformatics
Neurosciences
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Summary:Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.
ISSN:2694-0604
DOI:10.1109/EMBC48229.2022.9871708