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Identifying longitudinal trends within EEG experiments

Author

Listed:
  • Kyle Hasenstab
  • Catherine A. Sugar
  • Donatello Telesca
  • Kevin McEvoy
  • Shafali Jeste
  • Damla Şentürk

Abstract

type="main" xml:lang="en"> Differential brain response to sensory stimuli is very small (a few microvolts) compared to the overall magnitude of spontaneous electroencephalogram (EEG), yielding a low signal-to-noise ratio (SNR) in studies of event-related potentials (ERP). To cope with this phenomenon, stimuli are applied repeatedly and the ERP signals arising from the individual trials are averaged at the subject level. This results in loss of information about potentially important changes in the magnitude and form of ERP signals over the course of the experiment. In this article, we develop a meta-preprocessing step utilizing a moving average of ERP across sliding trial windows, to capture such longitudinal trends. We embed this procedure in a weighted linear mixed effects model to describe longitudinal trends in features such as ERP peak amplitude and latency across trials while adjusting for the inherent heteroskedasticity created at the meta-preprocessing step. The proposed unified framework, including the meta-processing and the weighted linear mixed effects modeling steps, is referred to as MAP-ERP (moving-averaged-processed ERP). We perform simulation studies to assess the performance of MAP-ERP in reconstructing existing longitudinal trends and apply MAP-ERP to data from young children with autism spectrum disorder (ASD) and their typically developing counterparts to examine differences in patterns of implicit learning, providing novel insights about the mechanisms underlying social and/or cognitive deficits in this disorder.

Suggested Citation

  • Kyle Hasenstab & Catherine A. Sugar & Donatello Telesca & Kevin McEvoy & Shafali Jeste & Damla Şentürk, 2015. "Identifying longitudinal trends within EEG experiments," Biometrics, The International Biometric Society, vol. 71(4), pages 1090-1100, December.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:4:p:1090-1100
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    Cited by:

    1. Boland, Joanna & Telesca, Donatello & Sugar, Catherine & Jeste, Shafali & Goldbeck, Cameron & Senturk, Damla, 2022. "A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Cheng‐Han Yu & Meng Li & Colin Noe & Simon Fischer‐Baum & Marina Vannucci, 2023. "Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 629-641, June.
    3. Kyle Hasenstab & Aaron Scheffler & Donatello Telesca & Catherine A. Sugar & Shafali Jeste & Charlotte DiStefano & Damla Şentürk, 2017. "A multi-dimensional functional principal components analysis of EEG data," Biometrics, The International Biometric Society, vol. 73(3), pages 999-1009, September.

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