Dynamic Characteristics of Electroencephalogram Reflecting Driving-Experience-Dependent Performance Using Microstates

To further reduce the number of traffic accidents, it has been necessary in recent years to develop advanced driver assistance systems with functions that provide optimal assistance based on driving performance. Recent studies have used electroencephalogram (EEG) to comprehensively evaluate the complex neural process of visual, perceptual, attentional, and motor functions. Microstate analysis, which reflects the interactions of global neural networks by measuring the temporal transition of brain activity, is widely used to capture complex cognitive processes. In this context, we hypothesized that microstate analysis is a suitable method for capturing driving-experience-dependent performance using EEG. To prove our hypothesis, we evaluated the EEG data of novice and expert drivers while they viewed driving scenes using microstate analysis to detect driving-experience-dependent performance using EEG. The results showed that the dynamic characteristic of EEG concerning driving-experience-dependent performance reflected efficient cognitive control and suppressed redundant bottom-up attention. This finding may be widely utilized for estimating the driving-experience-dependent performance of advanced driver assistant systems.