Bootstrap Signal-to-Noise Confidence Intervals: An Objective Method for Subject Exclusion and Quality Control in ERP Studies

Frontiers in Human Neuroscience
Nathan A ParksMadeleine E Young

Abstract

Analysis of event-related potential (ERP) data includes several steps to ensure that ERPs meet an appropriate level of signal quality. One such step, subject exclusion, rejects subject data if ERP waveforms fail to meet an appropriate level of signal quality. Subject exclusion is an important quality control step in the ERP analysis pipeline as it ensures that statistical inference is based only upon those subjects exhibiting clear evoked brain responses. This critical quality control step is most often performed simply through visual inspection of subject-level ERPs by investigators. Such an approach is qualitative, subjective, and susceptible to investigator bias, as there are no standards as to what constitutes an ERP of sufficient signal quality. Here, we describe a standardized and objective method for quantifying waveform quality in individual subjects and establishing criteria for subject exclusion. The approach uses bootstrap resampling of ERP waveforms (from a pool of all available trials) to compute a signal-to-noise ratio confidence interval (SNR-CI) for individual subject waveforms. The lower bound of this SNR-CI (SNRLB ) yields an effective and objective measure of signal quality as it ensures that ERP waveforms st...Continue Reading

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Citations

Oct 12, 2016·Frontiers in Human Neuroscience·Yuezhi LiYong Hu
Feb 27, 2018·Prosthetics and Orthotics International·Simon Sw Li, Daniel Hk Chow
Sep 9, 2016·PloS One·Matthew A GannonNathan A Parks
Aug 20, 2017·Experimental Brain Research·Matthew A GannonNathan A Parks
Mar 21, 2018·Frontiers in Human Neuroscience·Elvira KhachatryanMarc M Van Hulle
Mar 27, 2021·Scientific Reports·Iara De SchoenmackerJohn L K Kramer
Aug 12, 2021·BMC Biology·Benjamin WittevrongelMatthew J Brookes
Aug 7, 2021·Social Cognitive and Affective Neuroscience·Harald T Schupp, Ursula Kirmse

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