Reference health
Identification and Classification of EEG-Based Mental Fatigue Using Random Forest
38/38 checkable references clean · checked 2026-07-22
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
16 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 38 checked references that resolve
resolves10.1016/j.jsr.2019.12.015
Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: The value of differentiation of sleepiness and mental fatigue
resolves10.1016/j.neubiorev.2012.10.003
Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness
resolves10.1109/THMS.2019.2938156
Using EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art
resolves10.3233/THC-181480
Effective connectivity of mental fatigue: Dynamic causal modeling of EEG data
resolves10.1016/j.biopsycho.2013.11.010
Frontal theta activity reflects distinct aspects of mental fatigue
resolves10.1016/j.eng.2018.11.025
Neural Mechanisms of Mental Fatigue Revisited: New Insights from the Brain Connectome
resolves10.1155/2021/3965385
An Adaptive EEG Feature Extraction Method Based on Stacked Denoising Autoencoder for Mental Fatigue Connectivity
resolves10.1016/j.neuroimage.2012.05.035
Co-modulatory spectral changes in independent brain processes are correlated with task performance
resolves10.1016/j.bspc.2018.05.024
A new method for automatically modelling brain functional networks
resolves10.1016/j.neuroimage.2011.07.091
The effects of day-to-day variability of physiological data on operator functional state classification
resolves10.1016/j.cmpb.2014.04.011
Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques
resolves10.1109/TAES.2019.2933960
Spectral Analysis of EEG During Microsleep Events Annotated via Driver Monitoring System to Characterize Drowsiness
resolves10.1017/S0048577201393095
Driver fatigue: Electroencephalography and psychological assessment
resolves10.1016/j.ergon.2004.09.006
Electroencephalographic study of drowsiness in simulated driving with sleep deprivation
resolves10.1109/TBME.2014.2331189
In-Flight Automatic Detection of Vigilance States Using a Single EEG Channel
resolves10.1016/j.eswa.2007.12.043
Using EEG spectral components to assess algorithms for detecting fatigue
resolves10.1016/j.bspc.2010.01.001
EEG-based estimation of mental fatigue by using KPCA–HMM and complexity parameters
resolves10.1016/j.trf.2010.06.006
EEG signal analysis for the assessment and quantification of driver’s fatigue
resolves10.1109/TITS.2013.2275192
Automated Detection of Driver Fatigue Based on Entropy and Complexity Measures
resolves10.1088/1741-2552/aaf3f6
Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
resolves10.1007/s11571-018-9485-1
Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model
resolves10.1016/j.clinph.2008.03.012
EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate
resolves10.1109/TIM.2018.2885608
Pilots’ Fatigue Status Recognition Using Deep Contractive Autoencoder Network
resolves10.1016/j.neunet.2009.07.020
Time Domain Parameters as a feature for EEG-based Brain–Computer Interfaces
resolves10.1016/j.cmpb.2011.11.005
Automated sleep stage identification system based on time–frequency analysis of a single EEG channel and random forest classifier
resolves10.1016/j.clinph.2010.10.044
EEG alpha spindle measures as indicators of driver fatigue under real traffic conditions
resolves10.1016/j.eswa.2018.08.031
An end-to-end deep learning approach to MI-EEG signal classification for BCIs
resolves10.1016/j.tics.2021.10.009
Noradrenergic modulation of rhythmic neural activity shapes selective attention
resolves10.1016/j.neuroimage.2011.04.040
Individual differences in cognition, affect, and performance: Behavioral, neuroimaging, and molecular genetic approaches
resolves10.1109/TBME.2017.2694856
Passive BCI in Operational Environments: Insights, Recent Advances, and Future Trends
resolves10.1109/TNSRE.2018.2790359
Toward Drowsiness Detection Using Non-hair-Bearing EEG-Based Brain-Computer Interfaces
resolves10.3390/brainsci9120348
Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals
The 16 references without a DOI — listed, not checked
no DOI — not checkedIdentification and classification of construction equipment operators' mental fatigue using wearable eye-tracking technology
no DOI — not checkedUsing EEG for Mental Fatigue Assessment: A Comprehensive Look Into the Current State of the Art
no DOI — not checkedJingbo TLCTW. Internal control, safety culture and aviation safety
no DOI — not checkedref13
no DOI — not checkedDetecting Fatigue Status of Pilots Based on Deep Learning Network Using EEG Signals
no DOI — not checkedExploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data
no DOI — not checkedref26
no DOI — not checkedref27
no DOI — not checkedInter-subject transfer learning for EEG-based mental fatigue recognition
no DOI — not checkedref35
no DOI — not checkedDo we Need Hundreds of Classifiers to Solve Real World Classification Problems
no DOI — not checkedAutomatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree
no DOI — not checkedAn Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
no DOI — not checkedref48
no DOI — not checkedAssessing the effects of caffeine and theanine on the maintenance of vigilance during a sustained attention task
no DOI — not checkedNovel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy
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