ICA methods for MEG imaging

Activity of individual cortical sources cannot be uniquely imaged when MEG data is a sequence of complex spatial patterns of multiple cortical sources. Auxiliary constraints integrated into the imaging equations are required to remove the mathematical ambiguity. Therefore, it is important to adapt source separation techniques to MEG imaging. It is much easier to accurately image field patterns of isolated brain electric sources. We demonstrate how a combination of second and fourth order Independent Component Analysis (ICA) methods can be used to remove noise and isolate source activity for improved MEG imaging accuracy. A second order ICA technique was used to extract respiratory and eye movement artifact by exploiting cross-correlation differences over time between cortical sources and artifact. For brain electric source separation, a fourth order ICA technique that quantified probabilities of simultaneous source activity was used to separate brain electric sources characterized by bursts of oscillatory circuit activity.