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 standardized measurement error (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.

中文翻译：

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标准化测量误差：平均事件相关电位的数据质量通用指标

事件相关电位 (ERP) 可能非常嘈杂，然而，没有广泛接受的 ERP 数据质量指标。在这里，我们提出了一种用于 ERP 研究的通用数据质量衡量标准——标准化 测量 误差 (SME)——这是测量标准误差的一个特例。虽然一些现有的指标提供了噪声水平的通用量化，但 SME 量化了在给定研究中测量的特定幅度或延迟值（例如，P3 波的峰值延迟）的数据质量（精度）。它几乎可以应用于从平均 ERP 波形得出的任何值，使其成为数据质量的通用度量。此外，SME 对每个参与者的数据质量进行量化，从而可以识别具有低质量数据和“不良”渠道的参与者。当在个体之间适当聚合时，SME 值可用于量化单次试验 EEG 噪声的综合影响以及在给定实验中对效应大小和统计功效的平均试验次数。