LAUSR

Cognitive workload is an internal factor that can be influenced by external factors. When drivers are distracted by these internal and external influences, their ability to drive safely can be compromised. Electroencephalogram (EEG) is a non-invasive neuroimaging technique that measures the electrical activity of the brain. and it can provide information about brain activity related to various cognitive and motor functions. In this study, on-road driving experiments were conducted to investigate the EEG characteristics of drivers under different levels of cognitive workload generated through mathematical calculations with varying difficulties. The collected EEG signals were processed using continuous wavelet transform to decompose them into delta, theta, alpha, beta, and gamma waveforms. Numerical values and power spectral characteristics of each waveband in the left and right frontal regions of the driver were then analyzed. Furthermore, the study utilized an improved multiscale sample entropy algorithm to analyze the EEG entropy characteristics of drivers in different states. The results indicate that under different degrees of cognitive workload, the amplitude of delta, theta, and alpha waves decreased, while beta and gamma waves showed significant changes. The improved multiscale sample entropy algorithm provided a more objective assessment of changes in entropy across different wavebands and stages, with smaller entropy fluctuation ranges and more accurate complexity changes. Overall, this research provides valuable insights into the characteristics of drivers under cognitive workload, contributing to the analysis of human factors in driving safety. These findings can inform the development of strategies to mitigate the adverse effects of cognitive workload on driving performance and enhance overall road safety.