Krishnaswamy Pavitra, Bonmassar Giorgio, Purdon Patrick L, Brown Emery N
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:5426-9. doi: 10.1109/EMBC.2013.6610776.
Obtaining high quality electroencephalogram (EEG) data simultaneously with functional MRI (fMRI) recordings is increasingly relevant for the study of cognitive and clinical brain states - as EEG-fMRI offers uniquely high spatiotemporal resolution imaging of brain activity. However, the utility of this technique is limited by ballistocardiogram (BCG) artifacts induced in the EEG by cardiac pulsation and head movement inside the magnetic field. In this paper, we introduce a novel model-based harmonic regression technique to remove BCG artifacts from EEG recorded in the MR scanner. Our technique uses physically motivated parametric models of the BCG artifact and the true EEG signal, and incorporates maximum likelihood approaches to identify model parameters, estimate and subtract the BCG from corrupted EEG measurements. We show that this method effectively removes BCG artifacts from EEG recorded in the MR scanner, restores simulated oscillatory signatures and enables over 20-fold improvement in SNR in bands of interest. Further, unlike common BCG removal techniques that rely on cardiac or motion reference signals, our approach is reference-free and thus is useful when reference signals are corrupted or difficult to acquire.
在进行功能磁共振成像（fMRI）记录的同时获取高质量脑电图（EEG）数据，对于认知和临床脑状态的研究越来越重要，因为脑电图-功能磁共振成像（EEG-fMRI）能够提供独特的高时空分辨率脑活动成像。然而，该技术的实用性受到心冲击图（BCG）伪影的限制，这种伪影是由心脏搏动以及磁场内的头部运动在脑电图中诱发产生的。在本文中，我们引入了一种基于模型的新型谐波回归技术，用于去除在磁共振扫描仪中记录的脑电图中的BCG伪影。我们的技术使用了基于物理原理的BCG伪影和真实脑电信号的参数模型，并采用最大似然方法来识别模型参数，从受干扰的脑电测量中估计并减去BCG。我们表明，该方法能有效去除磁共振扫描仪记录的脑电图中的BCG伪影，恢复模拟的振荡特征，并使感兴趣频段的信噪比提高20倍以上。此外，与依赖心脏或运动参考信号的常见BCG去除技术不同，我们的方法无需参考信号，因此在参考信号受损或难以获取时很有用。
