BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network

TL;DR

This paper introduces BCGGAN, a novel generative adversarial network designed to effectively remove ballistocardiogram artifacts from EEG signals during simultaneous EEG-fMRI, without requiring extra hardware or reference signals.

Contribution

The paper presents a new modular GAN architecture and training strategy that enhances local representation and artifact removal performance in EEG-fMRI data.

Findings

Outperforms existing methods in BCG artifact removal

Retains essential EEG information effectively

Does not depend on additional hardware or reference signals

Abstract

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.