Real-time workload assessment using EEG signals in virtual reality environment

Shen Ren, Fabio Babiloni, Nitish V. Thakor, Anastasios Bezerianos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The next generation human-computer interaction requires, a close-to-real, virtual reality environment and a more sensitive real-time brain states monitoring to account for instantaneous human thoughts and feelings. Within this goal area, workload evaluation of human subject is extraordinary critical to both safety and security of risk-sensitive domains, including flight operations and mission success. In this paper, we present a non-intrusive workload assessment framework by real-time processing of continuous EEG signals measured from “pilots” during flight operations. Our framework has been experimented on a 2D computer screen based flight simulation platform (MATB-II, Revised Multi-Attribute Task Battery) and a virtual reality-based realistic flight simulator. This novel framework has the potential of reducing task overloads and improving performance in risk-sensitive domains.

Original languageEnglish
Title of host publicationIFMBE Proceedings
PublisherSpringer Verlag
Pages1345-1346
Number of pages2
Volume57
ISBN (Print)9783319327013
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016 - Paphos, Cyprus
Duration: Mar 31 2016Apr 2 2016

Other

Other14th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2016
CountryCyprus
CityPaphos
Period3/31/164/2/16

Keywords

  • EEG
  • Flight simulation
  • Real-time
  • Virtual reality
  • Workload

ASJC Scopus subject areas

  • Biomedical Engineering
  • Bioengineering

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  • Cite this

    Ren, S., Babiloni, F., Thakor, N. V., & Bezerianos, A. (2016). Real-time workload assessment using EEG signals in virtual reality environment. In IFMBE Proceedings (Vol. 57, pp. 1345-1346). Springer Verlag. https://doi.org/10.1007/978-3-319-32703-7_258