Joint independent component analysis for simultaneous EEG-fMRI: Principle and simulation

Matthias Moosmann, Tom Eichele, Helge Nordby, Kenneth Hugdahl, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Original languageEnglish (US)
Pages (from-to)212-221
Number of pages10
JournalInternational Journal of Psychophysiology
Issue number3
StatePublished - Mar 2008
Externally publishedYes


  • Data fusion
  • EEG-fMRI
  • ERP
  • ICA
  • Modelling
  • Simulation

ASJC Scopus subject areas

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


Dive into the research topics of 'Joint independent component analysis for simultaneous EEG-fMRI: Principle and simulation'. Together they form a unique fingerprint.

Cite this