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

• Published in: Frontiers in human neuroscience Vol. 10; p. 50
• Main Authors: Parks, Nathan A., Gannon, Matthew A., Long, Stephanie M., Young, Madeleine E.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 12.02.2016; Frontiers Media S.A
• Subjects: Confidence intervals; Data analysis; data quality; Electroencephalography; event-related potential (ERP); Event-related potentials; Experiments; Eye movements; Hypotheses; Methods; Neuroscience; Noise; Quality control; resampling; signal-to-noise; subject selection; event-related potentials (ERP); signal-to-noise ratio (SNR); subject exclusion criteria; bootstrap confidence intervals

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 qualit...