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 *

133 related articles for article (PubMed ID: 38718901)

  • 1. A hierarchical recursive feature elimination algorithm to develop brain computer interface application of user behavior for statistical reasoning and decision making.
    Ajrawi SA; Rao R; Sarkar M
    J Neurosci Methods; 2024 Aug; 408():110161. PubMed ID: 38718901
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Feature Selection Using Extreme Gradient Boosting Bayesian Optimization to upgrade the Classification Performance of Motor Imagery signals for BCI.
    Thenmozhi T; Helen R
    J Neurosci Methods; 2022 Jan; 366():109425. PubMed ID: 34838951
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach.
    Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG
    Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals.
    Salimpour S; Kalbkhani H; Seyyedi S; Solouk V
    Sci Rep; 2022 Jul; 12(1):11773. PubMed ID: 35817814
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals.
    Zarei R; He J; Siuly S; Zhang Y
    Comput Methods Programs Biomed; 2017 Jul; 146():47-57. PubMed ID: 28688489
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications.
    Chaudhary S; Taran S; Bajaj V; Siuly S
    Comput Methods Programs Biomed; 2020 Apr; 187():105325. PubMed ID: 31964514
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.
    Tibrewal N; Leeuwis N; Alimardani M
    PLoS One; 2022; 17(7):e0268880. PubMed ID: 35867703
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques.
    Yao L; Zhu B; Shoaran M
    J Neural Eng; 2022 Feb; 19(1):. PubMed ID: 35078156
    [No Abstract]   [Full Text] [Related]  

  • 9. Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods.
    Majidov I; Whangbo T
    Sensors (Basel); 2019 Apr; 19(7):. PubMed ID: 30978978
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A fresh look at functional link neural network for motor imagery-based brain-computer interface.
    Hettiarachchi IT; Babaei T; Nguyen T; Lim CP; Nahavandi S
    J Neurosci Methods; 2018 Jul; 305():28-35. PubMed ID: 29733940
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals.
    Malan NS; Sharma S
    Comput Biol Med; 2019 Apr; 107():118-126. PubMed ID: 30802693
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.
    Echtioui A; Zouch W; Ghorbel M; Mhiri C; Hamam H
    Clin EEG Neurosci; 2024 Jul; 55(4):455-464. PubMed ID: 36604821
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.
    Fumanal-Idocin J; Wang YK; Lin CT; Fernandez J; Sanz JA; Bustince H
    IEEE Trans Cybern; 2022 Aug; 52(8):7944-7955. PubMed ID: 34033571
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface.
    Siuly ; Li Y; Paul Wen P
    Comput Methods Programs Biomed; 2014 Mar; 113(3):767-80. PubMed ID: 24440135
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genetic-based feature selection for efficient motion imaging of a brain-computer interface framework.
    Chang H; Yang J
    J Neural Eng; 2018 Oct; 15(5):056020. PubMed ID: 30101753
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model.
    Hashem HA; Abdulazeem Y; Labib LM; Elhosseini MA; Shehata M
    Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991884
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An embedded implementation based on adaptive filter bank for brain-computer interface systems.
    Belwafi K; Romain O; Gannouni S; Ghaffari F; Djemal R; Ouni B
    J Neurosci Methods; 2018 Jul; 305():1-16. PubMed ID: 29738806
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Using Fractal and Local Binary Pattern Features for Classification of ECOG Motor Imagery Tasks Obtained from the Right Brain Hemisphere.
    Xu F; Zhou W; Zhen Y; Yuan Q; Wu Q
    Int J Neural Syst; 2016 Sep; 26(6):1650022. PubMed ID: 27255798
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.
    Hill NJ; Gupta D; Brunner P; Gunduz A; Adamo MA; Ritaccio A; Schalk G
    J Vis Exp; 2012 Jun; (64):. PubMed ID: 22782131
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Decision tree structure based classification of EEG signals recorded during two dimensional cursor movement imagery.
    Aydemir O; Kayikcioglu T
    J Neurosci Methods; 2014 May; 229():68-75. PubMed ID: 24751647
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.