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DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

  • Cristina Gil Ávila
  • , Felix S. Bott
  • , Laura Tiemann
  • , Vanessa D. Hohn
  • , Elisabeth S. May
  • , Moritz M. Nickel
  • , Paul Theo Zebhauser
  • , Joachim Gross
  • , Markus Ploner
  • Technical University of Munich
  • Ludwig-Maximilians-Universität München
  • University of Münster

Research output: Contribution to journalArticlepeer-review

47 Scopus citations

Abstract

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

Original languageEnglish
Article number613
JournalScientific Data
Volume10
Issue number1
DOIs
StatePublished - Dec 2023

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