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 *

107 related articles for article (PubMed ID: 38722313)

  • 1. Decomposition strategy for surface EMG with few channels: a simulation study.
    Wu W; Jiang L; Yang B
    J Neural Eng; 2024 May; 21(3):. PubMed ID: 38722313
    [No Abstract]   [Full Text] [Related]  

  • 2. A Novel Framework Based on FastICA for High Density Surface EMG Decomposition.
    Chen M; Zhou P
    IEEE Trans Neural Syst Rehabil Eng; 2016 Jan; 24(1):117-27. PubMed ID: 25775496
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Improved online decomposition of non-stationary electromyogram via signal enhancement using a neuron resonance model: a simulation study.
    Zheng Y; Xu G; Li Y; Qiang W
    J Neural Eng; 2022 Apr; 19(2):. PubMed ID: 35303735
    [No Abstract]   [Full Text] [Related]  

  • 4. A simulation study on the relation between the motor unit depth and action potential from multi-channel surface electromyography recordings.
    He J; Luo Z
    J Clin Neurosci; 2018 Aug; 54():146-151. PubMed ID: 29805080
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A new method for the extraction and classification of single motor unit action potentials from surface EMG signals.
    Gazzoni M; Farina D; Merletti R
    J Neurosci Methods; 2004 Jul; 136(2):165-77. PubMed ID: 15183268
    [TBL] [Abstract][Full Text] [Related]  

  • 6. EMG signal decomposition using motor unit potential train validity.
    Parsaei H; Stashuk DW
    IEEE Trans Neural Syst Rehabil Eng; 2013 Mar; 21(2):265-74. PubMed ID: 23033332
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Segment-Wise Decomposition of Surface Electromyography to Identify Discharges Across Motor Neuron Populations.
    Chen C; Ma S; Yu Y; Sheng X; Zhu X
    IEEE Trans Neural Syst Rehabil Eng; 2022; 30():2012-2021. PubMed ID: 35853067
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Progressive FastICA Peel-Off and Convolution Kernel Compensation Demonstrate High Agreement for High Density Surface EMG Decomposition.
    Chen M; Holobar A; Zhang X; Zhou P
    Neural Plast; 2016; 2016():3489540. PubMed ID: 27642525
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A fast gradient convolution kernel compensation method for surface electromyogram decomposition.
    Lin C; Cui Z; Chen C; Liu Y; Chen C; Jiang N
    J Electromyogr Kinesiol; 2024 Jun; 76():102869. PubMed ID: 38479095
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Statistics of inter-spike intervals as a routine measure of accuracy in automatic decomposition of surface electromyogram.
    Hu X; Suresh NL; Jeon B; Shin H; Rymer WZ
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():3541-4. PubMed ID: 25570755
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation.
    Negro F; Muceli S; Castronovo AM; Holobar A; Farina D
    J Neural Eng; 2016 Apr; 13(2):026027. PubMed ID: 26924829
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Amplitude cancellation reduces the size of motor unit potentials averaged from the surface EMG.
    Keenan KG; Farina D; Merletti R; Enoka RM
    J Appl Physiol (1985); 2006 Jun; 100(6):1928-37. PubMed ID: 16397060
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automatic Implementation of Progressive FastICA Peel-Off for High Density Surface EMG Decomposition.
    Chen M; Zhang X; Chen X; Zhou P
    IEEE Trans Neural Syst Rehabil Eng; 2018 Jan; 26(1):144-152. PubMed ID: 28981419
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Single channel surface electromyogram deconvolution to explore motor unit discharges.
    Mesin L
    Med Biol Eng Comput; 2019 Sep; 57(9):2045-2054. PubMed ID: 31350669
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Detecting the unique representation of motor-unit action potentials in the surface electromyogram.
    Farina D; Negro F; Gazzoni M; Enoka RM
    J Neurophysiol; 2008 Sep; 100(3):1223-33. PubMed ID: 18497352
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Adaptive Real-Time Decomposition of Electromyogram During Sustained Muscle Activation: A Simulation Study.
    Zheng Y; Hu X
    IEEE Trans Biomed Eng; 2022 Feb; 69(2):645-653. PubMed ID: 34357862
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A comparison of three quantitative motor unit analysis algorithms.
    McGill KC
    Suppl Clin Neurophysiol; 2009; 60():273-8. PubMed ID: 20715389
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Real-time motor unit identification from high-density surface EMG.
    Glaser V; Holobar A; Zazula D
    IEEE Trans Neural Syst Rehabil Eng; 2013 Nov; 21(6):949-58. PubMed ID: 23475379
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Using two-dimensional spatial information in decomposition of surface EMG signals.
    Kleine BU; van Dijk JP; Lapatki BG; Zwarts MJ; Stegeman DF
    J Electromyogr Kinesiol; 2007 Oct; 17(5):535-48. PubMed ID: 16904342
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Classification of Action Potentials With High Variability Using Convolutional Neural Network for Motor Unit Tracking.
    Li Y; Zheng Y; Xu G; Zhang S; Liang R; Ji R
    IEEE Trans Neural Syst Rehabil Eng; 2024; 32():905-914. PubMed ID: 38335077
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.