A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions.

The drivers' distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to "detect" eventual events, road traffic, and additional driving tasks that might contribute to the drivers' distraction. Therefore, the present study aimed at developing an EEG-based "Distraction index" obtained by the combination of the driver's mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver's attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers' distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers' distraction (all p < 0.01).

Avionic technology testing by using a cognitive neurometric index: A study with professional helicopter pilots.

In this study, we investigated the possibility to evaluate the impact of different avionic technologies on the mental workload of helicopter's pilots by measuring their brain activity with the EEG during a series of simulated missions carried out at AgustaWestland facilities in Yeovil (UK). The tested avionic technologies were: i) Head-Up Display (HUD); ii) Head-Mounted Display (HMD); iii) Full Conformal symbology (FC); iv) Flight Guidance (FG) symbology; v) Synthetic Vision System (SVS); and vi) Radar Obstacles (RO) detection system. It has been already demonstrated that in cognitive tasks, when the cerebral workload increases the EEG power spectral density (PSD) in theta band over frontal areas increases, and the EEG PSD in alpha band decreases over parietal areas. A mental workload index (MWL) has been here defined as the ratio between the frontal theta and parietal alpha EEG PSD values. Such index has been used for testing and comparing the different avionic technologies. Results suggested that the HUD provided a significant (p<;.05) workload reduction across all the flight scenarios with respect to the other technologies. In addition, the simultaneous use of FC and FG technologies (FC+FG) produced a significant decrement of the workload (p<;.01) with respect to the use of only the FC. Moreover, the use of the SVS technology provided on Head Down Display (HDD) with the simultaneous use of FC+FG and the RO seemed to produce a lower cerebral workload when compared with the use of only the FC. Interestingly, the workload estimation by means of subjective measures, provided by pilots through a NASA-TLX questionnaire, did not provide any significant differences among the different flight scenarios. These results suggested that the proposed MWL cognitive neurometrics could be used as a reliable measure of the user's mental workload, being a valid indicator for the comparison and the test of different avionic technologies.