A robust independent component analysis algorithm for removingballistocardiogram artifacts from EEG and fMRI recordings

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings provide complementary advantages with regard to the temporal and spatial resolution of brain activity. This methodology still now suffers from several artifacts, such as the gradient, the ballistocardiogram (BCG) and electrooculogram (EOG). A number of procedures have been developed in recent years for removing BCG artifacts and the usefulness of Independent Component Analysis (ICA) in this purpose was largely discussed and demonstrated. The aim of this study is to propose a more efficient and robust independent component analysis algorithm (RobustICA) for removing BCG and EOG artifacts. The algorithm has been validated on EEG datasets acquired inside the static magnetic field of a 1,5 T RM scanner and its performances were compared with those of other already applied processing methods (Optimal Basis Set, FastICA).