ArticleComputational and mathematical methods in medicine2017
Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.
- Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis.PloS one · 2020Pooled it
- 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
- A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.Sensors (Basel, Switzerland) · 2024Article
- Driving fatigue detection based on brain source activity and ARMA model.Medical & biological engineering & computing · 2024Article
- Article
- A Brain-Controlled Quadruped Robot: A Proof-of-Concept Demonstration.Sensors (Basel, Switzerland) · 2023Article
- A systematic review of physiological signals based driver drowsiness detection systems.Cognitive neurodynamics · 2023Review
- Understanding mental fatigue and its detection: a comparative analysis of assessments and tools.PeerJ · 2023Review
- Driving drowsiness detection using spectral signatures of EEG-based neurophysiology.Frontiers in physiology · 2023Article
- Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model.Entropy (Basel, Switzerland) · 2022Article
- Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.Scientific reports · 2022Article
- A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving.Computational and mathematical methods in medicine · 2022Article
- Convolutional Neural Network for Drowsiness Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Detecting Unfavorable Driving States in Electroencephalography Based on a PCA Sample Entropy Feature and Multiple Classification Algorithms.Entropy (Basel, Switzerland) · 2020Article
- Design of electroencephalogram authentication access control to smart car.Healthcare technology letters · 2020Article
- Using Electroencephalography (EEG) Power Responses to Investigate the Effects of Ambient Oxygen Content, Safety Shoe Type, and Lifting Frequency on the Worker's Activities.BioMed research international · 2020Article
- A hybrid model for EEG-based gender recognition.Cognitive neurodynamics · 2019Article
- Driving Fatigue Detection from EEG Using a Modified PCANet Method.Computational intelligence and neuroscience · 2019Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.
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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.