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Due to its advantages of high temporal and spatial resolution, the technology
of simultaneous electroencephalogram-functional magnetic resonance imaging
(EEG-fMRI) acquisition and analysis has attracted much attention, and has been
widely used in various research fields of brain science. However, during the
fMRI of the brain, ballistocardiogram (BCG) artifacts can seriously contaminate
the EEG. As an unpaired problem, BCG artifact removal now remains a
considerable challenge. Aiming to provide a solution, this paper proposed a
novel modular generative adversarial network (GAN) and corresponding training
strategy to improve the network performance by optimizing the parameters of
each module. In this manner, we hope to improve the local representation
ability of the network model, thereby improving its overall performance and
obtaining a reliable generator for BCG artifact removal. Moreover, the proposed
method does not rely on additional reference signal or complex hardware
equipment. Experimental results show that, compared with multiple methods, the
technique presented in this paper can remove the BCG artifact more effectively
while retaining essential EEG information.
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