Reducing Brain-computer Interaction Training Time with Embodied Virtual Avatar

Filip Škola, Fotis Liarokapis

2022

Abstract

Brain-computer interfaces (BCI) have been intensely researched to provide a method for controlling computers, robots, and other machinery using mental activity only. Nevertheless, BCIs remain difficult to use in everyday life. One of the major BCI paradigms, the motor imagery (MI), showed improved control performance when avatar embodiment in virtual reality (VR) was exploited in the BCI system. Control accuracy was further increased with gamification of the MI-BCI training procedure. This paper presents comparative study of 3 types of MI-BCI training: with the standard protocol, mediated using a virtual avatar, and in a gamified, embodied setting with progressive increase of the training pace. Overall analysis of the relationship between embodiment and BCI performance showed robust embodiment illusion supported by correlation between the sense of ownership towards the avatar and the sense of agency towards the BCI actions. Interestingly, the actual control proficiency was uncorrelated to the perceived performance and to the sense of ownership. This could work towards facilitation of the initial training steps similarly to designs exploiting positively biased feedback.

Paper Citation

in Harvard Style

Škola F. and Liarokapis F. (2022). Reducing Brain-computer Interaction Training Time with Embodied Virtual Avatar. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: VISIGRAPP, ISBN 978-989-758-555-5, pages 7-17. DOI: 10.5220/0011049100003124

in Bibtex Style

@conference{visigrapp22,
author={Filip Škola and Fotis Liarokapis},
title={Reducing Brain-computer Interaction Training Time with Embodied Virtual Avatar},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: VISIGRAPP,},
year={2022},
pages={7-17},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011049100003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: VISIGRAPP,
TI - Reducing Brain-computer Interaction Training Time with Embodied Virtual Avatar
SN - 978-989-758-555-5
AU - Škola F.
AU - Liarokapis F.
PY - 2022
SP - 7
EP - 17
DO - 10.5220/0011049100003124