Preprocessing Choices for P3 Analyses with Mobile EEG: A Systematic Literature Review and Interactive Exploration

Abstract

Preprocessing is necessary to extract meaningful results from electroencephalography (EEG) data. With many possible preprocessing choices, their impact on outcomes is fundamental. While previous studies have explored the effects of preprocessing on stationary EEG data, this research delves into mobile EEG, where complex processing is necessary to address motion artifacts. Specifically, we describe the preprocessing choices studies reported for analyzing the P3 event-related potential (ERP) during walking and standing. A systematic review of 258 studies of the P3 during walking, identified 27 studies meeting the inclusion criteria. Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as the widespread use of offline filters but limited application of line noise correction (3 of 27 studies). Notably, 59% of studies involved manual processing steps, and 56% omitted reporting critical parameters for at least one step. All studies employed unique preprocessing strategies. These findings align with stationary EEG preprocessing results, emphasizing the necessity for standardized reporting in mobile EEG research. We implemented an interactive visualization tool (Shiny app) to aid the exploration of the preprocessing landscape. The app allows users to structure the literature regarding different processing steps, enter planned processing methods, and compare them with the literature. The app could be utilized to examine how these choices impact P3 results and understand the robustness of various processing options. We hope to increase awareness regarding the potential influence of preprocessing decisions and advocate for comprehensive reporting standards to foster reproducibility in mobile EEG research.