HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset

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Abstract

The Child Mind Institute (CMI) Healthy Brain Network (HBN) project has recorded phenotypic, behavioral, and neuroimaging data from ~5,000 children and young adults between the ages of 5 and 21. Here, we present “analysis-ready” data from its high-density (128-channel) electroencephalographic (EEG) recording sessions formatted as Brain Imaging Data Structure (BIDS) datasets (HBN-EEG) with behavioral and task-condition events annotated using Hierarchical Event Descriptors (HED), making it analysis-ready for many purposes, without ‘forensic’ search for unreported details. We also ensured individual data files data and event integrity and marked inconsistencies. HBN-EEG sessions include six tasks, three with no participant behavioral input and three including button press responses following task instructions. Openly available participant information includes age, gender, and four psychopathology dimensions (internalizing, externalizing, attention, and p-factor) derived from bifactor model of questionnaire data. Currently, HBN-EEG data from more than 2,600 participants is freely available on NEMAR (nemar.org) and OpenNeuro in the form of nine dataset releases, with further dataset releases to follow. The HBN-EEG dataset is intended to support the development and validation of EEG analysis methods, including machine learning and deep learning approaches, and to facilitate the development of EEG-based biomarkers for psychiatric disorders.

The number of subjects per HBN-EEG dataset release

Citation

@ARTICLE{Shirazi2024-ye,
  title    = "{HBN}-{EEG}: The {FAIR} implementation of the Healthy Brain
              Network ({HBN}) electroencephalography dataset",
  author   = "Shirazi, Seyed Yahya and Franco, Alexandre and Hoffmann, Mauricio
              Scopel and Esper, Nathalia and Truong, Dung and Delorme, Arnaud
              and Milham, Michael and Makeig, Scott",
  journal  = "bioRxiv",
  pages    = "2024.10.03.615261",
  month    =  "3~" # oct,
  year     =  2024,
  doi      = "10.1101/2024.10.03.615261",
  language = "en"
}