EEG-fMRI Gradient Artifact Correction by Multiple Motion-Related Templates
EEG-fMRI Gradient Artifact Correction by Multiple Motion-Related Templates
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DOI：
10.1109/tbme.2016.2593726
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发表时间：
2016-07
影响因子：
4.6
通讯作者：
P. LeVan;Shuoyue Zhang;B. Knowles;M. Zaitsev;J. Hennig
中科院分区：
文献类型：
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作者：
P. LeVan;Shuoyue Zhang;B. Knowles;M. Zaitsev;J. Hennig
目的：在同时进行的脑电图（EEG）和功能磁共振成像（fMRI）中，脑电图上的伪影是由磁共振扫描仪中磁场梯度的切换引起的。这些伪影依赖于头部位置，因此，在受试者运动的情况下很难去除。在本研究中，梯度伪影通过从外部记录的运动信息中提取的多个模板来建模。方法：通过估计由缓慢变化的样条调制的伪影模板以及头部位置信息，对EEG-fMRI记录进行梯度伪影校正。采用平均伪影减法（AAS）和最优基集（OBS）两种常用方法对脑电信号质量进行比较。结果：使用从样条曲线或运动时间过程估计的多个模板进行伪影校正优于现有的AAS和OBS方法，通过梯度时期的均方根功率进行量化。改善主要见于脑电后通道，在AAS和OBS方法中可以看到大多数残余伪影。残差频谱功率与未进行fMRI扫描的脑电信号相当。结论：从头部位置信息估计多个模板可以很好地建模梯度伪影，从而有效地去除伪影。意义：该方法有助于对运动不可避免的非合作被试进行EEG- fmri，例如研究梯度伪影特别突出的高频脑电活动。
Objectives: In simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), artifacts on the EEG arise from the switching of magnetic field gradients in the MR scanner. These artifacts depend on head position, and are, therefore, difficult to remove in the presence of subject motion. In this study, gradient artifacts are modeled by multiple templates extracted from externally recorded motion information. Methods: Gradient artifact correction was performed in EEG-fMRI recordings by estimating artifactual templates modulated by slowly varying splines, as well as head position information. The EEG signal quality was then compared following two common methods: averaged artifact subtraction (AAS) and optimal basis sets (OBS). Results: Artifact correction using multiple templates estimated from splines or motion time courses outperformed the existing AAS and OBS approaches, as quantified by root-mean-square power across gradient epochs. Improvements were mostly seen in posterior EEG channels, where most of the residual artifacts are seen following the AAS and OBS methods. Residual spectral power was comparable to that of EEG signals recorded without fMRI scanning. Conclusion: Gradient artifacts can be well modeled by multiple templates estimated from head position information, resulting in an effective artifact removal. Significance: This method can facilitate EEG-fMRI of uncooperative subjects in whom motion is inevitable, for example, to investigate high-frequency EEG activity in which gradient artifacts are particularly prominent.