Event-related potentials (ERPs) are tiny signals, and they are embedded in noise that may be an order of magnitude larger. In theory, we can "average out" the noise by combining a large number of single-trial waveforms into an averaged ERP waveform. In practice, however, it is often difficult to obtain enough trials to adequately reduce the noise, and the remaining variability can dramatically reduce our power to detect significant differences. Moreover, the noise level may vary widely across recordings as a result of factors such as skin potentials, movement artifacts, poor electrode connections, and nearby electrical devices. The noise level may also be impacted by the experimental design, the recording procedure, and the signal processing pipeline. As a result, the signal-tonoise ratio may differ considerably across studies, across participants within a study, and across data processing methods. | Desirable properties for a metric of ERP data qualityAlthough noisy ERP waveforms are a major practical impediment in ERP research, the field has not adopted a universal measure of data quality that can be used to quantify the noise level in individual participants. Some metrics have been proposed, such as the root mean square of the voltage in the prestimulus period (Luck, 2014) or the standard deviation of a

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). Whereas some potential measures of data quality provide a generic quantification of the noise level, the SME quantifies the expected error in 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 impact of the single-trial EEG variability 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 papers, 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. Thus, the SME is a both a universal and useful metric of ERP data quality.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Electroencephalography (EEG) is one of the most widely used techniques to measure human brain activity. EEG recordings provide a direct, high temporal resolution measure of cortical activity from noninvasive scalp electrodes. However, the signals are small relative to the noise, and optimizing the quality of the recorded EEG data can significantly improve the ability to identify signatures of brain processing. This protocol provides a step-by-step guide to recording the EEG from human research participants using strategies optimized for producing the best quality EEG.

Electroencephalogram (EEG) recordings provide a valuable, noninvasive method for measuring human brain activity. This protocol modifies our general protocol for EEG recording (Farrens et al., 2019) for use during the COVID-19 pandemic. It was created with the help of numerous experts, and it specifies a clear set of steps for interacting with research participants, using personal protective equipment (PPE), and disinfecting equipment, all with the goal of reducing the COVID-19 risks for both laboratory personnel and participants. It focuses on the use of EEG in relatively simple research studies of adults who can easily understand and follow instructions, yet can be readily adapted for studies using other types of EEG experiments or other participant populations.