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Article

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

1
BrainSigns Srl, Lungotevere Michelangelo 9, 00192 Rome, Italy
2
Department of Molecular Medicine, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy
3
ITCL Technology Centre, C. López Bravo, 70, 09001 Burgos, Spain
4
College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310005, China
*
Author to whom correspondence should be addressed.
Academic Editor: Christian Collet
Brain Sci. 2022, 12(3), 304; https://doi.org/10.3390/brainsci12030304
Received: 30 December 2021 / Revised: 11 February 2022 / Accepted: 22 February 2022 / Published: 24 February 2022
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. View Full-Text
Keywords: stress; EEG; driving; random forest; wet EEG sensors stress; EEG; driving; random forest; wet EEG sensors
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MDPI and ACS Style

Sciaraffa, N.; Di Flumeri, G.; Germano, D.; Giorgi, A.; Di Florio, A.; Borghini, G.; Vozzi, A.; Ronca, V.; Varga, R.; van Gasteren, M.; Babiloni, F.; Aricò, P. Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving. Brain Sci. 2022, 12, 304. https://doi.org/10.3390/brainsci12030304

AMA Style

Sciaraffa N, Di Flumeri G, Germano D, Giorgi A, Di Florio A, Borghini G, Vozzi A, Ronca V, Varga R, van Gasteren M, Babiloni F, Aricò P. Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving. Brain Sciences. 2022; 12(3):304. https://doi.org/10.3390/brainsci12030304

Chicago/Turabian Style

Sciaraffa, Nicolina, Gianluca Di Flumeri, Daniele Germano, Andrea Giorgi, Antonio Di Florio, Gianluca Borghini, Alessia Vozzi, Vincenzo Ronca, Rodrigo Varga, Marteyn van Gasteren, Fabio Babiloni, and Pietro Aricò. 2022. "Validation of a Light EEG-Based Measure for Real-Time Stress Monitoring during Realistic Driving" Brain Sciences 12, no. 3: 304. https://doi.org/10.3390/brainsci12030304

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