EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co-registration and MRI selection approach

Abstract

EEG/MEG source localization requires a subject's brain MRI to compute the sourcemodel and headmodel.�As part of this computation, co-registration of the digitized head information and brain MRI scan is the essential step. However, in the absence of a brain MRI scan, an approximated sourcemodel and headmodel can be computed from the subject's digitized head information and brain MRI scans from other subjects. In the present work, we compared the fiducial (FID)- and iterative closet point (ICP)-based co-registration approaches for computing an approximated sourcemodel using single and multiple available brain MRI scans. We also evaluated the two different template MRI selection strategies: one is based on objective registration error, and another on sourcemodel approximation error. The outcome suggests that averaged approximated solutions using multiple template brain MRI scans showed better performance than single-template MRI-based solutions. The FID-based approach performed better than the ICP-based approach for co-registration of the digitized head surface and brain MRI scan. While selecting template MRIs, the selection approach based on objective registration error showed better performance than a sourcemodel approximation error-based criterion. Cross-dataset performance analysis showed a higher model approximation error than within-dataset analysis. In conclusion, the FID-based co-registration approach and objective registration error-based MRI selection criteria provide a simple, fast and more accurate solution to compute averaged approximated models compared with the ICP-based approach. The demography of brain MRI scans should be similar to that of the query subject whose brain MRI scan was unavailable.