Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary d...

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Published inScientific reports Vol. 14; no. 1; pp. 8856 - 15
Main Authors Pluta, Dustin, Hadj-Amar, Beniamino, Li, Meng, Zhao, Yongxiang, Versace, Francesco, Vannucci, Marina
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 17.04.2024
Nature Publishing Group
Nature Portfolio
Subjects
631/378/2649
639/705/531
Algorithms
Bayes Theorem
Bayesian analysis
Cognitive ability
Data Accuracy
EEG
Electroencephalography
Electroencephalography - methods
Event-related potentials
Evoked Potentials - physiology
Fourier transforms
Humanities and Social Sciences
Humans
Latency
multidisciplinary
Science
Science (multidisciplinary)
Statistical analysis
Statistical inference
Statistical power
Wakefulness
Online AccessGet full text
ISSN2045-2322
2045-2322
DOI10.1038/s41598-024-59579-2

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Abstract Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.
AbstractList Abstract Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.
Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.
Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.
ArticleNumber 8856
Author Versace, Francesco
Vannucci, Marina
Zhao, Yongxiang
Pluta, Dustin
Hadj-Amar, Beniamino
Li, Meng
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  fullname: Pluta, Dustin
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  fullname: Hadj-Amar, Beniamino
  organization: Department of Statistics, Rice University
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  surname: Li
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  organization: Department of Statistics, Rice University
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  givenname: Yongxiang
  surname: Zhao
  fullname: Zhao, Yongxiang
  organization: Department of Statistics and Computer Science, University of Michigan
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  givenname: Francesco
  surname: Versace
  fullname: Versace, Francesco
  organization: Department of Behavioral Science, MD Anderson Cancer Center
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  givenname: Marina
  surname: Vannucci
  fullname: Vannucci, Marina
  email: marina@rice.edu
  organization: Department of Statistics, Rice University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/38632350$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1016_j_resp_2025_104418
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Snippet Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple...
Abstract Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over...
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SubjectTerms 631/378/2649
639/705/531
Algorithms
Bayes Theorem
Bayesian analysis
Cognitive ability
Data Accuracy
EEG
Electroencephalography
Electroencephalography - methods
Event-related potentials
Evoked Potentials - physiology
Fourier transforms
Humanities and Social Sciences
Humans
Latency
multidisciplinary
Science
Science (multidisciplinary)
Statistical analysis
Statistical inference
Statistical power
Wakefulness
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Title Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes
URI https://link.springer.com/article/10.1038/s41598-024-59579-2
https://www.ncbi.nlm.nih.gov/pubmed/38632350
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https://www.proquest.com/docview/3041234957
https://pubmed.ncbi.nlm.nih.gov/PMC11024164
https://doaj.org/article/4b7dcb6ee1bd4f339200eab0dd9c680e
Volume 14
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