@Article{info:doi/10.2196/50992,
author="Rico-Olarte, Carolina
and Lopez, Diego M
and Eskofier, Bjoern M
and Becker, Linda",
title="Electrophysiological Insights in Exergaming---Electroencephalography Data Recording and Movement Artifact Detection: Systematic Review",
journal="JMIR Serious Games",
year="2025",
month="Apr",
day="7",
volume="13",
pages="e50992",
keywords="exergaming; EEG; brain activity; motion artifact; artifact removal",
abstract="Background: Exergames are interactive solutions that require physical activity and are commonly used in learning or rehabilitation settings. For cognitive rehabilitation with exergames, the assessment of the intervention progress can be conducted by verifying the changes in brain activity. Electroencephalography (EEG) is a well-known method for this evaluation. However, motion artifacts due to large body movements can impede signal quality. No comprehensive guide on the artifact removal methods in the context of exergaming has been found. Objective: This paper aimed to identify studies that have assessed EEG signals while a user interacts with an exergame and the applied methods for data handling and analysis with a focus on dealing with movement artifacts. Methods: This review included studies on human participants while engaging in exergames, where the primary outcome was brain activity measured by EEG. A total of 5 databases were searched at 3 time points: March 2021, October 2022, and February 2024. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies assessed methodological quality, rating studies as ``good,'' ``fair,'' or ``poor.'' Data were synthesized quantitatively to identify characteristics across studies, including sample demographics and intervention details, and basic statistics (mean [SD]) were calculated. Results: A total of 494 papers were screened, resulting in 17 studies having been included. All studies carried out EEG recordings during exergame interactions, primarily assessing attention and concentration, with the alpha wave being the most analyzed EEG band. Common motion artifact removal methods included visual inspection and independent component analysis. The review identified significant risks of bias, with 2 studies rated as ``good,'' 7 as ``fair,'' and 8 as ``poor.'' Due to the small number of studies and their heterogeneity, a meta-analysis was not feasible. Conclusions: The study successfully identifies the feasibility of recording electrophysiological brain activity during exergaming and provides insights into EEG devices, analysis methods, and exergaming systems used in previous studies. However, limitations, such as the lack of sufficient detail on motion artifact removal and a focus on short-term effects, underscore the need for improved methodologies and reporting standards, with recommendations for enhancing reliability in cognitive rehabilitation with exergames. ",
issn="2291-9279",
doi="10.2196/50992",
url="https://games.jmir.org/2025/1/e50992",
url="https://doi.org/10.2196/50992"
}