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

ABSTRACT: 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" algorit

SUBMITTER: Leach SC 

PROVIDER: S-EPMC7402217 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature