Neurophysiological correlates of user engagement in mixed reality exergames

<i>Objective.</i>This study investigates the neurophysiological correlates of user engagement (UE) during upper-limb exercises in mixed reality (MR)-based exergames. It explores whether MR environments modulate brain activity differently than traditional exercises and whether physical object interaction affects engagement.<i>Methods.</i>High-density electroencephalography (EEG) was recorded from fifteen healthy adults performing bilateral arm exercises under four conditions-with and without MR and physical object interaction. Engagement indices were derived from spectral features (theta, alpha, and their ratios) at both source- and sensor-levels across predefined cortical regions. Time-course dynamics and subjective user experience were also evaluated.<i>Main results.</i>MR-based exergames significantly increased theta power and decreased alpha power compared to non-MR conditions, indicating greater cognitive and attentional engagement. These effects were consistent across frontal, sensorimotor, and parietal regions and remained stable over time. Physical object interaction had no significant effect on engagement. Subjectively, MR conditions were rated higher in stimulation, efficiency, and novelty.<i>Significance.</i>The effectiveness of MR in rehabilitation depends on sustained UE, which is typically assessed through subjective questionnaires. This study demonstrates that EEG-derived metrics-particularly theta and alpha activity-objectively reflect engagement. These results support EEG as a real-time monitoring tool for developing adaptive, brain-responsive rehabilitation technologies.

Authors

Institutions

Publication Details

Journal
Journal of Neural Engineering
Published
2025-05-20
DOI
https://doi.org/10.1088/1741-2552/addae7
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Neurophysiological correlates of user engagement in mixed reality exergames

Enzo Fenoglio, Marianna Semprini, Matteo Laffranchi, Florencia Garro et al.
1 citations
Journal of Neural Engineering
EEG and Brain-Computer Interfaces
1.43
article

Neurophysiological correlates of user engagement in mixed reality exergames

Enzo Fenoglio, Marianna Semprini, Matteo Laffranchi, Florencia Garro, Nadia Garcia-Hernández
article en
1 citations

Abstract

<i>Objective.</i>This study investigates the neurophysiological correlates of user engagement (UE) during upper-limb exercises in mixed reality (MR)-based exergames. It explores whether MR environments modulate brain activity differently than traditional exercises and whether physical object interaction affects engagement.<i>Methods.</i>High-density electroencephalography (EEG) was recorded from fifteen healthy adults performing bilateral arm exercises under four conditions-with and without MR and physical object interaction. Engagement indices were derived from spectral features (theta, alpha, and their ratios) at both source- and sensor-levels across predefined cortical regions. Time-course dynamics and subjective user experience were also evaluated.<i>Main results.</i>MR-based exergames significantly increased theta power and decreased alpha power compared to non-MR conditions, indicating greater cognitive and attentional engagement. These effects were consistent across frontal, sensorimotor, and parietal regions and remained stable over time. Physical object interaction had no significant effect on engagement. Subjectively, MR conditions were rated higher in stimulation, efficiency, and novelty.<i>Significance.</i>The effectiveness of MR in rehabilitation depends on sustained UE, which is typically assessed through subjective questionnaires. This study demonstrates that EEG-derived metrics-particularly theta and alpha activity-objectively reflect engagement. These results support EEG as a real-time monitoring tool for developing adaptive, brain-responsive rehabilitation technologies.

Journal of Neural EngineeringVol. 22(3)
Italian Institute of Technology (IT), Universidad Insurgentes (MX), Secretaría de Ciencia, Humanidades, Tecnología e Innovación (MX), Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (MX), University of Genoa (IT)
Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro
Openalex Percentile: Top 19%
EEG and Brain-Computer Interfaces
1.43
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.