Simulated driving states altered EEG power bands compared to a resting state, with binary machine-learning models showing near-perfect accuracy in distinguishing the two states.
Observational (n=17)
Does driving in different virtual environments alter EEG-based neurological biomarkers compared to a resting state in healthy male drivers?
EEG-based neurological biomarkers can accurately distinguish between resting and driving states, suggesting potential utility for cognitive workload monitoring in advanced driver-assistance systems.
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).
Hussain et al. (Thu,) conducted a observational in Healthy (n=17). Simulated driving states (city-roadways and expressway) vs. Resting state was evaluated on Neurological biomarkers (EEG power bands) and machine-learning classification accuracy. Simulated driving states altered EEG power bands compared to a resting state, with binary machine-learning models showing near-perfect accuracy in distinguishing the two states.