Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological ass...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Iqram Hussain, Seo Young, Se-Jin Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	electroencephalogram physiological biomarker advanced driver assistance system (ADAS) mental workload driving simulator Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8331ee93d9954fa693f6884b8c9193e4
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8331ee93d9954fa693f6884b8c9193e4
record_format	dspace
spelling	oai:doaj.org-article:8331ee93d9954fa693f6884b8c9193e42021-11-11T19:02:24ZDriving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System10.3390/s212169851424-8220https://doaj.org/article/8331ee93d9954fa693f6884b8c9193e42021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6985https://doaj.org/toc/1424-8220Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).Iqram HussainSeo YoungSe-Jin ParkMDPI AGarticleelectroencephalogramphysiological biomarkeradvanced driver assistance system (ADAS)mental workloaddriving simulatorChemical technologyTP1-1185ENSensors, Vol 21, Iss 6985, p 6985 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	electroencephalogram physiological biomarker advanced driver assistance system (ADAS) mental workload driving simulator Chemical technology TP1-1185
spellingShingle	electroencephalogram physiological biomarker advanced driver assistance system (ADAS) mental workload driving simulator Chemical technology TP1-1185 Iqram Hussain Seo Young Se-Jin Park Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
description	Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).
format	article
author	Iqram Hussain Seo Young Se-Jin Park
author_facet	Iqram Hussain Seo Young Se-Jin Park
author_sort	Iqram Hussain
title	Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
title_short	Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
title_full	Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
title_fullStr	Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
title_full_unstemmed	Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System
title_sort	driving-induced neurological biomarkers in an advanced driver-assistance system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8331ee93d9954fa693f6884b8c9193e4
work_keys_str_mv	AT iqramhussain drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem AT seoyoung drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem AT sejinpark drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem
_version_	1718431656187002880

Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Ejemplares similares