The car driving is considered a very complex activity, consisting of different main tasks and subtasks. For such a reason, in particular situations the cognitive demand to the driver could be very high, inducing a strong mental workload and consequently a performance decreasing and an error commission probability increasing. In this preliminary study, a Workload Index based on the Electroencephalographic (EEG), i.e. brain, activity of eight drivers in real traffic conditions has been validated. In particular, by means of this objective Workload Index, it has been possible to classify correctly, with accuracy higher than 75%, two driving conditions different in terms of difficulty, i.e. Easy and Hard. Eye-Tracking technology was also employed to validate EEG-based results. Such a EEG-based Workload Index could allow to assess objectively, and even online, the mental workload experienced by the driver, standing out as a powerful tool for Neuroergonomics research.
Di Flumeri G., B.G. (2019). EEG-based mental workload assessment during real driving: a taxonomic tool for neuroergonomics in highly automated environments. London : Academic Press [10.1016/B978-0-12-811926-6.00020-8].
EEG-based mental workload assessment during real driving: a taxonomic tool for neuroergonomics in highly automated environments
Borghini G.;Vignali V.;Lantieri C.;Bichicchi A.;Simone A.;
2019
Abstract
The car driving is considered a very complex activity, consisting of different main tasks and subtasks. For such a reason, in particular situations the cognitive demand to the driver could be very high, inducing a strong mental workload and consequently a performance decreasing and an error commission probability increasing. In this preliminary study, a Workload Index based on the Electroencephalographic (EEG), i.e. brain, activity of eight drivers in real traffic conditions has been validated. In particular, by means of this objective Workload Index, it has been possible to classify correctly, with accuracy higher than 75%, two driving conditions different in terms of difficulty, i.e. Easy and Hard. Eye-Tracking technology was also employed to validate EEG-based results. Such a EEG-based Workload Index could allow to assess objectively, and even online, the mental workload experienced by the driver, standing out as a powerful tool for Neuroergonomics research.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
