TY  - JOUR
PY  - 2022//
TI  - Validation of a light EEG-based measure for real-time stress monitoring during realistic driving
JO  - Brain sciences
A1  - Sciaraffa, Nicolina
A1  - Di Flumeri, Gianluca
A1  - Germano, Daniele
A1  - Giorgi, Andrea
A1  - Di Florio, Antonio
A1  - Borghini, Gianluca
A1  - Vozzi, Alessia
A1  - Ronca, Vincenzo
A1  - Varga, Rodrigo
A1  - van Gasteren, Marteyn
A1  - Babiloni, Fabio
A1  - Aricò, Pietro
SP  - e304
EP  - e304
VL  - 12
IS  - 3
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2076-3425
UR  - http://dx.doi.org/10.3390/brainsci12030304
ID  - ref1
ER  -