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Infant Auditory Processing and Event-related Brain Oscillations
Published on: July 1, 2015
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Physiologic artifacts in resting state oscillations in young children: methodological considerations for noisy data
Kevin McEvoy1, Kyle Hasenstab, Damla Senturk
1Semel Institute for Neuroscience and Human Behavior, Center for Autism Research and Treatment, University of California Los Angeles, 760 Westwood Plaza, Suite 68-237, Los Angeles, CA, 90095, USA.
Brain Imaging and Behavior
|January 8, 2015
Summary
Physiologic artifacts like blinks and EMG significantly alter electroencephalography (EEG) band power in children. Careful artifact rejection is crucial for accurate quantitative EEG analysis in developmental studies.
Area of Science:
- Neuroscience
- Developmental Psychology
- Biomedical Engineering
Background:
- Quantitative electroencephalography (qEEG) is a promising biomarker for typical and atypical development.
- Accurate qEEG analysis requires effective artifact rejection methods.
- Physiologic artifacts can significantly impact EEG band power estimation.
Purpose of the Study:
- To quantify the effects of common physiologic artifacts on EEG band power in typically developing children (ages 2-6).
- To inform the development of artifact rejection strategies for developmental qEEG data.
- To guide transparent reporting of methods in developmental EEG research.
Main Methods:
- High-density EEG data were collected from children (ages 2-6) during a 2-minute video.
- Data segments were classified as artifact-free, blinks, saccades, or EMG.
- Absolute and relative band power (theta, alpha, beta, gamma) were calculated across 9 regions for each category.
- Linear mixed models compared band power between artifact-free and artifact segments.
Main Results:
- Significant differences in absolute and relative band power were observed between artifact and artifact-free segments across all frequency bands.
- The impact of artifacts varied by artifact type, brain region, and frequency band.
- Electromyography (EMG) artifacts most significantly affected gamma band power, while ocular artifacts (blinks, saccades) most impacted theta band power.
Conclusions:
- Artifact detection strategies must be tailored to the specific frequency bands and regions of interest.
- The most conservative approach involves removing all EMG and ocular artifacts.
- Transparency in reporting analysis choices (power type, regions, frequency bands) is essential for reliable interpretation of developmental qEEG data.
