Reliability Analysis of Driving Behaviour in Road Traffic System Considering Synchronization of Neural Activity

Abstract

This paper aims to disclose the reliability of driving behaviour in road traffic system. For this purpose, the drivers' electroencephalography (EEG) signals were collected with Emotiv, a portable device, and used for an experiment in actual driving environment. Through the analysis on the synchronization of 14-channel EGG signals, the author identified a proper threshold, and determined whether the brain network nodes are connected or not. On this basis, a brain network model was created for the drivers. The driving behaviour reliability of the drivers was discussed in detailed considering brain network parameters (clustering coefficient and global efficiency) and the power spectrum features of EEG signals. The research results show that, with the increase in driving time, the intercity drivers became increasingly fatigued and their brain network continued to densify, pushing up the network parameters like clustering coefficient and global efficiency. In this case, the neuronal activities became increasingly synchronized across the brain regions. In addition, the two brain network parameters of the drivers were less discrete and more accurate than the fatigue indicator of EEG power spectrum features. Therefore, the analysis of brain network parameters is a precise and feasible method for discussing driving behaviour reliability.

Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study

“…Moreover, we found that significantly altered FCs exhibited a long-range frontal-related pattern, leading to increased global and local properties of brain network [38]. These results are consistent with previous findings of other researchers in the study of fatigue-related alterations in brain network [52], [53].…”

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

“…Previous studies have shown that EEG equipment with fewer electrodes (Emotiv) can effectively monitor people’s driving fatigue [24,25,26,27]. The Emotiv EEG acquisition equipment has 14 electrodes (14 channels = AF3, AF4, F3, F4, FC5, FC6, F7, F8, T7, T8, P7, P8, O1, and O2), which is consistent with the 14 electrodes (14 channels = F3, F4, F7, F8, FT7, FT8, C3, C4, TP7, TP8, P3, P4, O1, and O2) on the Neuroscan device selected in this paper.…”

Applications of brain imaging methods in driving behaviour research

“…ensemble) entropy measures (Kar et Subjective measures of self-reported sleepiness, predominantly Karolinska Sleepiness Scale (KSS) (Åkerstedt and Gillberg 1990), have been the most common measure as the ground truth of the classification methods in these studies (Kar et al 2010), although this measure has often been supplemented by other physiological or behavioural indicators as well (Zhao et al 2011, Morales et al 2017. While the predominant method of experimentation in this category of studies has been simulated driving, a limited number of studies have tested ADSD system applications in real-world field driving settings (Perrier et al 2016, He et al 2018). In the majority of studies in this category, sleepiness is involved by sustained and monotonous driving in the simulated (or field) setting.…”