Sun Limin, Rieger Jochem, Hinrichs Hermann
Department of Neurology and Center for Advanced Imaging, University of Magdeburg/Germany, Leipziger Str. 44, D-39120 Magdeburg, Germany.
Neuroimage. 2009 May 15;46(1):144-53. doi: 10.1016/j.neuroimage.2009.01.059. Epub 2009 Feb 6.
Simultaneous electroencephalography (EEG) and magnetic resonance imaging (MRI) may allow imaging of the brain at high temporal and spatial resolution. However, EEGs recorded under these conditions are corrupted by large repetitive artifacts generated by the switching MR gradients and, second, by slightly less stable ballistocardiographic artifacts (BCG) resulting from heart beat related body movements. Here we present a new approach to remove BCG artifacts using a blind source separation (BSS) approach called maximum noise fraction (MNF). In contrast to other BSS methods MNF provides a set of components ordered by their signal-to noise-ratio. Applied to BCG contaminated EEG signals this means that components representing the artifact activity always result as the last or first ones (depending on the direction of ordering) thus making it easy to identify those components to be removed for artefact suppression. The new algorithm combines MNF and a subsequent template subtraction method to remove the BCG in a fully automatic manner. The efficiency of the new method was validated by comparing spontaneous EEG signals as well as event related potentials recorded from four subjects. According to these results MNF outperforms other BSS approaches in its capability to separate artifact activity from true EEG. In addition, MNF is superior to these alternatives regarding computational efficiency.
同步脑电图（EEG）和磁共振成像（MRI）可以在高时间和空间分辨率下对大脑进行成像。然而，在这些条件下记录的脑电图会受到由切换MR梯度产生的大量重复性伪影的干扰，其次，还会受到与心跳相关的身体运动产生的稍不稳定的心冲击图伪影（BCG）的干扰。在这里，我们提出了一种使用称为最大噪声分数（MNF）的盲源分离（BSS）方法来去除BCG伪影的新方法。与其他BSS方法不同，MNF提供了一组按信噪比排序的分量。应用于受BCG污染的EEG信号时，这意味着代表伪影活动的分量总是作为最后或第一个分量出现（取决于排序方向），从而便于识别那些要去除以抑制伪影的分量。新算法结合了MNF和随后的模板减法方法，以全自动方式去除BCG。通过比较从四名受试者记录的自发EEG信号以及事件相关电位，验证了新方法的效率。根据这些结果，MNF在将伪影活动与真实EEG分离的能力方面优于其他BSS方法。此外，在计算效率方面，MNF也优于这些替代方法。
