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

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dc.citation.articleNumber8856en_US
dc.citation.journalTitleScientific Reportsen_US
dc.citation.volumeNumber14en_US
dc.contributor.authorPluta, Dustinen_US
dc.contributor.authorHadj-Amar, Beniaminoen_US
dc.contributor.authorLi, Mengen_US
dc.contributor.authorZhao, Yongxiangen_US
dc.contributor.authorVersace, Francescoen_US
dc.contributor.authorVannucci, Marinaen_US
dc.date.accessioned2024-07-25T20:55:18Zen_US
dc.date.available2024-07-25T20:55:18Zen_US
dc.date.issued2024en_US
dc.description.abstractStudies 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.en_US
dc.identifier.citationPluta, D., Hadj-Amar, B., Li, M., Zhao, Y., Versace, F., & Vannucci, M. (2024). Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes. Scientific Reports, 14(1), 8856. https://doi.org/10.1038/s41598-024-59579-2en_US
dc.identifier.digitals41598-024-59579-2en_US
dc.identifier.doihttps://doi.org/10.1038/s41598-024-59579-2en_US
dc.identifier.urihttps://hdl.handle.net/1911/117526en_US
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleImproved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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