STTICS: A template-based algorithm for the objective selection of epilepsy-related EEG ICA components

In EEG-correlated fMRI studies, a representative time-course must be selected from the EEG data, which can be used to derive a predictor of the BOLD signal recorded in each voxel using fMRI. Independent Component Analysis (ICA) is commonly used for this purpose, but the selection of meaningful components is mostly performed through visual inspection. A new methodology is presented here for the automatic selection of independent components (ICs) from the EEG, called Spatio-Temporal Templates for Independent Component Selection (STTICS). Dataset-specific temporal and spatial templates for the epileptic activity are first extracted. The correlation between these templates and the ICs' time-courses and corresponding topographies, respectively, is then computed and used to inform a clustering algorithm. The performance of STTICS was compared with the only other existing method in the literature for the same purpose (COMPASS), by simulations with artificial data and application to real data (19 datasets acquired from 6 epileptic patients). In general, STTICS outperformed COMPASS in both simulations and real data. The ability of our method to accurately and objectively select epilepsy-related ICs makes it an important contribution for simultaneous EEG-fMRI epilepsy studies.