MEG and EEG fusion in Bayesian frame

In biomedical brain imaging, several distinctive brain imaging modalities have been developed with each demonstrating particular strengths and weaknesses. Despite such recent developments in biomedical brain imaging, an essential question persists: How can multi-modalities be effectively integrated so that they complement each other without compromising their inherently beneficial qualities? Toward such an end, Bayesian frame represents a reasonable solution for even the most complicated problems since corresponding fusion is particularly straightforward. Accordingly, a Bayesian integrative strategy for MEG and EEG brain imaging modalities is proposed in this work. The corresponding effects of synergy as well as overall feasibility are examined through numerical simulations. In addition, spatiotemporal noise covariance incorporated into the fusion frame is discussed.