TY - GEN
T1 - Real-time Control of UGV Robot in Gazebo Simulator using P300-based Brain-Computer Interface
AU - Nuaimi, Fatima Ali Al
AU - Zeddoug, Jamal
AU - Belkacem, Abdelkader Nasreddine
N1 - Funding Information:
This research was funded by Technology Innovation Institute.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were appropriately provided (data acquisition system, user interface design, BCI data architecture, ROS/ robot Jackal, and implementation and tests). Furthermore, the project implementation and some solutions to possible issues were posed. The project outcomes were assessed by employing BCI in a simulation; before implementing real-time, to determine errors and resolve the validity of the project scope.
AB - Brain computer interface (BCI)-based virtual environment control has found broad applications in solving and pursuing factual healthcare issues concerning efficiency, safety, and costs. In this technical paper, an unmanned ground vehicle (UGV) robot with a simulator-equipped BCI system was utilized. The Gazebo simulator was employed to develop a simulated setting. The software CitySim World allowed rendering the simulated milieu more down-to-earth. A non-invasive electroencephalogram (EEG)-based BCI was used to follow the brain signals and extract the P300 component, a kind of simultaneous BCI controlling procedure for safe, fast, and inexpensive implementation. This UGV control system using human brain activity can be beneficial for the real UGV platform control. It enables the discovery of the probable errors in the physical implementation. All the steps implementing our BCI system were appropriately provided (data acquisition system, user interface design, BCI data architecture, ROS/ robot Jackal, and implementation and tests). Furthermore, the project implementation and some solutions to possible issues were posed. The project outcomes were assessed by employing BCI in a simulation; before implementing real-time, to determine errors and resolve the validity of the project scope.
KW - Brain computer interface (BCI)
KW - Electroencephalogram (EEG)
KW - P300 speller
KW - Simulation
KW - Unmanned ground vehicle (UGV)
UR - http://www.scopus.com/inward/record.url?scp=85146689659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146689659&partnerID=8YFLogxK
U2 - 10.1109/BIBM55620.2022.9995623
DO - 10.1109/BIBM55620.2022.9995623
M3 - Conference contribution
AN - SCOPUS:85146689659
T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
SP - 2660
EP - 2666
BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
A2 - Adjeroh, Donald
A2 - Long, Qi
A2 - Shi, Xinghua
A2 - Guo, Fei
A2 - Hu, Xiaohua
A2 - Aluru, Srinivas
A2 - Narasimhan, Giri
A2 - Wang, Jianxin
A2 - Kang, Mingon
A2 - Mondal, Ananda M.
A2 - Liu, Jin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Y2 - 6 December 2022 through 8 December 2022
ER -
