A parallel framework for simultaneous EEG/fMRI analysis: Methodology and simulation

Concurrent EEG/fMRI recordings represent multiple, simultaneously active, regionally overlapping neuronal mass responses. To address the problems caused by the overlapping nature of these responses, we propose a parallel framework for Spatial-Temporal EEG/fMRI Fusion (STEFF). This technique adopts Independent Component Analysis (ICA) to recover the time-course and spatial mapping components from EEG and fMRI separately. These components are then linked concurrently in the spatial and temporal domain using an Empirical Bayesian (EB) model. This approach enables information one modality to be utilized as priors for the other and hence improves the spatial (for EEG) or temporal (for fMRI) resolution of the other modality. Consequently, STEFF achieves flexible and sparse matching among EEG and fMRI components with common neuronal substrates. Simulations under realistic noise conditions indicated that STEFF is a feasible and physiologically reasonable hybrid approach for spatiotemporal mapping of cognitive processing in the human brain.