Classification of Driver's Cognitive Responses Using Nonparametric Single-trial EEG Analysis

Accidents caused by errors and failures in human performance among traffic fatalities have a high rate causing death and become an important issue in public security. The key problem causing these car accidents is mainly because that the drivers failed to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for ongoing assessment of driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. Three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), discriminant analysis feature extraction (DAFE) are applied to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data can be classified with fewer features and their classification results are compared by utilizing three different classifiers including Gaussian classifier (GC), k nearest neighbor classification (KNNC), and naive Bayes classifier (NBC). Experimental results show that the successful rate of nonparametric weighted feature extraction combined with Gaussian classifier is higher more than 10% compared with other combinations. It also demonstrates the feasibility of detecting and analyzing single-trail ERP signals that represent operators' cognitive states and responses to task events.