SAFECAR: A Brain–Computer Interface and intelligent framework to detect drivers’ distractions

• Published in: Expert systems with applications Vol. 203; p. 117402
• Main Authors: Martínez Beltrán, Enrique Tomás, Quiles Pérez, Mario, López Bernal, Sergio, Martínez Pérez, Gregorio, Huertas Celdrán, Alberto
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2022
• Subjects: Brain–Computer Interfaces; Cognitive state; Distraction detection; Electroencephalographic signal; Framework; Machine Learning; Electroencephalographic signal; Cognitive state; Machine Learning; Brain–Computer Interfaces; Distraction detection; Framework
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117402

As recently reported by the World Health Organization (WHO), the high use of intelligent devices such as smartphones, multimedia systems, or billboards causes an increase in distraction and, consequently, fatal accidents while driving. The use of EEG-based Brain–Computer Interfaces (BCIs) has been proposed as a promising way to detect distractions. However, existing solutions are not well suited for driving scenarios. They do not consider complementary data sources, such as contextual data, nor guarantee realistic scenarios with real-time communications between components. This work proposes an automatic framework for detecting distractions using BCIs and a realistic driving simulator. The framework employs different supervised Machine Learning (ML)-based models on classifying the different types of distractions using Electroencephalography (EEG) and contextual driving data collected by car sensors, such as line crossings or objects detection. This framework has been evaluated using a driving scenario without distractions and a similar one where visual and cognitive distractions are generated for ten subjects. The proposed framework achieved 83.9% F1-score with a binary model and 73% with a multiclass model using EEG, improving 7% in binary classification and 8% in multi-class classification by incorporating contextual driving into the training dataset. Finally, the results were confirmed by a neurophysiological study, which revealed significantly higher voltage in selective attention and multitasking. •Intelligent framework based on BCI and EEG for detecting drivers’ distractions.•Realistic driving scenario with a steering wheel, pedals, and immersive simulator.•Promising performance of ML models when detecting different driving distractions.•Increment of the distractions detection accuracy when including contextual data.