The integration of physiological monitoring into the human–machine interface holds great promise both for real-time assessment of operator status and for providing a mean to allocate tasks between machines and humans based on the operator status. Our group, aiming to provide a new humanmachine interface to improve traffic safety using brain signals, has conducted a number of researches for the driver states monitoring based on EEG data in recent years.

This article presents our study for the representation of mental workload using EEG data. A simulated driving task - the Lane Change Task (LCT), combined with a secondary auditory task - the Paced Auditory Addition Serial Task (PASAT), was adopted to simulate the situation of in-vehicle conversations. Participants were requested to perform the lane change task under three task conditions - primary LCT, LCT with a slow PASAT and LCT with a fast PASAT.

The EEG recordings combined with performance data from LCT and PASAT provided plenty information for comprehensive understanding of driver’s workload. The analysis of event-related potentials (ERP) revealed that LCT evoked cognitive responses, such as P2, N2, P3b, CNV, and the amplitudes of P3b decreased with the task load. A crucial benefit of these findings is that the increase or decrease of amplitudes of ERP components can be directly used for representing driver’s mental workload.