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

Stephanie C Leach¹, Santiago Morales¹, Maureen E Bowers²

¹Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA.

Psychophysiology

|March 19, 2020

Summary

This summary is machine-generated.

A modified artifact detection algorithm, adjusted-ADJUST, improves electroencephalograph (EEG) data processing for pediatric studies. This automated method retains more neural data by effectively removing artifacts, enhancing research reliability.

Keywords:

EEG artifacts automated artifact classification algorithm developmental research electroencephalography geodesic sensor net independent component analysis

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Electroencephalograph (EEG) studies in children lose significant data due to artifacts like blinks and movements.
Manual artifact identification in EEG data is time-consuming and subjective.
Existing automated algorithms (e.g., ADJUST, ICLabel) are validated for adults but not optimized for pediatric EEG data.

Purpose of the Study:

To develop and validate an automated algorithm for artifact removal in pediatric EEG data.
To compare the performance of a modified ADJUST algorithm ('adjusted-ADJUST') against the original ADJUST and ICLabel for both adult and pediatric datasets.

Main Methods:

The ADJUST algorithm was modified to create 'adjusted-ADJUST' specifically for pediatric EEG data.
Performance was evaluated across adults, children, and infants using three metrics: agreement with expert coders, trial retention rate, and EEG signal reliability.
Comparisons were made between adjusted-ADJUST, original-ADJUST, ICLabel, and no Independent Component Analysis (ICA) correction.

Main Results:

The adjusted-ADJUST algorithm demonstrated superior performance compared to the original-ADJUST and no ICA correction across both adult and pediatric data.
For certain performance measures, adjusted-ADJUST outperformed ICLabel on pediatric data.
The optimized algorithm successfully improved artifact classification and increased the number of retained EEG trials.

Conclusions:

Optimizing existing artifact detection algorithms, such as adjusted-ADJUST, enhances artifact classification accuracy and maximizes usable EEG data in pediatric populations.
This improved preprocessing facilitates more robust and reliable EEG research in children.
The adjusted-ADJUST algorithm is publicly available to aid the research community.