EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Gianluca Di Flumeri; Vincenzo Ronca; Andrea Giorgi; Alessia Vozzi; Pietro Aricò; Nicolina Sciaraffa; Hong Zeng; Guojun Dai; Wanzeng Kong; Fabio Babiloni; Gianluca Borghini

doi:10.3389/fnhum.2022.866118

EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Front Hum Neurosci. 2022 May 20:16:866118. doi: 10.3389/fnhum.2022.866118. eCollection 2022.

Authors

Gianluca Di Flumeri^{1

2}, Vincenzo Ronca^{2

3}, Andrea Giorgi^{2

3}, Alessia Vozzi^{2

3}, Pietro Aricò^{1

2}, Nicolina Sciaraffa², Hong Zeng⁴, Guojun Dai⁴, Wanzeng Kong⁴, Fabio Babiloni^{1

2

4}, Gianluca Borghini^{1

2}

Affiliations

¹ Laboratory of Industrial Neuroscience, Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
² BrainSigns srl, Rome, Italy.
³ Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, Rome, Italy.
⁴ School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China.

Abstract

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, 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 onset 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 EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Keywords: EEG; cognitive neuroscience; driving performance; drowsiness; human factor; neuroergonomics; neurometrics; road safety.