Ricerc@Sapienza

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability.

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.

There is growing interest for implementing tools to monitor cognitive performance in naturalistic environments. Recent technological progress has allowed the development of new generations of brain imaging systems such as dry electrode electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. These highly portable brain imaging devices offer interesting prospects to implement passive brain computer interfaces (pBCI) and neuroadaptive technology.