  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.
