ArticleScientific reports2017
Utilization of a combined EEG/NIRS system to predict driver drowsiness.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
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Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.PloS one · 2020Pooled it
- Comparative Prefrontal Multimodal Physiological Signatures Under Active- and Passive-Fatigue-Inducing Simulated Driving Paradigms.Brain sciences · 2026Article
- Research on drowsiness detection in UAV operators based on the random decision forest method.Scientific reports · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- 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
- Beta-band desynchronization in the human hippocampus during movement preparation in a delayed reach task.Experimental brain research · 2025Article
- The state of the art in assessing mental fatigue in the cockpit using head-worn sensing technology.Frontiers in neuroergonomics · 2025Review
- 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
- Multimodal fNIRS-EEG sensor fusion: Review of data-driven methods and perspective for naturalistic brain imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Sleep Fragmentation Modulates the Neurophysiological Correlates of Cognitive Fatigue.Clocks & sleep · 2024Article
- Wireless ear EEG to monitor drowsiness.Nature communications · 2024Article
- Individual Variability in Brain Connectivity Patterns and Driving-Fatigue Dynamics.Sensors (Basel, Switzerland) · 2024Article
- Association of Visual-Based Signals with Electroencephalography Patterns in Enhancing the Drowsiness Detection in Drivers with Obstructive Sleep Apnea.Sensors (Basel, Switzerland) · 2024Article
- A systematic review of physiological signals based driver drowsiness detection systems.Cognitive neurodynamics · 2023Review
- Article
- Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation.Scientific reports · 2023Article
- Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement.Scientific reports · 2023Article
- Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG.International journal of environmental research and public health · 2023Article
- Article
- Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving.Frontiers in neurorobotics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The large number of automobile accidents due to driver drowsiness is a critical concern of many countries. To solve this problem, numerous methods of countermeasure have been proposed. However, the results were unsatisfactory due to inadequate accuracy of drowsiness detection. In this study, we introduce a new approach, a combination of EEG and NIRS, to detect driver drowsiness. EEG, EOG, ECG and NIRS signals have been measured during a simulated driving task, in which subjects underwent both awake and drowsy states. The blinking rate, eye closure, heart rate, alpha and beta band power were used to identify subject's condition. Statistical tests were performed on EEG and NIRS signals to find the most informative parameters. Fisher's linear discriminant analysis method was employed to classify awake and drowsy states. Time series analysis was used to predict drowsiness. The oxy-hemoglobin concentration change and the beta band power in the frontal lobe were found to differ the most between the two states. In addition, these two parameters correspond well to an awake to drowsy state transition. A sharp increase of the oxy-hemoglobin concentration change, together with a dramatic decrease of the beta band power, happened several seconds before the first eye closure.
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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.