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Kritikos J, Alevizopoulos G, Koutsouris D. Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses. Front Hum Neurosci. 2021;15:596980doi: 10.3389/fnhum.2021.596980.
Kritikos, J., Alevizopoulos, G., & Koutsouris, D. (2021). Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses. Frontiers in human neuroscience, 15596980. https://doi.org/10.3389/fnhum.2021.596980
Kritikos, Jacob, et al. "Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses." Frontiers in human neuroscience vol. 15 (2021): 596980. doi: https://doi.org/10.3389/fnhum.2021.596980
Kritikos J, Alevizopoulos G, Koutsouris D. Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses. Front Hum Neurosci. 2021 Feb 12;15:596980. doi: 10.3389/fnhum.2021.596980. eCollection 2021. PMID: 33643010; PMCID: PMC7906990.
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Front Hum Neurosci. 2021 Feb 12;15:596980. doi: 10.3389/fnhum.2021.596980. eCollection 2021.

Personalized Virtual Reality Human-Computer Interaction for Psychiatric and Neurological Illnesses: A Dynamically Adaptive Virtual Reality Environment That Changes According to Real-Time Feedback From Electrophysiological Signal Responses.

Frontiers in human neuroscience

Jacob Kritikos, Georgios Alevizopoulos, Dimitris Koutsouris

Affiliations

  1. Department of Bioengineering, Imperial College London, South Kensington Campus, London, United Kingdom.
  2. Psychiatric Clinic, Agioi Anargyroi General Oncological Hospital of Kifisia, Athens, Greece.
  3. Biomedical Engineering Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.

PMID: 33643010 PMCID: PMC7906990 DOI: 10.3389/fnhum.2021.596980

Abstract

Virtual reality (VR) constitutes an alternative, effective, and increasingly utilized treatment option for people suffering from psychiatric and neurological illnesses. However, the currently available VR simulations provide a predetermined simulative framework that does not take into account the unique personality traits of each individual; this could result in inaccurate, extreme, or unpredictable responses driven by patients who may be overly exposed and in an abrupt manner to the predetermined stimuli, or result in indifferent, almost non-existing, reactions when the stimuli do not affect the patients adequately and thus stronger stimuli are recommended. In this study, we present a VR system that can recognize the individual differences and readjust the VR scenarios during the simulation according to the treatment aims. To investigate and present this dynamically adaptive VR system we employ an Anxiety Disorder condition as a case study, namely arachnophobia. This system consists of distinct anxiety states, aiming to dynamically modify the VR environment in such a way that it can keep the individual within a controlled, and appropriate for the therapy needs, anxiety state, which will be called "desired states" for the study. This happens by adjusting the VR stimulus, in real-time, according to the electrophysiological responses of each individual. These electrophysiological responses are collected by an external electrodermal activity biosensor that serves as a tracker of physiological changes. Thirty-six diagnosed arachnophobic individuals participated in a one-session trial. Participants were divided into two groups, the Experimental Group which was exposed to the proposed real-time adaptive virtual simulation, and the Control Group which was exposed to a pre-recorded static virtual simulation as proposed in the literature. These results demonstrate the proposed system's ability to continuously construct an updated and adapted virtual environment that keeps the users within the appropriately chosen state (higher or lower intensity) for approximately twice the time compared to the pre-recorded static virtual simulation. Thus, such a system can increase the efficiency of VR stimulations for the treatment of central nervous system dysfunctions, as it provides numerically more controlled sessions without unexpected variations.

Copyright © 2021 Kritikos, Alevizopoulos and Koutsouris.

Keywords: electrodermal activity sensor; electrophysiology; human-computer interaction; mental illnesses; neurological illnesses; noninvasive device; real time adaptation; virtual reality

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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