Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans

Gerd Wübbeler, Andreas Ziehe, Bruno Marcel Mackert, Klaus Robert Müller, Lutz Trahms, Gabriel Curio

Research output: Contribution to journalReview articlepeer-review

50 Citations (Scopus)


We apply a recently developed multivariate statistical data analysis technique - so called blind source separation (BSS) by independent component analysis - to process magnetoencephalogram recordings of near-dc fields. The extraction of near-dc fields from MEG recordings has great relevance for medical applications since slowly varying dc-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a dc-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.

Original languageEnglish
Pages (from-to)594-599
Number of pages6
JournalIEEE Transactions on Biomedical Engineering
Issue number5
Publication statusPublished - 2000

Bibliographical note

Funding Information:
Manuscript received July 22, 1999; revised January 6, 2000. The work of A. Ziehe was supported in part by the DFG under Contracts JA 379/52 and JA 379/71. The work of G. Wübbeler, B.-M. Mackert, and G. Curio was supported in part by the DFG under Grants Cu 36/1-1 and 1-2. Asterisk indicates corresponding author.


  • Biomagnetism
  • Biomedical data processing
  • Blind source separation
  • Independent component analysis
  • Magnetoencephalography (MEG)
  • dc- recordings

ASJC Scopus subject areas

  • Biomedical Engineering


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