Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/19164
Title: Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment
Authors: Škola, Filip 
Tinková, Simona 
Liarokapis, Fotis 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: body ownership transfer; brain-computer interface; embodiment; gamification; motor imagery
Issue Date: 26-Sep-2019
Source: Frontiers in human neuroscience, 2019 vol. 13
Volume: 13
Project: Advanced VR, iMmersive serious games and Augmented REality as tools to raise awareness and access to European underwater CULTURal heritage 
Journal: Frontiers in human neuroscience 
Abstract: This paper presents a gamified motor imagery brain-computer interface (MI-BCI) training in immersive virtual reality. The aim of the proposed training method is to increase engagement, attention, and motivation in co-adaptive event-driven MI-BCI training. This was achieved using gamification, progressive increase of the training pace, and virtual reality design reinforcing body ownership transfer (embodiment) into the avatar. From the 20 healthy participants performing 6 runs of 2-class MI-BCI training (left/right hand), 19 were trained for a basic level of MI-BCI operation, with average peak accuracy in the session = 75.84%. This confirms the proposed training method succeeded in improvement of the MI-BCI skills; moreover, participants were leaving the session in high positive affect. Although the performance was not directly correlated to the degree of embodiment, subjective magnitude of the body ownership transfer illusion correlated with the ability to modulate the sensorimotor rhythm.
URI: https://ktisis.cut.ac.cy/handle/10488/19164
ISSN: 1662-5161
DOI: 10.3389/fnhum.2019.00329
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Type: Article
Affiliation : Masaryk University 
Appears in Collections:Άρθρα/Articles

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