论文标题
BCGGAN：使用生成的对抗网络同时进行脑电图中的ballistarcardiogram伪像去除伪影
BCGGAN: Ballistocardiogram artifact removal in simultaneous EEG-fMRI using generative adversarial network
论文作者
论文摘要
由于其具有高时空和空间分辨率的优势，同时脑电图功能磁共振成像（EEG-FMRI）的采集和分析的技术吸引了很多关注，并已在各种研究领域中广泛使用。然而，在大脑fMRI期间，ballistarcardiogram（BCG）伪像会严重污染脑电图。作为一个未配对的问题，BCG人工删除现在仍然是一个巨大的挑战。为了提供解决方案，本文提出了一种新型的模块化生成对抗网络（GAN）和相应的培训策略，以通过优化每个模块的参数来改善网络性能。通过这种方式，我们希望提高网络模型的局部表示能力，从而提高其整体性能并获得可靠的BCG伪像去除的发电机。此外，提出的方法不依赖其他参考信号或复杂的硬件设备。实验结果表明，与多种方法相比，本文介绍的技术可以在保留必需的脑电图信息的同时更有效地去除BCG人工制品。
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.
