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CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation

Brain–computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi...

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Detalles Bibliográficos
Autores principales: Li, Mengfan, Wei, Ran, Zhang, Ziqi, Zhang, Pengfei, Xu, Guizhi, Liao, Wenzhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AAAS 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202181/
https://www.ncbi.nlm.nih.gov/pubmed/37223547
http://dx.doi.org/10.34133/cbsystems.0024
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author Li, Mengfan
Wei, Ran
Zhang, Ziqi
Zhang, Pengfei
Xu, Guizhi
Liao, Wenzhe
author_facet Li, Mengfan
Wei, Ran
Zhang, Ziqi
Zhang, Pengfei
Xu, Guizhi
Liao, Wenzhe
author_sort Li, Mengfan
collection PubMed
description Brain–computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.
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spelling pubmed-102021812023-05-23 CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation Li, Mengfan Wei, Ran Zhang, Ziqi Zhang, Pengfei Xu, Guizhi Liao, Wenzhe Cyborg Bionic Syst Research Article Brain–computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments. AAAS 2023-04-18 /pmc/articles/PMC10202181/ /pubmed/37223547 http://dx.doi.org/10.34133/cbsystems.0024 Text en Copyright © 2023 Mengfan Li et al. https://creativecommons.org/licenses/by/4.0/Exclusive Licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research Article
Li, Mengfan
Wei, Ran
Zhang, Ziqi
Zhang, Pengfei
Xu, Guizhi
Liao, Wenzhe
CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title_full CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title_fullStr CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title_full_unstemmed CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title_short CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation
title_sort cvt-based asynchronous bci for brain-controlled robot navigation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10202181/
https://www.ncbi.nlm.nih.gov/pubmed/37223547
http://dx.doi.org/10.34133/cbsystems.0024
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