Improved cortical source localization of ICA-derived EEG components using a source scalp projection noise model
Zeynep Akalin Acar, Scott Makeig · 2020
Here, we introduce a novel approach to estimating noise covariance matrices for scalp projection maps of ICA-decomposed EEG sources, and show that they are useful for estimating cortical EEG source distributions. To determine spatial uncertainty characteristics of individual independent component (IC) maps returned by the AMICA decomposition [1], we used the RELICA (Bootstrap-ICA) toolbox [2] to generate 50 decompositions of bootstrap resampled versions of the same EEG data set. This allows identification of clusters of near-identical bootstrap ICs of independent component (IC) maps, each matching the scalp map of a localizable brain effective source in the full data set (reference) AMICA decomposition. This, in turn, makes it possible to estimate the spatial variability of the associated whole-data IC scalp map. For cortical source localization we used the Sparse Compact Smooth (SCS) algorithm of Cao applied to an electrical forward problem head model optimized with a SCALE estimate of individual skull conductivity. When component scalp map noise covariance matrix used in SCS was initialized to the RELICA-derived covariance map (rather than ignored), we observed an improvement in residual variance left unexplained by SCS source localization to a compact cortical patch (or pair of patches). In addition, the peak of the estimated source patch moved, by an average of 14.6 mm (range: 2-20 mm) and in some cases was localized to a different sulcus or gyrus.