Exploration of Methodological and Participant-Related Influences on the Number of Artifacts in ERP Data

Event-related potential (ERP) data has low signal-to-noise ratio, requiring the conduction of a large number of trials in order to collect sufficient amounts of data for subsequent analysis. Therefore, it would be highly beneficial if researchers could minimize the number of artifacts that occur in the data, minimizing the number of discarded trials and the total number of trials needed. This study thus examined connections between the number of trials that have to be eliminated due to artifacts and a set of methodological variables, physical considerations, and individual differences. In half of the electroencephalography (EEG) data collection blocks, naïve undergraduate participants were asked not to blink for the duration of the block (approximately 2.5 minutes), but in the other half, the stimulus set included blinking cues to give participants a chance to blink during blocks. The number of artifacts did not differ based on whether participants were cued to blink during blocks nor which type of block participants experienced first. However, the first block had significantly more artifacts than other blocks, and the third block had significantly fewer. Participants who had previously known one or both investigators had significantly fewer artifacts in their data than participants who had not, but no significant relationship was found between the number of artifacts and any other individual difference or physical consideration examined. These results imply that researchers could preemptively reduce the number of artifacts in their EEG data by including practice blocks and recruiting friends or acquaintances for studies if possible. Based on subjective, unsolicited participant feedback, the authors also recommend having blink cues in data collection blocks in order to make the task more comfortable for participants. Future studies with similar aims could use different equipment setups, e.g. electrode caps, and experimental manipulation of individual difference factors, e.g. motivation and comfort.