ArticleFrontiers in human neuroscience2016
Bootstrap Signal-to-Noise Confidence Intervals: An Objective Method for Subject Exclusion and Quality Control in ERP Studies.
Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- An ERP study on verb bias and thematic role assignment in standard Indonesian.Scientific reports · 2025Article
- Registered Report: Neural correlates of thematic role assignment for passives in Standard Indonesian.PloS one · 2025Article
- Can the Mismatch Negativity Really Be Elicited by Abstract Linguistic Contrasts?Neurobiology of language (Cambridge, Mass.) · 2024Article
- Improved acquisition of contact heat evoked potentials with increased heating ramp.Scientific reports · 2022Article
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- Neural Correlates of Affective Stimulus Evaluation: A Case-by-Case Analysis.Social cognitive and affective neuroscience · 2021Article
- An intensity matched comparison of laser- and contact heat evoked potentials.Scientific reports · 2021Article
- A Video-Based Classification System for Assessing Locomotor Skills in Children.Journal of sports science & medicine · 2020Article
- Power-spectra and cross-frequency coupling changes in visual and Audio-visual acquired equivalence learning.Scientific reports · 2019Article
- Excitability regulation in the dorsomedial prefrontal cortex during sustained instructed fear responses: a TMS-EEG study.Scientific reports · 2018Article
- Event Related Potential Study of Language Interaction in Bilingual Aphasia Patients.Frontiers in human neuroscience · 2018Article
- Homeostatic plasticity in human extrastriate cortex following a simulated peripheral scotoma.Experimental brain research · 2017Article
- Depression-Related Brain Connectivity Analyzed by EEG Event-Related Phase Synchrony Measure.Frontiers in human neuroscience · 2016Article
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4 authors.
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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 statistically exceed a desired signal-to-noise criterion. SNRLB provides a quantifiable metric of individual subject ERP quality and eliminates the need for subjective evaluation of waveform quality by the investigator. We detail the SNR-CI methodology, establish the efficacy of employing this approach with Monte Carlo simulations, and demonstrate its utility in practice when applied to ERP datasets.
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