ArticlePloS one2017
Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.PloS one · 2020Pooled it
- A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection.Frontiers in neuroinformatics · 2020Pooled it
- Multimodal Phenotyping Dataset of Driving Fatigue.Scientific data · 2026Article
- MSANet: a multi-scale spatiotemporal adaptive network for EEG-based driver fatigue recognition.Frontiers in neuroscience · 2026Article
- Driver Fatigue Detection using EEG Microstate Features and Support Vector Machines.Journal of biomedical physics & engineering · 2025Article
- STFTransNet: A Transformer Based Spatial Temporal Fusion Network for Enhanced Multimodal Driver Inattention State Recognition System.Sensors (Basel, Switzerland) · 2025Article
- A Novel Neural Network Model Based on Real Mountain Road Data for Driver Fatigue Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Integrating DRN-RF with computer vision for detection of control room operator's mental fatigue.PloS one · 2025Article
- Evaluation of entropy features and classifier performance in person authentication using resting-state EEG.Frontiers in neuroscience · 2025Article
- Multiple entropy fusion predicts driver fatigue using forehead EEG.Frontiers in neuroscience · 2025Article
- Cognitive Workload Detection of Air Traffic Controllers Based on mRMR and Fewer EEG Channels.Brain sciences · 2024Article
- Rapid Mental Workload Detection of Air Traffic Controllers with Three EEG Sensors.Sensors (Basel, Switzerland) · 2024Article
- An Identification Method for Road Hypnosis Based on Human EEG Data.Sensors (Basel, Switzerland) · 2024Article
- Article
- Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study.Sleep advances : a journal of the Sleep Research Society · 2024Article
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- Drowsiness Detection Using Ocular Indices from EEG Signal.Sensors (Basel, Switzerland) · 2022Article
- A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving.Computational and mathematical methods in medicine · 2022Article
- EEG brain signals to detect the sleep health of a driver: An automated framework system based on deep learning.Frontiers in human neuroscience · 2022Article
- Evaluation of a Fatigue Detector Using Eye Closure-Associated Indicators Acquired from Truck Drivers in a Simulator Study.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Driver fatigue is an important contributor to road accidents, and fatigue detection has major implications for transportation safety. The aim of this research is to analyze the multiple entropy fusion method and evaluate several channel regions to effectively detect a driver's fatigue state based on electroencephalogram (EEG) records. First, we fused multiple entropies, i.e., spectral entropy, approximate entropy, sample entropy and fuzzy entropy, as features compared with autoregressive (AR) modeling by four classifiers. Second, we captured four significant channel regions according to weight-based electrodes via a simplified channel selection method. Finally, the evaluation model for detecting driver fatigue was established with four classifiers based on the EEG data from four channel regions. Twelve healthy subjects performed continuous simulated driving for 1-2 hours with EEG monitoring on a static simulator. The leave-one-out cross-validation approach obtained an accuracy of 98.3%, a sensitivity of 98.3% and a specificity of 98.2%. The experimental results verified the effectiveness of the proposed method, indicating that the multiple entropy fusion features are significant factors for inferring the fatigue state of a driver.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.