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Multi-modal physiological markers of arousal induced by CO2 inhalation in Virtual Reality.

Gnacek, M., Özhan, N., Broulidakis, J., Mavridou, I., Kostoulas, T., Balaguer-Ballester, E., Gjoreski, M., Gjoreski, H., Nduka, C., Garner, M., Graf, E. and Seiss, E., 2026. Multi-modal physiological markers of arousal induced by CO2 inhalation in Virtual Reality. Information Fusion, 126 (Part B), 103643.

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DOI: 10.1016/j.inffus.2025.103643

Abstract

High arousal states, like fear and anxiety, play a crucial role in organisms’ adaptive responses to threats. Yet, inducing and reliably measuring such states within controlled settings presents challenges. This study uses a novel approach of CO<inf>2</inf> enriched air vs normal air in a Virtual Reality (VR) context to induce high arousal whilst measuring physiological signals such as galvanic skin response (GSR), facial skin impedance, facial electromyography (fEMG), photoplethysmography (PPG), breathing, and pupillometry. In a single-blind study, 63 participants underwent a regimen involving 20 min of breathing regular air followed by 20 min of 7.5% CO<inf>2</inf>, separated by a brief interval. Findings demonstrate the efficacy of CO<inf>2</inf> inhalation in eliciting high arousal, as substantiated by statistically significant changes for all signals, further validated through high (94%) accuracy arousal classification. This study establishes a method for inducing high arousal states within a laboratory context validated through comprehensive multi-sensor data and machine learning analyses. The study underscores the value of employing a suite of physiological measures to comprehensively describe the intricate dynamics of arousal. The generated database is a promising resource for researching physiological markers of arousal, panic, fear, and anxiety, offering insights that can potentially resonate within clinical and therapeutic realms.

Item Type:Article
ISSN:1566-2535
Uncontrolled Keywords:Affective computing; Physiological signals; Virtual reality; Methods of data collection; Arousal
Group:Faculty of Science & Technology
ID Code:41399
Deposited By: Symplectic RT2
Deposited On:26 Sep 2025 10:41
Last Modified:26 Sep 2025 10:41

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