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

  • Michal Gnacek*
  • , Neslihan Özhan
  • , John Broulidakis
  • , Ifigeneia Mavridou
  • , Theodoros Kostoulas
  • , Emili Balaguer-Ballester
  • , Martin Gjoreski
  • , Hristijan Gjoreski
  • , Charles Nduka
  • , Matthew Garner
  • , Erich Graf
  • , Ellen Seiss
  • *Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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 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, separated by a brief interval. Findings demonstrate the efficacy of CO 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.
Original languageEnglish
Article number103643
Number of pages17
JournalInformation Fusion
Volume126, part B
DOIs
Publication statusPublished - Feb 2026

Keywords

  • affective computing
  • physiological signals
  • virtual reality
  • methods of data collection
  • arousal

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