부산대학교 도서관

Event‐related potentials (ERPs) can be very noisy, and yet, there is no widely accepted metric of ERP data quality. Here, we propose a universal measure of data quality for ERP research—the standardizedmeasurementerror(SME)—which is a special case of the standard error of measurement. Whereas some existing metrics provide a generic quantification of the noise level, the SME quantifies the data quality (precision) for the specific amplitude or latency value being measured in a given study (e.g., the peak latency of the P3 wave). It can be applied to virtually any value that is derived from averaged ERP waveforms, making it a universal measure of data quality. In addition, the SME quantifies the data quality for each individual participant, making it possible to identify participants with low‐quality data and "bad" channels. When appropriately aggregated across individuals, SME values can be used to quantify the combined impact of the single‐trial EEG noise and the number of trials being averaged together on the effect size and statistical power in a given experiment. If SME values were regularly included in published articles, researchers could identify the recording and analysis procedures that produce the highest data quality, which could ultimately lead to increased effect sizes and greater replicability across the field. Averaged ERP waveforms from individual research participants may be greatly distorted by noise, but the field lacks a widely accepted metric of data quality that would allow us to objectively quantify the extent of the noise. Here, we propose such a metric (the Standardized Measurement Error) and show how it is related to effect sizes and statistical power. [ABSTRACT FROM AUTHOR]