  Objective: The concurrent recording of electroencephalography (EEG) and
functional magnetic resonance imaging (fMRI) is a technique that has received
much attention due to its potential for combined high temporal and spatial
resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact
caused by cardiac induced movement contaminates the EEG during EEG-fMRI
recordings. Removal of BCG in software has generally made use of linear
decompositions of the corrupted EEG. This is not ideal as the BCG signal is
non-stationary and propagates in a manner which is non-linearly dependent on
the electrocardiogram (ECG). In this paper, we present a novel method for BCG
artifact suppression using recurrent neural networks (RNNs). Methods: EEG
signals were recovered by training RNNs on the nonlinear mappings between ECG
and the BCG corrupted EEG. We evaluated our model's performance against the
commonly used Optimal Basis Set (OBS) method at the level of individual
subjects, and investigated generalization across subjects. Results: We show
that our algorithm can generate larger average power reduction of the BCG at
critical frequencies, while simultaneously improving task relevant EEG based
classification. Conclusion: The presented deep learning architecture can be
used to reduce BCG related artifacts in EEG-fMRI recordings. Significance: We
present a deep learning approach that can be used to suppress the BCG artifact
in EEG-fMRI without the use of additional hardware. This method may have scope
to be combined with current hardware methods, operate in real-time and be used
for direct modeling of the BCG.
