A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.

When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.

Evaluation of a New Lightweight EEG Technology for Translational Applications of Passive Brain-Computer Interfaces.

Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.

Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving.

Driver's stress affects decision-making and the probability of risk occurrence, and it is therefore a key factor in road safety. This suggests the need for continuous stress monitoring. This work aims at validating a stress neurophysiological measure-a Neurometric-for out-of-the-lab use obtained from lightweight EEG relying on two wet sensors, in real-time, and without calibration. The Neurometric was tested during a multitasking experiment and validated with a realistic driving simulator. Twenty subjects participated in the experiment, and the resulting stress Neurometric was compared with the Random Forest (RF) model, calibrated by using EEG features and both intra-subject and cross-task approaches. The Neurometric was also compared with a measure based on skin conductance level (SCL), representing one of the physiological parameters investigated in the literature mostly correlated with stress variations. We found that during both multitasking and realistic driving experiments, the Neurometric was able to discriminate between low and high levels of stress with an average Area Under Curve (AUC) value higher than 0.9. Furthermore, the stress Neurometric showed higher AUC and stability than both the SCL measure and the RF calibrated with a cross-task approach. In conclusion, the Neurometric proposed in this work proved to be suitable for out-of-the-lab monitoring of stress levels.

Mental Effort Estimation by Passive BCI: A Cross-Subject Analysis.

Despite the technological advancements, the employment of passive brain computer interface (BCI) out of the laboratory context is still challenging. This is largely due to methodological reasons. On the one hand, machine learning methods have shown their potential in maximizing performance for user mental states classification. On the other hand, the issues related to the necessary and frequent calibration of algorithms and to the temporal resolution of the measurement (i.e. how long it takes to have a reliable state measure) are still unsolved. This work explores the performances of a passive BCI system for mental effort monitoring consisting of three frontal electroencephalographic (EEG) channels. In particular, three calibration approaches have been tested: an intra-subject approach, a cross-subject approach, and a free-calibration procedure based on the simple average of theta activity over the three employed channels. A Random Forest model has been employed in the first two cases. The results obtained during multi-tasking have shown that the cross-subject approach allows the classification of low and high mental effort with an AUC higher than 0.9, with a related time resolution of 45 seconds. Moreover, these performances are not significantly different from the intra-subject approach although they are significantly higher than the calibration-free approach. In conclusion, these results suggest that a light (three EEG channels) passive BCI system based on a Random Forest algorithm and cross-subject calibration could be a simple and reliable tool for out-of-the-lab employment.