Limitations and applications of ICA for surface electromyogram

Ganesh R. Naik, Dinesh K. Kumar, Sridhar P. Arjunan, M. Palaniswami, Rezaul Begg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This paper reports research conducted to evaluate the use of sparse ICA for the separation of muscle activity from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates Issues related to the properties of the signals and number of sources. The paper reports tests using Zibulevsky's method of temporal plotting to Identify number of Independent sources In SEMG recordings. The theoretical analysis and experimental results demonstrate that sparse ICA Is not suitable for SEMG signals. The results Identify that the technique Is unable to Identify finite number of active muscles. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, It Is not possible to get the data sparse enough to Identify number of Independent sources using Zibulevsky's sparse decomposition technique

Original languageEnglish
Title of host publication2006 International Conference of the IEEE Engineering in Medicine and Biology Society
Place of PublicationNew York, NY
PublisherIEEE
Pages5739-5742
Number of pages4
ISBN (Print)1424400325, 9781424400324
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States
Duration: 30 Aug 20063 Sep 2006

Conference

Conference28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
CountryUnited States
CityNew York, NY
Period30/08/063/09/06

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