BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

159 related articles for article (PubMed ID: 35853067)

  • 1. 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]  

  • 2. Real-Time Hand Gesture Recognition by Decoding Motor Unit Discharges Across Multiple Motor Tasks From Surface Electromyography.
    Chen C; Yu Y; Sheng X; Meng J; Zhu X
    IEEE Trans Biomed Eng; 2023 Jul; 70(7):2058-2068. PubMed ID: 37018607
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.
    Chen C; Ma S; Sheng X; Farina D; Zhu X
    IEEE Trans Biomed Eng; 2020 Dec; 67(12):3501-3509. PubMed ID: 32324538
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Simultaneous and proportional control of wrist and hand movements by decoding motor unit discharges in real time.
    Chen C; Yu Y; Sheng X; Farina D; Zhu X
    J Neural Eng; 2021 Apr; 18(5):. PubMed ID: 33764315
    [No Abstract]   [Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. A convolutional neural network to identify motor units from high-density surface electromyography signals in real time.
    Wen Y; Avrillon S; Hernandez-Pavon JC; Kim SJ; Hug F; Pons JL
    J Neural Eng; 2021 Apr; 18(5):. PubMed ID: 33721852
    [No Abstract]   [Full Text] [Related]  

  • 10. Estimating motor unit discharge patterns from high-density surface electromyogram.
    Holobar A; Farina D; Gazzoni M; Merletti R; Zazula D
    Clin Neurophysiol; 2009 Mar; 120(3):551-62. PubMed ID: 19208498
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle.
    Marateb HR; McGill KC; Holobar A; Lateva ZC; Mansourian M; Merletti R
    J Neural Eng; 2011 Dec; 8(6):066002. PubMed ID: 21975280
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Non-Invasive Analysis of Motor Unit Activation During Simultaneous and Continuous Wrist Movements.
    Chen C; Yu Y; Sheng X; Zhu X
    IEEE J Biomed Health Inform; 2022 May; 26(5):2106-2115. PubMed ID: 34910644
    [TBL] [Abstract][Full Text] [Related]  

  • 14. On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms.
    Glaser V; Holobar A
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():430-433. PubMed ID: 29059902
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Spatial decomposition of ultrafast ultrasound images to identify motor unit activity - A comparative study with intramuscular and surface EMG.
    Rohlén R; Lubel E; Grandi Sgambato B; Antfolk C; Farina D
    J Electromyogr Kinesiol; 2023 Dec; 73():102825. PubMed ID: 37757604
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Highly Accurate Real-Time Decomposition of Single Channel Intramuscular EMG.
    Yu T; Akhmadeev K; Carpentier EL; Aoustin Y; Farina D
    IEEE Trans Biomed Eng; 2022 Feb; 69(2):746-757. PubMed ID: 34388089
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control.
    Chen C; Yu Y; Sheng X; Zhu X
    Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():4791-4794. PubMed ID: 33019062
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A Novel and Efficient Surface Electromyography Decomposition Algorithm Using Local Spatial Information.
    Xu Y; Yu Y; Xia M; Sheng X; Zhu X
    IEEE J Biomed Health Inform; 2023 Jan; 27(1):286-295. PubMed ID: 36166568
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 8.