On utilizing uncertainty information in template-based EEG-fMRI ballistocardiogram artifact removal

The correction of ballistocardiogram artifacts in simultaneous EEG-fMRI often yields unsatisfactory results. To improve the signal-to-noise ratio (SNR) of results, we inferred EEG signal uncertainty from postcorrection artifact residuals and computed the uncertainty-weighted mean of ERPs. Using an uncertainty-weighted mean significantly and consistently reduced both inter- and intrasubject SEM in the analysis of auditory evoked responses (AER, indicated by the N1-P2 complex) and in the effects of an auditory oddball paradigm (N1-P3 complex, standard-deviant difference). SNR increased by 3% on average for the AER amplitude (intrasubject) and 17% on average for the auditory oddball ERP (intersubject). This demonstrates that weighting by uncertainty complements existing artifact correction algorithms to increase SNR in ERPs. More specifically, it is an efficient method to utilize seemingly corrupt (difficult-to-correct) EEG data that might otherwise be discarded.