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 *

165 related articles for article (PubMed ID: 37838152)

  • 1. Real driving environment EEG-based detection of driving fatigue using the wavelet scattering network.
    Wang F; Chen D; Yao W; Fu R
    J Neurosci Methods; 2023 Dec; 400():109983. PubMed ID: 37838152
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Classifying Driving Fatigue by Using EEG Signals.
    Zeng C; Mu Z; Wang Q
    Comput Intell Neurosci; 2022; 2022():1885677. PubMed ID: 35371255
    [TBL] [Abstract][Full Text] [Related]  

  • 3. End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network.
    Jia H; Xiao Z; Ji P
    Comput Biol Med; 2023 Jan; 152():106431. PubMed ID: 36543007
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A novel deep-learning model based on τ-shaped convolutional network (τNet) with long short-term memory (LSTM) for physiological fatigue detection from EEG and EOG signals.
    He L; Zhang L; Lin X; Qin Y
    Med Biol Eng Comput; 2024 Jun; 62(6):1781-1793. PubMed ID: 38374416
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Exploring the fatigue affecting electroencephalography based functional brain networks during real driving in young males.
    Chen J; Wang H; Wang Q; Hua C
    Neuropsychologia; 2019 Jun; 129():200-211. PubMed ID: 30995455
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Using long short term memory and convolutional neural networks for driver drowsiness detection.
    Quddus A; Shahidi Zandi A; Prest L; Comeau FJE
    Accid Anal Prev; 2021 Jun; 156():106107. PubMed ID: 33848710
    [TBL] [Abstract][Full Text] [Related]  

  • 7. EEG-Based Spatio-Temporal Convolutional Neural Network for Driver Fatigue Evaluation.
    Gao Z; Wang X; Yang Y; Mu C; Cai Q; Dang W; Zuo S
    IEEE Trans Neural Netw Learn Syst; 2019 Sep; 30(9):2755-2763. PubMed ID: 30640634
    [TBL] [Abstract][Full Text] [Related]  

  • 8. AGL-Net: An Efficient Neural Network for EEG-Based Driver Fatigue Detection.
    Fang W; Tang L; Pan J
    J Integr Neurosci; 2023 Oct; 22(6):146. PubMed ID: 38176922
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Research on low-power driving fatigue monitoring method based on spiking neural network.
    Gu T; Yao W; Wang F; Fu R
    Exp Brain Res; 2024 Oct; 242(10):2457-2471. PubMed ID: 39177685
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SFT-Net: A Network for Detecting Fatigue From EEG Signals by Combining 4D Feature Flow and Attention Mechanism.
    Gao D; Wang K; Wang M; Zhou J; Zhang Y
    IEEE J Biomed Health Inform; 2024 Aug; 28(8):4444-4455. PubMed ID: 37310832
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Application of Graph Neural Network in Driving Fatigue Detection Based on EEG Signals.
    Mu Z; Jin L; Yin J; Wang Q
    Comput Intell Neurosci; 2022; 2022():9775784. PubMed ID: 36052050
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals.
    Liu L; Ji Y; Gao Y; Ping Z; Kuang L; Li T; Xu W
    J Healthc Eng; 2021; 2021():7799793. PubMed ID: 34853672
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue.
    Guo M; Li S; Wang L; Chai M; Chen F; Wei Y
    Int J Environ Res Public Health; 2016 Nov; 13(12):. PubMed ID: 27886139
    [No Abstract]   [Full Text] [Related]  

  • 14. 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]  

  • 15. InstanceEasyTL: An Improved Transfer-Learning Method for EEG-Based Cross-Subject Fatigue Detection.
    Zeng H; Zhang J; Zakaria W; Babiloni F; Gianluca B; Li X; Kong W
    Sensors (Basel); 2020 Dec; 20(24):. PubMed ID: 33348823
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Real-time EEG-based detection of fatigue driving danger for accident prediction.
    Wang H; Zhang C; Shi T; Wang F; Ma S
    Int J Neural Syst; 2015 Mar; 25(2):1550002. PubMed ID: 25541095
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A recurrence network-based convolutional neural network for fatigue driving detection from EEG.
    Gao ZK; Li YL; Yang YX; Ma C
    Chaos; 2019 Nov; 29(11):113126. PubMed ID: 31779352
    [TBL] [Abstract][Full Text] [Related]  

  • 19. ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods.
    Li C; Fu Y; Ouyang R; Liu Y; Hou X
    Sensors (Basel); 2022 Aug; 22(17):. PubMed ID: 36080966
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals.
    Lee BG; Lee BL; Chung WY
    Sensors (Basel); 2014 Sep; 14(10):17915-36. PubMed ID: 25264954
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.