Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets

• Published in: Psychophysiology Vol. 57; no. 8; pp. e13566 - n/a
• Main Authors: Leach, Stephanie C., Morales, Santiago, Bowers, Maureen E., Buzzell, George A., Debnath, Ranjan, Beall, Daniel, Fox, Nathan A.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.08.2020
• Subjects: Adult; Algorithms; automated artifact classification algorithm; Automation; Cerebral Cortex - physiology; Child; Child, Preschool; Classification; developmental research; EEG; EEG artifacts; electroencephalography; Electroencephalography - methods; Electroencephalography - standards; geodesic sensor net; Humans; independent component analysis; Infants; Models, Theoretical; Motor Activity - physiology; Nets; Pediatrics; Population studies; Psychomotor Performance - physiology; Signal Processing, Computer-Assisted; Visual Perception - physiology; electroencephalography; independent component analysis; EEG artifacts; automated artifact classification algorithm; developmental research; geodesic sensor net
• ISSN: 0048-5772; 1469-8986; 1540-5958
• DOI: 10.1111/psyp.13566

A major challenge for electroencephalograph (EEG) studies on pediatric populations is that large amounts of data are lost due to artifacts (e.g., movement and blinks). Independent component analysis (ICA) can separate artifactual and neural activity, allowing researchers to remove such artifactual activity and retain a greater percentage of EEG data for analyses. However, manual identification of artifactual components is time‐consuming and requires subjective judgment. Automated algorithms, like ADJUST and ICLabel, have been validated on adults, but to our knowledge, no such algorithms have been optimized for pediatric data. Therefore, in an attempt to automate artifact selection for pediatric data collected with geodesic nets, we modified ADJUST's algorithm. Our “adjusted‐ADJUST” algorithm was compared to the “original‐ADJUST” algorithm and ICLabel in adults, children, and infants on three different performance measures: respective classification agreement with expert coders, the number of trials retained following artifact removal, and the reliability of the EEG signal after preprocessing with each algorithm. Overall, the adjusted‐ADJUST algorithm performed better than the original‐ADJUST algorithm and no ICA correction with adult and pediatric data. Moreover, in some measures, it performed better than ICLabel for pediatric data. These results indicate that optimizing existing algorithms improves artifact classification and retains more trials, potentially facilitating EEG studies with pediatric populations. Adjusted‐ADJUST is freely available under the terms of the GNU General Public License at: https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline/tree/master/adjusted_adjust_scripts. Currently, there are no automated independent component analysis artifact classification algorithms optimized for pediatric data. Therefore, we modified the ADJUST algorithm, which was originally optimized for adult data, in order to improve ADJUST's performance on developmental populations. The results show that optimizing a well‐established algorithm for pediatric data improves performance in this population without compromising performance on adult populations.