Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli - Archive ouverte HAL
Article Dans Une Revue Journal of Neural Engineering Année : 2024

Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli

Résumé

Abstract Objective. Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR. Approach. In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE. Main results. Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions. Significance. Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE.
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Dates et versions

hal-04672526 , version 1 (19-08-2024)

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Emile Savalle, Léa Pillette, Kyungho Won, Ferran Argelaguet, Anatole Lécuyer, et al.. Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli. Journal of Neural Engineering, 2024, 21 (4), pp.046015. ⟨10.1088/1741-2552/ad5cc2⟩. ⟨hal-04672526⟩
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