Evaluation of the User Adaptation in a BCI Game Environment
Open Access
- 12 December 2022
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 12 (24) , 12722
Abstract
Brain-computer interface (BCI) technology is a developing field of study with numerous applications. The purpose of this paper is to discuss the use of brain signals as a direct communication pathway to an external device. In this work, Zombie Jumper is developed, which consists of 2 brain commands, imagining moving forward and blinking. The goal of the game is to jump over static or moving “zombie” characters in order to complete the level. To record the raw EEG data, a Muse 2 headband is used, and the OpenViBE platform is employed to process and classify the brain signals. The Unity engine is used to build the game, and the lab streaming layer (LSL) protocol is the connective link between Muse 2, OpenViBE and the Unity engine for this BCI-controlled game. A total of 37 subjects tested the game and played it at least 20 times. The average classification accuracy was 98.74%, ranging from 97.06% to 99.72%. Finally, playing the game for longer periods of time resulted in greater control.Keywords
Funding Information
- AGROTOUR–New Technologies and Innovative Approaches to Agri-Food and Tourism to Boost Regional Excellence in Western Macedonia (MIS 5047196, NSRF 2014-2020, T2EΔ K-02438)
- Competitiveness, Entrepreneurship and Innovation (MIS 5047196, NSRF 2014-2020, T2EΔ K-02438)
- European Union and Greek national funds through Operational Program Competitiveness, Entrepreneurship and Innovation under the call RESEARCH—CREATE—INNOVATE: “Intelli– WheelChair (MIS 5047196, NSRF 2014-2020, T2EΔ K-02438)
This publication has 20 references indexed in Scilit:
- Mental War: An Attention-Based Single/Multiplayer Brain-Computer Interface Game2018
- More attention, less deficit: Wearable EEG-based serious game for focus improvement2017
- Trends in EEG-BCI for daily-life: Requirements for artifact removalBiomedical Signal Processing and Control, 2017
- Low complexity approach for controlling a robotic arm using the Emotiv EPOC headset2016
- Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based brain-computer interface2016
- Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail?Frontiers in Neuroscience, 2016
- A Novel Brain Computer Interface Based on Principle Component AnalysisProcedia Computer Science, 2016
- Development of a mind-controlled Android racing game using a brain computer interface (BCI)2014
- Brain-Computer Interfaces in MedicineMayo Clinic Proceedings, 2012
- Brain Computer Interfaces, a ReviewSensors, 2012