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A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions.
Ronca, Vincenzo; Brambati, Francois; Napoletano, Linda; Marx, Cyril; Trösterer, Sandra; Vozzi, Alessia; Aricò, Pietro; Giorgi, Andrea; Capotorto, Rossella; Borghini, Gianluca; Babiloni, Fabio; Di Flumeri, Gianluca.
Afiliación
  • Ronca V; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.
  • Brambati F; BrainSigns SRL, 00198 Rome, Italy.
  • Napoletano L; DeepBlue SRL, 00185 Rome, Italy.
  • Marx C; DeepBlue SRL, 00185 Rome, Italy.
  • Trösterer S; Virtual Vehicle Research GmbH, 8010 Graz, Austria.
  • Vozzi A; Virtual Vehicle Research GmbH, 8010 Graz, Austria.
  • Aricò P; BrainSigns SRL, 00198 Rome, Italy.
  • Giorgi A; Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy.
  • Capotorto R; Department of Computer, Control, and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy.
  • Borghini G; BrainSigns SRL, 00198 Rome, Italy.
  • Babiloni F; BrainSigns SRL, 00198 Rome, Italy.
  • Di Flumeri G; Department of Anatomical, Histological, Forensic and Orthopaedic Sciences, Sapienza University of Rome, 00185 Rome, Italy.
Brain Sci ; 14(3)2024 Feb 21.
Article en En | MEDLINE | ID: mdl-38539582
ABSTRACT
The drivers' distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to "detect" eventual events, road traffic, and additional driving tasks that might contribute to the drivers' distraction. Therefore, the present study aimed at developing an EEG-based "Distraction index" obtained by the combination of the driver's mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver's attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers' distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers' distraction (all p < 0.01).
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Brain Sci Año: 2024 Tipo del documento: Article País de afiliación: Italia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Brain Sci Año: 2024 Tipo del documento: Article País de afiliación: Italia