Multimodal integration of high-resolution EEG and functional magnetic resonance imaging data: A simulation study

F. Babiloni, C. Babiloni, F. Carducci, G. L. Romani, P. M. Rossini, L. M. Angelone, F. Cincotti

Research output: Contribution to journalArticlepeer-review


Previous simulation studies have stressed the importance of the use of fMRI priors in the estimation of cortical current density. However, no systematic variations of signal-to-noise ratio (SNR) and number of electrodes were explicitly taken into account in the estimation process. In this simulation study we considered the utility of including information as estimated from fMRI. This was done by using as the dependent variable both the correlation coefficient and the relative error between the imposed and the estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were the different values of SNR of the scalp-generated data, the different inverse operators used to estimated the cortical source activity, the strengths of the fMRI priors in the fMRI-based inverse operators, and the number of scalp electrodes used in the analysis. Analysis of variance results suggested that all the considered factors significantly afflict the correlation and the relative error between the estimated and the simulated cortical activity. For the ROIs analyzed with simulated fMRI hot spots, it was observed that the best estimation of cortical source currents was performed with the inverse operators that used fMRI information. When the ROIs analyzed do not present fMRI hot spots, both standard (i.e., minimum norm) and fMRI-based inverse operators returned statistically equivalent correlation and relative error values.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
Issue number1
Publication statusPublished - May 1 2003


  • EEG and fMRI integration
  • Linear inverse source estimate
  • Movement-related potentials

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology


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