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Recognising drivers’ mental fatigue based on EEG multi-dimensional feature selection and fusion
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals
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Drivers’ misjudgement of vigilance state during prolonged monotonous daytime driving
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Electroencephalogram and electrocardiograph assessment of mental fatigue in a driving simulator
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Prediction of drowsiness events in night shift workers during morning driving
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Driving behavior recognition using EEG data from a simulated car-following experiment
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Asleep at the automated wheel—Sleepiness and fatigue during highly automated driving
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Effects of a seat-integrated mobilization system during passive driver fatigue
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Inter-subject transfer learning for EEG-based mental fatigue recognition
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EEG-based workers' stress recognition at construction sites
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Adaptation of Common Spatial Patterns based on mental fatigue for motor-imagery BCI
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Multiple nonlinear features fusion based driving fatigue detection
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Fatigue driving detection method based on EEG analysis in low-voltage and hypoxia plateau environment
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Feature-level fusion approaches based on multimodal EEG data for depression recognition
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Correlation between driving errors and vigilance level: influence of the driver's age
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Does preoccupation impair hazard perception? A simultaneous EEG and Eye Tracking study
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Exploring the effects of EEG signals on collision cases happening in the process of young drivers’ braking
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Epileptic seizure onset detection based on EEG and ECG data fusion
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A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
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Development of an algorithm for an EEG-based driver fatigue countermeasure
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Gene Selection for Cancer Classification using Support Vector Machines
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Active and passive fatigue in simulated driving: Discriminating styles of workload regulation and their safety impacts.
inferredcites →https://doi.org/10.1038/s41597-019-0027-4openalex-snapshot
Multi-channel EEG recordings during a sustained-attention driving task
inferredcites →https://doi.org/10.1063/5.0008434openalex-snapshot
Partial directed coherence based graph convolutional neural networks for driving fatigue detection
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Approximate entropy as a measure of system complexity.
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Measuring working memory load effects on electrophysiological markers of attention orienting during a simulated drive
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Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers
inferredcites →https://doi.org/10.1103/physrevlett.88.174102openalex-snapshot
Permutation Entropy: A Natural Complexity Measure for Time Series
inferredcites →https://doi.org/10.1109/iembs.2004.1403164openalex-snapshot
Extraction of features in EEG signals with the non-stationary signal analysis technology
inferredcites →https://doi.org/10.1109/iembs.2006.259231openalex-snapshot
Early Driver Fatigue Detection from Electroencephalography Signals using Artificial Neural Networks
inferredcites →https://doi.org/10.1109/jsen.2021.3077021openalex-snapshot
Estimating the Vigilance of High-Speed Rail Drivers Using a Stacking Ensemble Learning Method
inferredcites →https://doi.org/10.1109/rbme.2015.2427254openalex-snapshot
Body Sensor Networks: In the Era of Big Data and Beyond
inferredcites →https://doi.org/10.1109/tbcas.2014.2316224openalex-snapshot
Wireless and Wearable EEG System for Evaluating Driver Vigilance
inferredcites →https://doi.org/10.1109/tits.2005.848368openalex-snapshot
Detecting Stress During Real-World Driving Tasks Using Physiological Sensors
inferredcites →https://doi.org/10.1109/tr.2017.2778754openalex-snapshot
Detection of Driver Vigilance Level Using EEG Signals and Driving Contexts
inferredcites →https://doi.org/10.1152/ajpheart.2000.278.6.h2039openalex-snapshot
Physiological time-series analysis using approximate entropy and sample entropy
inferredcites →https://doi.org/10.1155/2008/519480openalex-snapshot
EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach
inferredcites →https://doi.org/10.1155/2017/8317357openalex-snapshot
Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain
inferredcites →https://doi.org/10.1155/2021/8632685openalex-snapshot
Effects of Level 3 Automated Vehicle Drivers’ Fatigue on Their Take-Over Behaviour: A Literature Review
inferredcites →https://doi.org/10.1177/0018720816634226openalex-snapshot
Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations
inferredcites →https://doi.org/10.1371/journal.pone.0198206openalex-snapshot
The impairing effects of mental fatigue on response inhibition: An ERP study
inferredcites →https://doi.org/10.3390/app7020150openalex-snapshot
Driver Fatigue Detection System Using Electroencephalography Signals Based on Combined Entropy Features
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Disturbance Analysis and Safety Monitoring of Driver's Emotion Swings in Human-Machine Co-Driving Environment: A Digital Twin Approach
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Multi‐parameter fusion driver fatigue detection method based on facial fatigue features
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A Review of EEG ‐Based Driver Fatigue Detection: Nonlinear Dynamics, Brain Networks, and Deep Learning Advances
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The Survival Analysis of Mental Fatigue Utilizing the Estimator of Kaplan-Meier and Nelson-Aalen
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A New Method for Detecting the Fatigue Using Automated Deep Learning Techniques for Medical Imaging Applications
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An improved CapsNet based on data augmentation for driver vigilance estimation with forehead single-channel EEG
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Adaptive multi-branch CNN of integrating manual features and functional network for driver fatigue detection
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MHCNN-STF: A fatigue detection method based on multimodal hybrid CNNs
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Subject-independent evaluation of mental fatigue detection models using limited-channel EEG in construction operations
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