Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity

Javier Escudero*, Roberto Hornero, Daniel Abasolo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Blind source separation (BSS) is widely used to analyse brain recordings like the magnetoencephalogram (MEG). However, few studies have compared different BSS decompositions of real brain data. Those comparisons were usually limited to specific applications. Therefore, we aimed at studying the consistency (i.e., similarity) of the decompositions estimated for real MEGs from 26 subjects using five widely used BSS algorithms (AMUSE, SOBI, JADE, extended-Infomax and FastICA) for five epoch lengths (10s, 20s, 40s, 60s and 90s). A statistical criterion based on Factor Analysis was applied to calculate the number of components into which each epoch would be decomposed. Then, the BSS techniques were applied. The results indicate that the pair of algorithms 'AMUSE-SOBI', followed by 'JADE-FastICA', provided the most similar separations. On the other hand, the most dissimilar outcomes were computed with 'AMUSE-JADE' and 'SOBI-JADE'. The BSS decompositions were more similar for longer epochs. Furthermore, additional analyses of synthetic signals supported the results of the real MEGs. Thus, when selecting BSS algorithms to explore brain signals, the techniques offering the most different decompositions, such as AMUSE and JADE, may be preferred to obtain complementary, or at least different, perspectives of the underlying components. (C) 2010 IPEM. Published by Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)1137-1144
Number of pages8
JournalMedical Engineering and Physics
Volume32
Issue number10
DOIs
Publication statusPublished - Dec 2010

Keywords

  • Algorithm comparison
  • Blind Source Separation (BSS)
  • Consistency
  • Independent Component Analysis (ICA)
  • Magnetoencephalogram (MEG)
  • INDEPENDENT COMPONENT ANALYSIS
  • ARTIFACT REMOVAL
  • EEG
  • SIGNALS
  • STATISTICS
  • NOISE

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