(Case et al., 2017) examined simultaneously recorded EEG-fMRI and preprocessed EEG using either a microstate analysis or spontaneous power analysis to create normalized frequency-specific timecourses for convolution with the BOLD timeseries for use as regressors in a general linear model with fMRI data. A third approach that was recently used (Chang et al., 2013) utilizes a sliding window of simultaneous EEG-fMRI data to examine whether temporal variations in coupling are associated with the amplitude of alpha and theta oscillations. These three approaches all directly compare the timeseries of EEG to fMRI and try to account for differing temporal and spatial resolutions of the two modalities. Since we did not record the EEG and fMRI simultaneously, we did not compare timeseries or adjust for different spatial resolutions in our analysis, but instead used parallel ICA to look for features in derived maps (either seed-correlation or coherence) that covaried across subjects.
