These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

134 related articles for article (PubMed ID: 26736312)

  • 1. Classification of driver fatigue in an electroencephalography-based countermeasure system with source separation module.
    Rifai Chai ; Naik GR; Tran Y; Sai Ho Ling ; Craig A; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug; 2015():514-7. PubMed ID: 26736312
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System.
    Chai R; Naik GR; Nguyen TN; Ling SH; Tran Y; Craig A; Nguyen HT
    IEEE J Biomed Health Inform; 2017 May; 21(3):715-724. PubMed ID: 26915141
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Enhancing accuracy of mental fatigue classification using advanced computational intelligence in an electroencephalography system.
    Chai R; Tran Y; Craig A; Ling SH; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():1338-41. PubMed ID: 25570210
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparing features extractors in EEG-based cognitive fatigue detection of demanding computer tasks.
    Rifai Chai ; Smith MR; Nguyen TN; Sai Ho Ling ; Coutts AJ; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2015; 2015():7594-7. PubMed ID: 26738050
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Classification of EEG based-mental fatigue using principal component analysis and Bayesian neural network.
    Rifai Chai ; Tran Y; Naik GR; Nguyen TN; Sai Ho Ling ; Craig A; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():4654-4657. PubMed ID: 28269312
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification.
    Rifai Chai ; Naik GR; Sai Ho Ling ; Tran Y; Craig A; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():1808-1811. PubMed ID: 29060240
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Early driver fatigue detection from electroencephalography signals using artificial neural networks.
    King LM; Nguyen HT; Lal SK
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():2187-90. PubMed ID: 17945698
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm.
    Zheng Y; Ma Y; Cammon J; Zhang S; Zhang J; Zhang Y
    Comput Biol Med; 2022 Aug; 147():105718. PubMed ID: 35716435
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.
    Chai R; Ling SH; San PP; Naik GR; Nguyen TN; Tran Y; Craig A; Nguyen HT
    Front Neurosci; 2017; 11():103. PubMed ID: 28326009
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel.
    Hu J
    Comput Math Methods Med; 2017; 2017():5109530. PubMed ID: 28255330
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Partial directed coherence based graph convolutional neural networks for driving fatigue detection.
    Zhang W; Wang F; Wu S; Xu Z; Ping J; Jiang Y
    Rev Sci Instrum; 2020 Jul; 91(7):074713. PubMed ID: 32752838
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification.
    Li F; Fan Y; Zhang X; Wang C; Hu F; Jia W; Hui H
    J Med Syst; 2019 Dec; 44(2):39. PubMed ID: 31865469
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Mental Fatigue Degree Recognition Based on Relative Band Power and Fuzzy Entropy of EEG.
    Xu X; Tang J; Xu T; Lin M
    Int J Environ Res Public Health; 2023 Jan; 20(2):. PubMed ID: 36674202
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network.
    J P; Subathra MSP; Mohammed MA; Maashi MS; Garcia-Zapirain B; Sairamya NJ; George ST
    Sensors (Basel); 2020 Sep; 20(17):. PubMed ID: 32883006
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Brain-computer interface classifier for wheelchair commands using neural network with fuzzy particle swarm optimization.
    Chai R; Ling SH; Hunter GP; Tran Y; Nguyen HT
    IEEE J Biomed Health Inform; 2014 Sep; 18(5):1614-24. PubMed ID: 25192573
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.
    Zeng H; Yang C; Zhang H; Wu Z; Zhang J; Dai G; Babiloni F; Kong W
    Comput Intell Neurosci; 2019; 2019():3761203. PubMed ID: 31611912
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Combining ICA Clustering and Power Spectral Density for Feature Extraction of Mental Fatigue of Spinal Cord Injury Patients.
    Chai R; Tran Y; Ling SH; Craig A; Nguyen HT
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():530-533. PubMed ID: 31945954
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.
    Hu J
    Front Comput Neurosci; 2017; 11():72. PubMed ID: 28824409
    [No Abstract]   [Full Text] [Related]  

  • 19. Source-Space Brain Functional Connectivity Features in Electroencephalogram-Based Driver Fatigue Classification.
    Nguyen KH; Ebbatson M; Tran Y; Craig A; Nguyen H; Chai R
    Sensors (Basel); 2023 Feb; 23(5):. PubMed ID: 36904587
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Constructing Multi-scale Entropy Based on the Empirical Mode Decomposition(EMD) and its Application in Recognizing Driving Fatigue.
    Zou S; Qiu T; Huang P; Bai X; Liu C
    J Neurosci Methods; 2020 Jul; 341():108691. PubMed ID: 32464125
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.