Concepedia

Electromyography Source Separation

Electromyography Source Separation is a signal processing technique that decomposes multichannel electromyographic signals into constituent neural sources to isolate individual motor unit activity from mixed recordings.

40

Publications

2.1K

Citations

101

Authors

67

Institutions

Publications per year

2017–2026

10

Parents

Publications

40

Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation

Francesco Negro, Silvia Muceli, Anna Margherita Castronovo et al. · Journal of Neural Engineering · 2016 · 663 citations · Full text

, , +14

Characterization of Human Motor Units From Surface EMG Decomposition

Dario Farina, Aleš Holobar · Proceedings of the IEEE · 2016 · 232 citations

, , +14

Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders

Ganesh R. Naik, Hung T. Nguyen · IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2015 · 146 citations

, , +16

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

Sebastian Halder, Michael Bensch, Jürgen Mellinger et al. · Computational Intelligence and Neuroscience · 2007 · 118 citations · Full text

, , +17

Blind Separation of Linear Instantaneous Mixtures of Nonstationary Surface Myoelectric Signals

Dario Farina, Cédric Févotte, C. Doncarli et al. · IEEE Transactions on Biomedical Engineering · 2004 · 82 citations

, , +12

1–5 of 40

Authors

101

  1. DF
    Dario Farina

    Aalborg University, Denmark

    9 publications · 1.2K citations

    9

    h-index

  2. GR
    Ganesh R. Naik

    University of Technology Sydney, Australia

    9 publications · 357 citations

    9

    h-index

  3. DK
    Dinesh Kumar

    RMIT University, Australia

    7 publications · 194 citations

    7

    h-index

  4. AH
    Aleš Holobar

    University of Maribor, Slovenia

    3 publications · 921 citations

    3

    h-index

  5. MP
    Marimuthu Palaniswami

    The University of Melbourne, Australia

    3 publications · 112 citations

    3

    h-index

1–5 of 101

Institutions

67

  1. RMIT University

    Melbourne, Australia

    8 h-index

    17 publications

    447 citations

  2. University of Maribor

    Maribor, Slovenia

    5 h-index

    6 publications

    985 citations

  3. Imperial College London

    London, United Kingdom

    4 h-index

    14 publications

    437 citations

  4. University of Göttingen

    Göttingen, Germany

    3 h-index

    7 publications

    2.9K citations

  5. Bernstein Center for Computational Neuroscience Göttingen

    Göttingen, Germany

    3 h-index

    7 publications

    2.9K citations

  6. The University of Osaka

    Osaka, Japan

    2 h-index

    4 publications

    174 citations

  7. University of Science and Technology of China

    Hefei, China

    2 h-index

    4 publications

    82 citations

  8. Memorial Hermann Institute for Rehabilitation and Research Foundation

    Houston, United States

    2 h-index

    3 publications

    54 citations

1–8 of 67

Venues

  1. IEEE Transactions on Biomedical Engineering

    4 publications · 168 citations

    4

    h-index

  2. 3

    h-index

  3. Journal of Electromyography and Kinesiology

    2 publications · 39 citations

    2

    h-index

  4. Journal of Neural Engineering

    2 publications · 722 citations

    2

    h-index

  5. Frontiers in Computational Neuroscience

    2 publications · 51 citations

    2

    h-index

1–5