Discovering biomarkers in neurological and psychiatric disorders hinges on employing reliable and transparent methods with extensive datasets. Electroencephalography (EEG) holds great promise as a tool for identifying these biomarkers. Nevertheless, the process of recording, preprocessing, and analyzing EEG data consumes considerable time and often relies on subjective judgments. To address these challenges, we’ve developed DISCOVER-EEG—an open, fully automated pipeline that streamlines the preprocessing, analysis, and visualization of resting-state EEG data.
This pipeline automatically handles data in the standard EEG-BIDS format, performing preprocessing and extracting physiologically significant features of brain function, including oscillatory power, connectivity, and network characteristics. These features are then visualized using two widely recognized and open-source Matlab toolboxes: EEGlab and FieldTrip.
We illustrate the effectiveness of our pipeline by applying it to biomarker discovery in healthy aging using the LEMON dataset, which comprises 212 healthy participants. Furthermore, we showcase its utility in accelerating biomarker discovery in a clinical context by applying it to a new dataset featuring 74 patients with chronic pain.
The DISCOVER-EEG pipeline streamlines the aggregation, reusability, and analysis of extensive EEG datasets, promoting open and reproducible research in the field of brain function.