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 human-machineinterface to improve traffic safety using brain signals, has conducted a number of researches for the driver states monitoring based on EEG data inrecent 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. The full text of this paper may be found at: http://www-nrd.nhtsa.dot.gov/pdf/esv/esv21/09-0250.pdf For the covering abstract see ITRD E145407.
Abstract