A resampling approach to estimate the stability of one-dimensional or multidimensional independent components

Frank Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus Muller

Research output: Contribution to journalArticle

71 Citations (Scopus)


When applying unsupervised learning techniques in biomedical data analysis, a key question is whether the estimated parameters of the studied system are reliable. In other words, can we assess the quality of the result produced by our learning technique? We propose resampling methods to tackle this question and illustrate their usefulness for blind-source separation (BSS). We demonstrate that our proposed reliability estimation can be used to discover stable one-dimensional or multidimensional independent components, to choose the appropriate BSS-model, to enhance significantly the separation performance, and, most importantly, to flag components that carry physical meaning. Application to different biomedical testbed data sets (magnetoencephalography (MEG)/electrocardiography (ECG)-recordings) underline the usefulness of our approach.

Original languageEnglish
Pages (from-to)1514-1525
Number of pages12
JournalIEEE Transactions on Biomedical Engineering
Issue number12 I
Publication statusPublished - 2002 Dec 1
Externally publishedYes



  • Blind-source separation
  • Bootstrap
  • Electrocardiography (ECG)
  • Independent component analysis
  • Magnetoencephalography (MEG)
  • Multidimensional independent component analysis (ICA)
  • Reliability
  • Resampling
  • Stability
  • Unsupervised learning

ASJC Scopus subject areas

  • Biomedical Engineering

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