Electroencephalography correlates of fear of heights in a virtual reality environment

Authors

  • Andrea Apicella Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
  • Simone Barbato Idego Digital Psychology, Rome, Italy.
  • Luis Alberto Barradas Chacόn Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
  • Giovanni D'Errico Department of Applied Science and Technology, Politecnico di Torino, Turin, Italy .
  • Lucio Tommaso De Paolis Department of Engineering for Innovation University of Salento, Lecce, Italy.
  • Luigi Maffei Neuroagain Neurological and psychiatric center, Benevento, Italy.
  • Patrizia Massaro Neuroagain Neurological and psychiatric center, Benevento, Italy.
  • Giovanna Mastrati Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
  • Nicola Moccaldi Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
  • Andrea Pollastro Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
  • Selina Christin Wriessenegger Institute of Neural Engineering, Graz University of Technology, Graz, Austria.

DOI:

https://doi.org/10.21014/actaimeko.v12i2.1457

Keywords:

Electroencephalography, brain computer interfaces, fear of heights, virtual reality

Abstract

An electroencephalography (EEG)-based classification system of three levels of fear of heights is proposed. A virtual reality (VR) scenario representing a canyon was exploited to gradually expose the subjects to fear inducing stimuli with increasing intensity. An elevating platform allowed the subjects to reach three different height levels. Psychometric tools were employed to initially assess the severity of fear of heights and to assess the effectiveness of fear induction. A feasibility study was conducted on eight subjects who underwent three experimental sessions. The EEG signals were acquired through a 32-channel headset during the exposure to the eliciting VR scenario. The main EEG bands and scalp regions were explored in order to identify which are the most affected by the fear of heights. As a result, the gamma band, followed by the high-beta band, and the frontal area of the scalp resulted the most significant. The average accuracies in the within-subject case for the three-classes fear classification task, were computed. The frontal region of the scalp resulted particularly relevant and an average accuracy of (68.20 ± 11.60) % was achieved using as features the absolute powers in the five EEG bands. Considering the frontal region only, the most significant EEG bands resulted to be the high-beta and gamma bands achieving accuracies of (57.90 ± 10.10) % and of (61.30 ± 8.43) %, respectively. The Sequential Feature Selection (SFS) confirmed those results by selecting for the whole set of channels, in the 48.26 % of the cases the gamma band and in the 22.92 % the high-beta band and by achieving an average accuracy of (86.10 ± 8.29) %.

Downloads

Published

2023-05-04

Issue

Vol. 12 No. 2 (2023)

Section

Research Papers

License

Copyright (c) 2023 Andrea Apicella, Simone Barbato, Luis Alberto Barradas Chacόn, Giovanni D'Errico, Lucio Tommaso De Paolis, Luigi Maffei, Patrizia Massaro, Giovanna Mastrati, Nicola Moccaldi, Andrea Pollastro, Selina Christin Wriessenegger

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC BY 4.0, Creative Commons Attribution 4.0 International License.

Users are free to

  • share, i.e. copy and redistribute the material in any medium or format for any purpose, even commercially;
  • adapt, i.e. remix, transform, and build upon the material for any purpose, even commercially.

At the same time, the user must give appropriate credit, provide a link to the license, and indicate if changes were made.
Additional information about the license can be found at: https://creativecommons.org/licenses/by/4.0/.

Authors are

  • able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).