BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network
This addresses a critical challenge in brain science research by enabling cleaner EEG data from simultaneous EEG-fMRI without needing additional hardware, though it appears incremental as it builds on existing GAN approaches.
The paper tackled the problem of ballistocardiogram artifact removal in simultaneous EEG-fMRI by proposing a modular generative adversarial network, achieving more effective artifact removal while retaining essential EEG information compared to multiple methods.
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.