ArticleBrain sciences2024
A Novel EEG-Based Assessment of Distraction in Simulated Driving under Different Road and Traffic Conditions.
Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Multi-method characterization of neurophysiological and biological stress responses in surgical teams during real surgical procedures.Frontiers in neuroergonomics · 2026Article
- A novel approach for the EEG-driven assessment of divided attention through mutual information theory: A case study at the wheel.PloS one · 2026Article
- Editorial: Passive brain-computer interfaces: moving from lab to real-world application.Frontiers in computational neuroscience · 2026Article
- Neurophysiological Markers of Cognitive Failures in Drivers: An Electroencephalography Study.Basic and clinical neuroscience · 2025Article
- Analysis of Neurophysiological Correlates of Mental Fatigue in Both Monotonous and Demanding Driving Conditions.Brain sciences · 2025Article
- A Multimodal Neurophysiological Approach to Evaluate Educational Contents in Terms of Cognitive Processes and Engagement.Bioengineering (Basel, Switzerland) · 2025Article
- Mutual information-based teamwork evaluation in real-world environments: an exploratory investigation with professional surgeons.Frontiers in network physiology · 2025Article
- The dual system model of distraction: explaining the cognitive mechanism of distraction.Frontiers in psychology · 2025Article
- Impact of visual distractors in virtual reality environments on sustained attention behavioral performance and EEG characteristics.Frontiers in human neuroscience · 2025Article
- Smart IoT-driven biosensors for EEG-based driving fatigue detection: A CNN-XGBoost model enhancing healthcare quality.BioImpacts : BI · 2025Article
- Cognitive Response of Underground Car Driver Observed by Brain EEG Signals.Sensors (Basel, Switzerland) · 2024Article
- Optimizing EEG Signal Integrity: A Comprehensive Guide to Ocular Artifact Correction.Bioengineering (Basel, Switzerland) · 2024Article
- Characterization of Cochlear Implant Artifact and Removal Based on Multi-Channel Wiener Filter in Unilateral Child Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Augmented Recognition of Distracted Driving State Based on Electrophysiological Analysis of Brain Network.Cyborg and bionic systems (Washington, D.C.) · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
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
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Registered trials
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