ArticleScientific reports2022
Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 64 citations in OpenAlex.
- Emotion-Aware Contextual Modelling for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Research on drowsiness detection in UAV operators based on the random decision forest method.Scientific reports · 2026Article
- Drowsiness Classification in Young Drivers Based on Facial Near-Infrared Images Using a Convolutional Neural Network: A Pilot Study.Sensors (Basel, Switzerland) · 2025Article
- Improved drowsiness detection in drivers through optimum pairing of EEG features using an optimal EEG channel comparable to a multichannel EEG system.Medical & biological engineering & computing · 2025Article
- Article
- Brain activity patterns reflecting security perceptions of female cyclists in virtual reality experiments.Scientific reports · 2025Article
- Beyond the time-on-task: an EEG-driven approach for effective physiological assessment of mental fatigue in simulated and real driving.Frontiers in bioengineering and biotechnology · 2025Article
- Mind the road: attention related neuromarkers during automated and manual simulated driving captured with a new mobile EEG sensor system.Frontiers in neuroergonomics · 2025Article
- Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.Behavioral sciences (Basel, Switzerland) · 2024Article
- Article
- Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2024Article
- Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.Sensors (Basel, Switzerland) · 2023Article
- A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development.Sensors (Basel, Switzerland) · 2023Article
- Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving.Frontiers in neurorobotics · 2023Article
- ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drowsiness is a leading cause of accidents on the road as it negatively affects the driver's ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.