Evidence map›Paper›PMID 40194274›Full record

ReviewJMIR serious games2025

Electrophysiological Insights in Exergaming-Electroencephalography Data Recording and Movement Artifact Detection: Systematic Review.

Carolina Rico-Olarte, Diego M Lopez, Bjoern M Eskofier, Linda Becker

Abstract readReview
In one paragraph

Review in JMIR serious games, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Carolina Rico-OlarteTelematics Department, Universidad del Cauca, Popayán, Colombia.ORCID https://orcid.org/0000-0003-1588-4112
Diego M LopezTelematics Department, Universidad del Cauca, Popayán, Colombia.ORCID https://orcid.org/0000-0001-9425-4375
Bjoern M EskofierMachine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-0417-0336
Linda BeckerDepartment of Psychology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-9950-6882

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExergames 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.

objectiveThis 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.

methodsThis 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.

resultsA 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.

conclusionsThe 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.

Indexed as

artifact removalbrain activityEEGexergamingmotion artifact

Identifiers

PMID40194274
PMCPMC12012405

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.