MEG-EEG fusion by Kalman filtering within a source analysis framework

The fusion of data from multiple neuroimaging modalities may improve the temporal and spatial resolution of non-invasive brain imaging. In this paper, we present a novel method for the fusion of simultaneously recorded electroencephalograms (EEG) and magnetoencephalograms (MEG) within the framework of source analysis. This method represents an extension of a previously published spatio-temporal inverse solution method to the case of MEG or combined MEG-EEG signals. Moreover, we use a state-of-the-art realistic finite element (FE) head model especially calibrated for the MEG-EEG fusion problem. Using a real data set containing an epileptic spike, we validate the source analysis results of the spatio-temporal inverse solution using the results of the LORETA method and the findings from other structural and functional modalities. We show that the proposed fusion method, despite the low signal-to-noise ratio (SNR) of single spikes, points to the same brain area that was found by the other modalities. Furthermore, it correctly identifies the same source as the main generator for the MEG and EEG spikes.