Title :

Detecting System Errors in Virtual Reality Using EEG Through Error-Related Potentials

Author(s) :

Si-Mohammed, Hakim [Auteur]
3D interaction with virtual environments using body and mind [Hybrid]
Lopes-Dias, Catarina [Auteur]
Graz University of Technology [Graz] [TU Graz]
Duarte, María [Auteur]
Argelaguet Sanz, Ferran [Auteur]
3D interaction with virtual environments using body and mind [Hybrid]
Jeunet, Camille [Auteur]
Cognition, Langues, Langage, Ergonomie [CLLE]
Casiez, Géry [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut Universitaire de France [IUF]
Technology and knowledge for interaction [LOKI]
Müller-Putz, Gernot [Auteur]
Graz University of Technology [Graz] [TU Graz]
Lécuyer, Anatole [Auteur]
3D interaction with virtual environments using body and mind [Hybrid]
Scherer, Reinhold [Auteur]
School of Computer Science and Electronic Engineering [Essex] [CSEE]

Conference title :

VR 2020 - 27th IEEE Conference on Virtual Reality and 3D User Interfaces

City :

Atlanta

Country :

Etats-Unis d'Amérique

Start date of the conference :

2020-03-22

Book title :

2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

Publisher :

IEEE

HAL domain(s) :

Informatique [cs]/Interface homme-machine [cs.HC]

English abstract : [en]

When persons interact with the environment and experience or witness an error (e.g. an unexpected event), a specific brain pattern, known as error-related potential (ErrP) can be observed in the electroencephalographic ...
Show more >When persons interact with the environment and experience or witness an error (e.g. an unexpected event), a specific brain pattern, known as error-related potential (ErrP) can be observed in the electroencephalographic signals (EEG). Virtual Reality (VR) technology enables users to interact with computer-generated simulated environments and to provide multi-modal sensory feedback. Using VR systems can, however, be error-prone. In this paper, we investigate the presence of ErrPs when Virtual Reality users face 3 types of visualization errors: (Te) tracking errors when manipulating virtual objects, (Fe) feedback errors, and (Be) background anomalies. We conducted an experiment in which 15 participants were exposed to the 3 types of errors while performing a center-out pick and place task in virtual reality. The results showed that tracking errors generate error-related potentials, the other types of errors did not generate such discernible patterns. In addition, we show that it is possible to detect the ErrPs generated by tracking losses in single trial, with an accuracy of 85%. This constitutes a first step towards the automatic detection of error-related potentials in VR applications, paving the way to the design of adaptive and self-corrective VR/AR applications by exploiting information directly from the user’s brain.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

European Project :

Non-invasive decoding of cortical patterns induced by goal directed movement intentions and artificial sensory feedback in humans

Comment :

Virtual conference

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL