Blind source separation techniques for decomposing event-related brain signals

Klaus Robert Müller, Ricardo Vigário, Frank Meinecke, Andreas Ziehe

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data.

Original languageEnglish
Pages (from-to)773-791
Number of pages19
JournalInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
Issue number2
Publication statusPublished - 2004 Feb


  • Blind Source Separation (BSS)
  • Bootstrap
  • Evoked responses
  • High order statistics
  • Independent Component Analysis (ICA)
  • MEG
  • Reliability
  • Temporal decorrelation

ASJC Scopus subject areas

  • Modelling and Simulation
  • Engineering (miscellaneous)
  • General
  • Applied Mathematics


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