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Understanding the causes and consequences of variability in infant ERP editing practices
Claire Monroy1, Estefanía Domínguez-Martínez2,3, Benjamin Taylor3,4
1School of Psychology, Keele University, Keele, UK.
Developmental Psychobiology
|November 23, 2021
Summary
Infant event-related potential (ERP) data editing shows low agreement between human experts and automated methods. This variability impacts final electroencephalogram (EEG) results, highlighting a need for standardized infant ERP analysis practices.
Area of Science:
- Neuroscience
- Developmental Psychology
- Biomedical Engineering
Background:
- Analyzing infant electroencephalogram (EEG) data often involves trial-by-trial editing to remove noisy segments.
- Current methods for artifact detection and trial rejection in infant event-related potential (ERP) studies lack standardization.
Purpose of the Study:
- To compare the impact of different data editing methods on infant ERP analysis.
- To assess inter-editor reliability in processing infant EEG data.
- To identify sources of variability in ERP data editing pipelines.
Main Methods:
- Compared editing outcomes from three human experts and one automated algorithm on an infant EEG dataset.
- Analyzed agreement on trial inclusion and channel interpolation.
- Investigated EEG characteristics influencing human editors' decisions.
Main Results:
- Low inter-editor agreement was observed for both trial inclusion and channel interpolation.
- Variability in editing led to differences in final ERP morphology and statistical outcomes.
- Disagreements stemmed from differing criteria used by human editors.
Conclusions:
- Significant variability exists in infant ERP data editing pipelines.
- This variability can substantially influence final ERP results and statistical analyses.
- Establishing best practices for ERP editing in infant research is crucial.
