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.

翻译：同步脑电图-功能磁共振成像（EEG-fMRI）采集与分析技术因其高时间分辨率与高空间分辨率的优势而备受关注，并已广泛应用于脑科学的多个研究领域。然而，在大脑fMRI过程中，心冲击图（BCG）伪影会严重污染脑电图。作为一个非配对问题，BCG伪影去除至今仍是一项重大挑战。针对这一问题，本文提出了一种新颖的模块化生成对抗网络（GAN）及相应的训练策略，通过优化各模块参数提升网络性能。我们期望借此增强网络模型的局部表征能力，进而提高其整体性能，并得到一个可靠的BCG伪影去除生成器。此外，所提方法无需依赖额外的参考信号或复杂硬件设备。实验结果表明，与多种方法相比，本文提出的技术能在更有效去除BCG伪影的同时保留关键的脑电图信息。