Biblio

Mental workload is a popular concept in ergonomics as it provides an intuitive explanation why exceedingly cognitive task demands result in a decrease in task performance and increase the risk of fatal incidents while driving. At the same time, affective states such as frustration, also play a role in traffic safety as they increase the tendency for speedy and aggressive driving and may even degrade cognitive processing capacities. To reduce accidents due to dangerous effects of degraded cognitive processing capacities and affective biases causing human errors, it is necessary to continuously assess multiple user states simultaneously to better understand potential interactions. In two previous studies, we measured brain activity with functional near-infrared spectroscopy (fNIRS) for separate brain based prediction of working memory load (WML) (Unni et al., 2017) and frustration levels (Ihme et al. submitted) while driving. Here, we report results from a study designed to predict simultaneously manipulated WML and frustration using data driven machine learning approaches from whole-head fNIRS brain activation measurements.