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International Conference on Independent Component Analysis and Signal Separation

ICA 2004: Independent Component Analysis and Blind Signal Separation pp 985–992Cite as

Decomposition of Synthetic Multi-channel Surface-Electromyogram Using Independent Component Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

Abstract. Independent Component Analysis (ICA) can be used as a signal pre-processing tool to decompose electrode-array surface-electromyogram (s-EMG) signals into their constitutive motor-unit action potentials [García et al., IEEE EMB Mag., vol. 23(5) (2004)]. In the present study, we have established the ef- fectiveness and the limitations of ICA for s-EMG decomposition using a set of synthetic signals. In addition, we have selected the best-suited algorithm to perform s-EMG decomposition by comparing the effectiveness of two of the most popular standard ICA algorithms.

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Author information

Authors and Affiliations

  1. Dep. of Bioinformatic Eng, Osaka Univ, Osaka, Japan

    Gonzalo A. García, Kazuya Maekawa & Kenzo Akazawa

Authors
  1. Gonzalo A. García
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  2. Kazuya Maekawa
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  3. Kenzo Akazawa
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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García, G.A., Maekawa, K., Akazawa, K. (2004). Decomposition of Synthetic Multi-channel Surface-Electromyogram Using Independent Component Analysis. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_124

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_124

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

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