Abstract
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).
Highlights
Car driving demands low-level physical activities and heavy mental workloads, to deal with complex driving environments
We developed a neurological state prediction model to classify the neurological responses in different driving environments
Global indicates the average measures of the features of the frontal and occipital lobes
Summary
Car driving demands low-level physical activities and heavy mental workloads, to deal with complex driving environments. Driving is a complicated attention-intensive and cognitively demanding task, and involves driving skills, understanding road scenarios, and drivers’ behavior [1]. The increased cognitive demand on the brain gives the drivers fatigue and subsequently drowsiness and boredom. The increased cognitive load is a significant source of road accidents and fatalities, which demands extensive studies. Modern vehicles are equipped with various luxurious components and driving information systems, such as navigation systems, on-board lifestyle accessories, personal communication devices, and music systems, which contribute to mental distraction [2]. Road traffic systems, including traffic signals, multi-lane roads, and information boards, and road conditions, such as road curvature and road slopes, affect the cognitive workload of the driver
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