Electroencephalography (EEG) signals recorded during simultaneous functional magnetic resonance imaging (fMRI) are contaminated by strong artifacts. Among these, the ballistocardiographic (BCG) artifact is the most challenging, due to its complex spatio-temporal dynamics associated with ongoing cardiac activity. The presence of BCG residuals in EEG data may hide true, or generate spurious correlations between EEG and fMRI time-courses. Here, we propose an adaptive Optimal Basis Set (aOBS) method for BCG artifact removal. Our method is adaptive, as it can estimate the delay between cardiac activity and BCG occurrence on a beat-to-beat basis. The effective creation of an optimal basis set by principal component analysis (PCA) is therefore ensured by a more accurate alignment of BCG occurrences. Furthermore, aOBS can automatically estimate which components produced by PCA are likely to be BCG artifact-related and therefore need to be removed. The aOBS performance was evaluated on high-density EEG data acquired with simultaneous fMRI in healthy subjects during visual stimulation. As aOBS enables effective reduction of BCG residuals while preserving brain signals, we suggest it may find wide application in simultaneous EEG-fMRI studies.

Birmingham University Imaging Centre School of Psychology University of Birmingham Edgbaston Birmingham United Kingdom

Department of Experimental Psychology University of Oxford Oxford United Kingdom

Functional Neuroimaging Laboratory IRCCS San Camillo Hospital Foundation Venice Lido Italy

Institute of Computer Science Academy of Sciences of the Czech Republic Prague Czech Republic

LET'S ISTC National Research Council Rome Italy

National Institute of Mental Health Klecany Czech Republic

Neural Control of Movement Laboratory ETH Zurich Zurich Switzerland

Research Center for Motor Control and Neuroplasticity KU Leuven Leuven Belgium

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