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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/21/6985 |
| _version_ | 1827677887237455872 |
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| author | Iqram Hussain Seo Young Se-Jin Park |
| author_facet | Iqram Hussain Seo Young Se-Jin Park |
| author_sort | Iqram Hussain |
| collection | DOAJ |
| 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). |
| first_indexed | 2024-03-10T05:53:00Z |
| format | Article |
| id | doaj.art-8331ee93d9954fa693f6884b8c9193e4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:00Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8331ee93d9954fa693f6884b8c9193e42023-11-22T21:35:03ZengMDPI AGSensors1424-82202021-10-012121698510.3390/s21216985Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance SystemIqram Hussain0Seo Young1Se-Jin Park2Center for Medical Convergence Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, KoreaCenter for Medical Convergence Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, KoreaCenter for Medical Convergence Metrology, Korea Research Institute of Standards and Science, Daejeon 34113, KoreaPhysiological 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).https://www.mdpi.com/1424-8220/21/21/6985electroencephalogramphysiological biomarkeradvanced driver assistance system (ADAS)mental workloaddriving simulator |
| spellingShingle | Iqram Hussain Seo Young Se-Jin Park Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System Sensors electroencephalogram physiological biomarker advanced driver assistance system (ADAS) mental workload driving simulator |
| title | 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_short | Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System |
| title_sort | driving induced neurological biomarkers in an advanced driver assistance system |
| topic | electroencephalogram physiological biomarker advanced driver assistance system (ADAS) mental workload driving simulator |
| url | https://www.mdpi.com/1424-8220/21/21/6985 |
| work_keys_str_mv | AT iqramhussain drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem AT seoyoung drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem AT sejinpark drivinginducedneurologicalbiomarkersinanadvanceddriverassistancesystem |