Abstract

Simultaneous electroencephalography (EEG) and magnetic resonance imaging (MRI) may allow imaging of the brain at high temporal and spatial resolution. However, EEGs recorded under these conditions are corrupted by large repetitive artifacts generated by the switching MR gradients and, second, by slightly less stable ballistocardiographic artifacts (BCG) resulting from heart beat related body movements. Here we present a new approach to remove BCG artifacts using a blind source separation (BSS) approach called maximum noise fraction (MNF). In contrast to other BSS methods MNF provides a set of components ordered by their signal-to noise-ratio. Applied to BCG contaminated EEG signals this means that components representing the artifact activity always result as the last or first ones (depending on the direction of ordering) thus making it easy to identify those components to be removed for artefact suppression. The new algorithm combines MNF and a subsequent template subtraction method to remove the BCG in a fully automatic manner. The efficiency of the new method was validated by comparing spontaneous EEG signals as well as event related potentials recorded from four subjects. According to these results MNF outperforms other BSS approaches in its capability to separate artifact activity from true EEG. In addition, MNF is superior to these alternatives regarding computational efficiency.