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Literature DB >> 11335147

EMG signal decomposition: how can it be accomplished and used?

Abstract

Electromyographic (EMG) signals are composed of the superposition of the activity of individual motor units. Techniques exist for the decomposition of an EMG signal into its constituent components. Following is a review and explanation of the techniques that have been used to decompose EMG signals. Before describing the decomposition techniques, the fundamental composition of EMG signals is explained and after, potential sources of information from and various uses of decomposed EMG signals are described.

Mesh：

Year: 2001 PMID： 11335147 DOI： 10.1016/s1050-6411(00)00050-x

Source DB: PubMed Journal: J Electromyogr Kinesiol ISSN： 1050-6411 Impact factor: 2.368

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Cited

36 in total

1. Motor unit identification in two neighboring recording positions of the human trapezius muscle during prolonged computer work.

Authors: Daniel Zennaro; Thomas Läubli; Helmut Krueger
Journal: Eur J Appl Physiol Date: 2003-04-24 Impact factor: 3.078

2. Voluntary drive-dependent changes in vastus lateralis motor unit firing rates during a sustained isometric contraction at 50% of maximum knee extension force.

Authors: C J de Ruiter; M J H Elzinga; P W L Verdijk; W van Mechelen; A de Haan
Journal: Pflugers Arch Date: 2003-11-22 Impact factor: 3.657

EMG signal decomposition: how can it be accomplished and used?

1. Motor unit identification in two neighboring recording positions of the human trapezius muscle during prolonged computer work.

2. Voluntary drive-dependent changes in vastus lateralis motor unit firing rates during a sustained isometric contraction at 50% of maximum knee extension force.

3. Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques.

4. Validating motor unit firing patterns extracted by EMG signal decomposition.

5. Adaptive spatio-temporal filtering of multichannel surface EMG signals.

6. Adaptive certainty-based classification for decomposition of EMG signals.

7. MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition.

8. Equalization filters for multiple-channel electromyogram arrays.

9. Surface EMG signal alterations in Carpal Tunnel syndrome: a pilot study.

10. Surface EMG decomposition based on K-means clustering and convolution kernel compensation.