EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD).

Anna Kaiser,Elena Von Wirth,Luise Poustka,Thomas Ethofer,Sabina Millenet,Tobias Banaschewski,Sarah Hohmann,Katja Becker,Martin Holtmann,Tanja Legenbauer,Johanna Ketter,Wolfgang Retz,Manfred Döpfner,Toivo Zinnow,Thomas Jans,Johannes Hebebrand,Henrik Uebel-Von Sandersleben,Alexandra Philipsen,Barbara Alm,Johannes Thome,Marcel Romanos,Karina Abenova,Daniel Brandeis,Michael Rösler,Pascal-M Aggensteiner,Julia Geißler ,Tobias Renner,Nathalie E Holz ,Michael Huß ,Andreas J Fallgatter ,Christine M Freitag ,Lea Teresa Jendreizik

doi:10.3390/brainsci11020214

Abstract

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

Highlights

Electroencephalography (EEG) is a non-invasive method for assessing brain-electrical activity on the scalp using a set number of electrodes [1,2]
No significant data quality differences were obtained for the direct comparison of the three conditions across all participants assessed
The current study contributes to our understanding of EEG data quality, participantrelated and methodological variables influencing EEG data quality, and the additional effects of data quality on results obtained from Fourier transformation (FFT) analyses beyond demographic and clinical characteristics

Summary

Introduction

Electroencephalography (EEG) is a non-invasive method for assessing brain-electrical activity on the scalp using a set number of electrodes [1,2] It has been widely used in the research fields of physiology, psychology, neuroscience, and cognitive science to explore the neural dynamics and circuits related to typically developing and altered human information processing and behavior [3]. Significant signal distortions due to contamination through participant-induced artifacts or experimental factors sometimes lead to unavailability of sufficient EEG data for subsequent analyses, resulting in a lower reliability of study results [4]. To this end, a series of offline processing methods exists that are applied to EEG data for extracting uncontaminated signals prior to further analyses. There is little standardization, and pre-processing methods vary substantially [5,6]

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