Single channel blind source separation based local mean decomposition for Biomedical applications

Yina Guo, Ganesh R. Naik, Hung Nguyen

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

34 Citations (Scopus)

Abstract

Single Channel Blind Source Separation (SCBSS) is an extreme case of underdetermined (more sources and fewer sensors) Blind Source Separation (BSS) problem. In this paper, we propose a novel technique using Local Mean Decomposition (LMD) and Independent Component Analysis (ICA) combined with single channel BSS (LMD-ICA). First, the LMD was used to decompose the single channel source into a series of data sequences, which are called as Product Functions (PF), then, ICA algorithm was used to process PFs to get similar independent components and extract the original signals. A comparison was made between LMD-ICA and previously proposed single channel ICA method (EEMD-ICA). The real time experimental results demonstrated the advantage of the proposed single channel source separation method for artifact removal and in biomedical source separation applications.

Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages6812-6815
Number of pages4
ISBN (Electronic)978-1-4577-0216-7
ISBN (Print)9781457702167
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
Duration: 3 Jul 20137 Jul 2013

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)1557-170X

Conference

Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
CountryJapan
CityOsaka
Period3/07/137/07/13

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