A streamable large-scale clinical EEG dataset for Deep Learning

TL;DR

This paper introduces the first large-scale, streamable clinical EEG dataset from juvenile participants, facilitating deep learning research in neuroimaging by simplifying data access and management.

Contribution

It provides a large-scale, accessible EEG dataset specifically designed for deep learning applications in neuroscience, which was previously unavailable.

Findings

Dataset contains EEG data from 1,574 juvenile participants.

Demonstrates a use case integrating the dataset with deep learning models.

Highlights importance of neuroinformatics infrastructure for future discoveries.

Abstract

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.