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ORIGINAL RESEARCH article
Front. Neuroergonomics
Sec. Neurotechnology and Systems Neuroergonomics
Volume 6 - 2025 | doi: 10.3389/fnrgo.2025.1572851
This article is part of the Research TopicInsights from the 5th International Neuroergonomics ConferenceView all 6 articles
Identifying EEG Biomarkers of Sense of Embodiment in Virtual Reality: Insights from Spatio-Spectral Features
Provisionally accepted- 1Institute of Biophysics and Biomedical Engineering, Faculty of Sciences, University of Lisbon, Lisbon, Lisboa, Portugal
- 2Laboratory for Robotics and Engineering Systems (LARSyS), Instituto Superior Técnico (ISR), Lisboa, Portugal
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The Sense of Embodiment (SoE) refers to the subjective experience of perceiving a non-biological body part as one’s own. Virtual Reality (VR) provides a powerful platform to manipulate SoE, making it a crucial factor in immersive human-computer interaction. This becomes particularly relevant in Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), especially motor imagery (MI)-BCIs, which harness brain activity to enable users to control virtual avatars in a self-paced manner. In such systems, a strong SoE can significantly enhance user engagement, control accuracy, and the overall effectiveness of the interface. However, SoE assessment remains largely subjective, relying on questionnaires, as no definitive EEG biomarkers have been established. Additionally, methodological inconsistencies across studies introduce biases that hinder biomarker identification.This study aimed to identify EEG-based SoE biomarkers by analyzing frequency band changes in a combined dataset of 41 participants under standardized experimental conditions. Participants underwent virtual SoE induction and disruption using multisensory triggers, with a validated questionnaire confirming the illusion.Results revealed a significant increase in Beta and Gamma power over the occipital lobe, suggesting these as potential EEG biomarkers for SoE. The findings underscore the occipital lobe’s role in multisensory integration and sensorimotor synchronization, supporting the theoretical framework of SoE. However, no single frequency band or brain region fully explains SoE. Instead, it emerges as a complex, dynamic process evolving across time, frequency, and spatial domains, necessitating a comprehensive approach that considers interactions across multiple neural networks.
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Received: 07 Feb 2025; Accepted: 18 Apr 2025.
Copyright: © 2025 Esteves, Valente, Bendor, Andrade and Vourvopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Athanasios Vourvopoulos, Laboratory for Robotics and Engineering Systems (LARSyS), Instituto Superior Técnico (ISR), Lisboa, Portugal
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