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 *

160 related articles for article (PubMed ID: 35528339)

  • 1. Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.
    Islam MJ; Ahmad S; Haque F; Ibne Reaz MB; Bhuiyan MAS; Minhad KN; Islam MR
    Comput Intell Neurosci; 2022; 2022():6414664. PubMed ID: 35528339
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A novel channel selection method for multiple motion classification using high-density electromyography.
    Geng Y; Zhang X; Zhang YT; Li G
    Biomed Eng Online; 2014 Jul; 13():102. PubMed ID: 25060509
    [TBL] [Abstract][Full Text] [Related]  

  • 3. EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.
    Liu J; Li X; Li G; Zhou P
    Med Eng Phys; 2014 Jul; 36(7):975-80. PubMed ID: 24844608
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol.
    Stango A; Negro F; Farina D
    IEEE Trans Neural Syst Rehabil Eng; 2015 Mar; 23(2):189-98. PubMed ID: 25389242
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition.
    Khushaba RN; Al-Timemy AH; Al-Ani A; Al-Jumaily A
    IEEE Trans Neural Syst Rehabil Eng; 2017 Oct; 25(10):1821-1831. PubMed ID: 28358690
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns.
    Pan L; Zhang D; Jiang N; Sheng X; Zhu X
    J Neuroeng Rehabil; 2015 Dec; 12():110. PubMed ID: 26631105
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Feature dimensionality reduction for myoelectric pattern recognition: a comparison study of feature selection and feature projection methods.
    Liu J
    Med Eng Phys; 2014 Dec; 36(12):1716-20. PubMed ID: 25292451
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Real-time intelligent pattern recognition algorithm for surface EMG signals.
    Khezri M; Jahed M
    Biomed Eng Online; 2007 Dec; 6():45. PubMed ID: 18053184
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals.
    Purushothaman G; Vikas R
    Australas Phys Eng Sci Med; 2018 Jun; 41(2):549-559. PubMed ID: 29744809
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.
    Khushaba RN; Takruri M; Miro JV; Kodagoda S
    Neural Netw; 2014 Jul; 55():42-58. PubMed ID: 24721224
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand.
    Chu JU; Moon I; Mun MS
    IEEE Trans Biomed Eng; 2006 Nov; 53(11):2232-9. PubMed ID: 17073328
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Whitening of the electromyogram for improved classification accuracy in prosthesis control.
    Liu L; Liu P; Clancy EA; Scheme E; Englehart KB
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():2627-30. PubMed ID: 23366464
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Correlation analysis of electromyogram signals for multiuser myoelectric interfaces.
    Khushaba RN
    IEEE Trans Neural Syst Rehabil Eng; 2014 Jul; 22(4):745-55. PubMed ID: 24760933
    [TBL] [Abstract][Full Text] [Related]  

  • 14. HD-EMG Electrode Count and Feature Selection Influence on Pattern-based Movement Classification Accuracy.
    Lara J; Paskaranandavadivel N; Cheng LK
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():4787-4790. PubMed ID: 33019061
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Classification complexity in myoelectric pattern recognition.
    Nilsson N; Håkansson B; Ortiz-Catalan M
    J Neuroeng Rehabil; 2017 Jul; 14(1):68. PubMed ID: 28693533
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Spatially Filtered Low-Density EMG and Time-Domain Descriptors Improves Hand Movement Recognition.
    Al Taee AA; Khushaba RN; Al-Jumaily A
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():2671-2674. PubMed ID: 31946445
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration.
    Young AJ; Hargrove LJ; Kuiken TA
    IEEE Trans Biomed Eng; 2012 Mar; 59(3):645-52. PubMed ID: 22147289
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Upper Arm Motion High-Density sEMG Recognition Optimization Based on Spatial and Time-Frequency Domain Features.
    Bai D; Chen S; Yang J
    J Healthc Eng; 2019; 2019():3958029. PubMed ID: 31080576
    [TBL] [Abstract][Full Text] [Related]  

  • 19. On the challenge of classifying 52 hand movements from surface electromyography.
    Kuzborskij I; Gijsberts A; Caputo B
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():4931-7. PubMed ID: 23367034
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Pattern recognition of surface electromyogram based on multi-scale principal component analysis].
    Tian XY; Lei M
    Zhongguo Yi Liao Qi Xie Za Zhi; 2009 Jul; 33(4):243-6. PubMed ID: 19938518
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.