Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
J Neural Eng. 2019 Feb;16(1):016011. doi: 10.1088/1741-2552/aaec42. Epub 2018 Oct 29.
The simultaneous application of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) opens up new ways to investigate the human brain. The EEG recordings of simultaneous EEG-fMRI, however, are overlaid to a great degree by fMRI related artifacts and an artifact reduction is mandatory before any EEG analysis. The most severe artifacts-the gradient artifact and the pulse artifact-are repetitive. Average artifact subtraction (AAS) technique exploits the repetitiveness and is presumably the most often used artifact reduction technique. In this method artifact templates are calculated by averaging over adjacent artifact epochs and subsequently the templates are subtracted to reduce the artifacts. Although the AAS technique is one of the best performing methods, artifact residuals are usually present in the resulting EEG after applying the AAS technique. This work aims at identifying sources of the artifact residuals.
Application of the AAS technique to artificial EEG that is contaminated with artificial fMRI related artifacts.
A new source of artifact residuals was identified. It was found that the AAS technique itself adds artifacts to the EEG during gradient artifact reduction, because the gradient artifact template is corrupted by pulse artifact remainders.
This work shows that using a standard number of 25 epochs to calculate the gradient artifact template-as suggested by the inventors of AAS-results in substantial artifact residuals and consequently to a low EEG quality. Furthermore, the work discusses how potential solutions to this problem have serious side effects such as loss of adaptivity of the AAS technique. Hence, this problem must be considered carefully already in the design of simultaneous EEG-fMRI experiments.
脑电图（EEG）和功能磁共振成像（fMRI）的同步应用为研究人类大脑开辟了新途径。然而，同步 EEG-fMRI 的 EEG 记录在很大程度上受到 fMRI 相关伪影的叠加，在进行任何 EEG 分析之前，必须进行伪影减少。最严重的伪影——梯度伪影和脉冲伪影——是重复的。平均伪影减法（AAS）技术利用了这种重复性，据推测是最常用的伪影减少技术。在这种方法中，通过对相邻伪影时段进行平均来计算伪影模板，然后通过减去模板来减少伪影。尽管 AAS 技术是表现最好的方法之一，但在应用 AAS 技术后，EEG 中通常仍存在伪影残差。本工作旨在确定伪影残差的来源。
将 AAS 技术应用于人工 EEG，该 EEG 受到人工 fMRI 相关伪影的污染。
确定了一种新的伪影残差源。发现 AAS 技术本身在进行梯度伪影减少时会向 EEG 添加伪影，因为梯度伪影模板被脉冲伪影残差污染。
这项工作表明，使用标准数量的 25 个时段来计算梯度伪影模板——正如 AAS 的发明者所建议的那样——会导致大量的伪影残差，从而导致 EEG 质量低下。此外，该工作讨论了如何解决这个问题的潜在解决方案会带来严重的副作用，例如 AAS 技术的适应性丧失。因此，在设计同步 EEG-fMRI 实验时，必须仔细考虑这个问题。
