Feature

Improved method for fusion of EEG and fMRI data.

At NeuroImage we try and offer our clients the most efficient and reliable methods to capture and analyze brain imaging data.

It has been shown that when evaluating for the time sequence of brain events, either EEG or fMRI can be inadequate. The classical method for combining this data was surpassed by a method detailed by Daunizeau et al. in their study “Symmetrical event-related EEG/FMRI information fusion in a variational Bayesian framework”.

EEG is known for its high temporal resolution. fMRI is known for its high spatial resolution when compared to EEG. EEG will provide the electrical activity information and fMRI the hemodynamic information. It would make sense that fusing and combining the data available from each technology would give more robust information of the localization of the time sequence of events.

The Daunizeau et al. study proposed a data generative model and devoted VBEM learning scheme that provided an un-supervised, well-balanced approach for the fusion of EEG/fMRI information. It also showed that a) the localization of the time sequence of events vary between EEG and fMRI, b) there is a significant finding of areas that are active on EEG and not so on fMRI.

This fusion approach should give better results than LORETA for source localization using EEG.

For more information you can click here:

Symmetrical event-related EEG/FMRI information fusion in a variational Bayesian framework J. Daunizeau et al. / NeuroImage 36 (2007) 69–87

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