Mining EEG-fMRI using independent component analysis

Tom Eichele, Vince D. Calhoun, Stefan Debener

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that constitute the data and alleviates the need for explicit spatial and temporal priors about the responses. In this paper, we introduce ICA for hemodynamic (fMRI) and electrophysiological (EEG) data processing, and one of the possible extensions to the population level that is available for both data types. We then selectively review some work employing ICA for the decomposition of EEG and fMRI data to facilitate the integration of the two modalities to provide an overview of what is available and for which purposes ICA has been used. An optimized method for symmetric EEG-fMRI decomposition is proposed and the outstanding challenges in multimodal integration are discussed.

Original languageEnglish (US)
Pages (from-to)53-61
Number of pages9
JournalInternational Journal of Psychophysiology
Issue number1
StatePublished - Jul 2009
Externally publishedYes


  • EEG
  • ERP
  • Group analysis
  • ICA
  • PCA
  • Single trial analysis
  • fMRI

ASJC Scopus subject areas

  • General Neuroscience
  • Neuropsychology and Physiological Psychology
  • Physiology (medical)


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