Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

Various driver’s vigilance estimation techniques currently exist in the literature. But none of them estimates the driver’s vigilance in the complexity domain. In this research, we propose the recently introduced multivariate multiscale entropy method to fill the above mentioned research gap. We apply this technique to differential entropy features of electroencephalogram and electrooculogram signals to detect driver’s vigilance. Also, we employ it to the percentage of eye closure values to analyse the driver’s cognitive states (awake, tired and drowsy) in the complexity domain. The contribution of this research is to efficiently classify the driver’s cognitive states using a new feature based on multivariate multiscale entropy. The experimental complexity profile curves show the statistically significant differences (p < 0.01) among brain electroencephalogram, forehead electroencephalogram and electrooculogram signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the support vector machine, a machine learning technique, discriminates the driver’s cognitive states with a promising classification accuracy of 76.2%. Therefore, the complexity profile of driver’s cognitive states could be an indicator for vigilance estimation.