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How Immersed Are You? State of the Art of the Neurophysiological Characterization of Embodiment in Mixed Reality for Out-of-the-Lab Applications.

Authors :
Ronca, Vincenzo
Ricci, Alessia
Capotorto, Rossella
Di Donato, Luciano
Freda, Daniela
Pirozzi, Marco
Palermo, Eduardo
Mattioli, Luca
Di Gironimo, Giuseppe
Coccorese, Domenico
Buonocore, Sara
Massa, Francesca
Germano, Daniele
Di Flumeri, Gianluca
Borghini, Gianluca
Babiloni, Fabio
Aricò, Pietro
Source :
Applied Sciences (2076-3417); Sep2024, Vol. 14 Issue 18, p8192, 24p
Publication Year :
2024

Abstract

Mixed Reality (MR) environments hold immense potential for inducing a sense of embodiment, where users feel like their bodies are present within the virtual space. This subjective experience has been traditionally assessed using subjective reports and behavioral measures. However, neurophysiological approaches offer unique advantages in objectively characterizing embodiment. This review article explores the current state of the art in utilizing neurophysiological techniques, particularly Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal activity (EDA), to investigate the neural and autonomic correlates of embodiment in MR for out-of-the-lab applications. More specifically, it was investigated how EEG, with its high temporal resolution, PPG, and EDA, can capture transient brain activity associated with specific aspects of embodiment, such as visuomotor synchrony, visual feedback of a virtual body, and manipulations of virtual body parts. The potential of such neurophysiological signals to differentiate between subjective experiences of embodiment was discussed, with a particular regard to identify the neural and autonomic markers of early embodiment formation during MR exposure in real settings. Finally, the strengths and limitations of the neurophysiological approach in the context of MR embodiment research were discussed, in order to achieve a more comprehensive understanding of this multifaceted phenomenon. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
18
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
180047575
Full Text :
https://doi.org/10.3390/app14188192