A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Autores
Zhang, Zhao; Han, Shuning; Yi, Huaihai; Duan, Feng; Kang, Fei; Sun, Zhe; Solé Casals, Jordi; Caiafa, César Federico
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.
Fil: Zhang, Zhao. Civil Aviation University Of China; China
Fil: Han, Shuning. Universitat de Vic - Universitat Central de Catalunya ; España
Fil: Yi, Huaihai. China University Of Geosciences; China
Fil: Duan, Feng. Nankai University; China
Fil: Kang, Fei. Maebashi Institute Of Technology; Japón
Fil: Sun, Zhe. Riken; Japón
Fil: Solé Casals, Jordi. Universitat de Vic - Universitat Central de Catalunya; España. Nankai University; China. University of Cambridge; Reino Unido
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Nankai University; China
Materia
BRAIN-COMPUTER INTERFACE (BCI)
BRAIN-CONTROLLED VEHICLE (BCV)
ELECTRO-ENCEPHALOGRAM (EEG)
INTELLIGENT DRIVING TECHNOLOGY
STEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP)
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/214541
Acceder
id CONICETDig_d4b7ebcfd3501794e5eb1c3fa8f4b263
oai_identifier_str oai:ri.conicet.gov.ar:11336/214541
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A Brain-Controlled Vehicle System Based on Steady State Visual Evoked PotentialsZhang, ZhaoHan, ShuningYi, HuaihaiDuan, FengKang, FeiSun, ZheSolé Casals, JordiCaiafa, César FedericoBRAIN-COMPUTER INTERFACE (BCI)BRAIN-CONTROLLED VEHICLE (BCV)ELECTRO-ENCEPHALOGRAM (EEG)INTELLIGENT DRIVING TECHNOLOGYSTEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP)https://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.Fil: Zhang, Zhao. Civil Aviation University Of China; ChinaFil: Han, Shuning. Universitat de Vic - Universitat Central de Catalunya ; EspañaFil: Yi, Huaihai. China University Of Geosciences; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Kang, Fei. Maebashi Institute Of Technology; JapónFil: Sun, Zhe. Riken; JapónFil: Solé Casals, Jordi. Universitat de Vic - Universitat Central de Catalunya; España. Nankai University; China. University of Cambridge; Reino UnidoFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Nankai University; ChinaSpringer2022-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/214541Zhang, Zhao; Han, Shuning; Yi, Huaihai; Duan, Feng; Kang, Fei; et al.; A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials; Springer; Cognitive Computation; 15; 1; 9-2022; 159-1751866-99561866-9964CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s12559-022-10051-1info:eu-repo/semantics/altIdentifier/doi/10.1007/s12559-022-10051-1info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:51:34Zoai:ri.conicet.gov.ar:11336/214541instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:51:34.782CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
title A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
spellingShingle A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
Zhang, Zhao
BRAIN-COMPUTER INTERFACE (BCI)
BRAIN-CONTROLLED VEHICLE (BCV)
ELECTRO-ENCEPHALOGRAM (EEG)
INTELLIGENT DRIVING TECHNOLOGY
STEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP)
title_short A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
title_full A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
title_fullStr A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
title_full_unstemmed A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
title_sort A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials
dc.creator.none.fl_str_mv Zhang, Zhao
Han, Shuning
Yi, Huaihai
Duan, Feng
Kang, Fei
Sun, Zhe
Solé Casals, Jordi
Caiafa, César Federico
author Zhang, Zhao
author_facet Zhang, Zhao
Han, Shuning
Yi, Huaihai
Duan, Feng
Kang, Fei
Sun, Zhe
Solé Casals, Jordi
Caiafa, César Federico
author_role author
author2 Han, Shuning
Yi, Huaihai
Duan, Feng
Kang, Fei
Sun, Zhe
Solé Casals, Jordi
Caiafa, César Federico
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv BRAIN-COMPUTER INTERFACE (BCI)
BRAIN-CONTROLLED VEHICLE (BCV)
ELECTRO-ENCEPHALOGRAM (EEG)
INTELLIGENT DRIVING TECHNOLOGY
STEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP)
topic BRAIN-COMPUTER INTERFACE (BCI)
BRAIN-CONTROLLED VEHICLE (BCV)
ELECTRO-ENCEPHALOGRAM (EEG)
INTELLIGENT DRIVING TECHNOLOGY
STEADY-STATE VISUAL EVOKED POTENTIAL (SSVEP)
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.
Fil: Zhang, Zhao. Civil Aviation University Of China; China
Fil: Han, Shuning. Universitat de Vic - Universitat Central de Catalunya ; España
Fil: Yi, Huaihai. China University Of Geosciences; China
Fil: Duan, Feng. Nankai University; China
Fil: Kang, Fei. Maebashi Institute Of Technology; Japón
Fil: Sun, Zhe. Riken; Japón
Fil: Solé Casals, Jordi. Universitat de Vic - Universitat Central de Catalunya; España. Nankai University; China. University of Cambridge; Reino Unido
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Nankai University; China
description In this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system.
publishDate 2022
dc.date.none.fl_str_mv 2022-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/214541
Zhang, Zhao; Han, Shuning; Yi, Huaihai; Duan, Feng; Kang, Fei; et al.; A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials; Springer; Cognitive Computation; 15; 1; 9-2022; 159-175
1866-9956
1866-9964
CONICET Digital
CONICET
url http://hdl.handle.net/11336/214541
identifier_str_mv Zhang, Zhao; Han, Shuning; Yi, Huaihai; Duan, Feng; Kang, Fei; et al.; A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials; Springer; Cognitive Computation; 15; 1; 9-2022; 159-175
1866-9956
1866-9964
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s12559-022-10051-1
info:eu-repo/semantics/altIdentifier/doi/10.1007/s12559-022-10051-1
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269103169994752
score 13.13397

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