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 (Scopus, Web of Science, PubMed) of the P3 during walking, identified 27 studies meeting the inclusion criteria (empirical study, EEG data of healthy humans, P3 during a gait and non-movement condition). Two independent coders extracted preprocessing choices reported in each study. Analysis of preprocessing choices revealed commonalities and differences, such as widespread use of offline filters but limited application of line noise correction (3 out of 27 studies). Notably, 63% of studies involved manual processing steps, and 52% 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 Shiny app allows users to structure the literature regarding different processing steps and processing combinations, thereby providing a meaningful overview. It is also possible to enter planned or preferred processing methods and compare own choices with the literature. The app thus helps to identify defensible preprocessing choices, specifically with a focus on P3 ERP analyses during standing and walking. It 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.