EEG-Based Mental Workload Assessment During Real Driving

Car driving is considered a very complex activity, consisting of different main tasks and subtasks. For this reason, in particular situations the cognitive demand on the driver can be very high, and this large mental workload decreases performance and increases the probability of error commission. In this preliminary study a workload index based on electroencephalography (EEG), i.e., brain activity of eight drivers in real traffic conditions, is 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 that differ in terms of difficulty, i.e., easy and hard. Eye-tracking technology was employed to validate EEG-based results. This EEG-based workload index could allow researchers to assess objectively, and even online, the mental workload experienced by drivers, and it thus forms a powerful tool for neuroergonomics research.