HAPPILEE: HAPPE In Low Electrode Electroencephalography, a standardized pre-processing software for lower density recordings.

Autor: Lopez KL; Northeastern University, 360 Huntington Ave, Boston, MA, United States., Monachino AD; Northeastern University, 360 Huntington Ave, Boston, MA, United States., Morales S; University of Maryland, College Park, MD, United States., Leach SC; University of Maryland, College Park, MD, United States., Bowers ME; University of Maryland, College Park, MD, United States., Gabard-Durnam LJ; Northeastern University, 360 Huntington Ave, Boston, MA, United States. Electronic address: l.gabard-durnam@northeastern.edu.
Jazyk: angličtina
Zdroj: NeuroImage [Neuroimage] 2022 Oct 15; Vol. 260, pp. 119390. Date of Electronic Publication: 2022 Jul 08.
DOI: 10.1016/j.neuroimage.2022.119390
Abstrakt: Lower-density Electroencephalography (EEG) recordings (from 1 to approximately 32 electrodes) are widely-used in research and clinical practice and enable scalable brain function measurement across a variety of settings and populations. Though a number of automated pipelines have recently been proposed to standardize and optimize EEG pre-processing for high-density systems with state-of-the-art methods, few solutions have emerged that are compatible with lower-density systems. However, lower-density data often include long recording times and/or large sample sizes that would benefit from similar standardization and automation with contemporary methods. To address this need, we propose the HAPPE In Low Electrode Electroencephalography (HAPPILEE) pipeline as a standardized, automated pipeline optimized for EEG recordings with lower density channel layouts of any size. HAPPILEE processes task-free (e.g., resting-state) and task-related EEG (including event-related potential data by interfacing with the HAPPE+ER pipeline), from raw files through a series of processing steps including filtering, line noise reduction, bad channel detection, artifact correction from continuous data, segmentation, and bad segment rejection that have all been optimized for lower density data. HAPPILEE also includes post-processing reports of data and pipeline quality metrics to facilitate the evaluation and reporting of data quality and processing-related changes to the data in a standardized manner. Here the HAPPILEE steps and their optimization with both recorded and simulated EEG data are described. HAPPILEE's performance is then compared relative to other artifact correction and rejection strategies. The HAPPILEE pipeline is freely available as part of HAPPE 2.0 software under the terms of the GNU General Public License at: https://github.com/PINE-Lab/HAPPE.
(Copyright © 2022. Published by Elsevier Inc.)
Databáze: MEDLINE
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