EEG-Based Index for Timely Detecting User’s Drowsiness Occurrence in Automotive Applications - Di Flumeri, Gianluca; Ronca, Vincenzo; Giorgi, Andrea; Vozzi, Alessia; Aricò, Pietro; Sciaraffa, Nicolina; Zeng, Hong; Dai, Guojun; Kong, Wanzeng; Babiloni, Fabio; Borghini, Gianluca (Frontiers in Human Neuroscience, 2022)
Human errors are widely considered among the major causes of road traffic. Furthermore, it is estimated that more than 90% of vehicles crashes causing fatal and permanent injuring are directly related with mental tiredness, fatigue and drowsiness of the drivers. In particular, the driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the insurgence of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic Electroencephalographic (EEG)-based index able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the Eye Blinks Rate and the Heart Rate Variability, and to subjective measurements (self - reports).