Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction
Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction
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DOI：
10.1016/j.neuroimage.2012.09.022
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发表时间：
2013-01-01
期刊：
影响因子：
5.7
通讯作者：
Ossenblok, Pauly P. W.
中科院分区：
文献类型：
--
作者：
de Munck, Jan C.;van Houdt, Petra J.;Ossenblok, Pauly P. W.
脑电和功能性磁共振成像（EEG/fMRI）联合记录是一种潜在的临床工具，用于规划癫痫患者的侵入性EEG。此外，EEG/fMRI数据的分析提供了对fMRI和EEG数据的精确生理意义的基本见解。常规应用脑电图/功能磁共振成像定位癫痫源是阻碍了大的伪影在脑电图中，扫描仪梯度和心跳效应的切换所造成的。心冲击图（BCG）伪影的残留形状类似于癫痫棘波，因此可能导致棘波的错误识别。在这项研究中，提出了新的想法和方法来去除梯度伪影并减少在时间上相互重叠的不同形状的BCG伪影。当EEG采样频率和EEG低通滤波对于MR梯度切换足够时，可以通过减去平均伪影模板来有效地去除梯度伪影（Goncalves等人，2007年）。当不是这种情况时，梯度伪影以取决于fMRI重复时间与EEG采集时间的最接近倍数之间的余数的时间间隔重复自身。这些重复是确定性的，但由于这些定时已知的有限精度而难以预测。因此，我们建议使用聚类算法结合选择性平均来估计梯度伪影重复。当使用2048 Hz的采样频率时，梯度伪影的聚类为在3 T扫描仪的扫描期间记录的数据产生更干净的EEG。它甚至给干净的EEG时，EEG采样只有256 Hz。目前的BCG伪影减少算法的基础上平均模板减法有固有的局限性，他们不能正确处理的伪影重叠的时间。为了消除这一约束，伪影重叠的精确定时被建模并表示在稀疏矩阵中。接下来，用最小二乘程序解开人工制品。这种方法的相关性说明通过确定BCG伪影的数据集，包括29名健康受试者记录在1.5 T扫描仪和15例癫痫患者记录在3 T扫描仪。伪影幅度、持续时间和心跳间隔之间的关系的分析表明，在22%（1.5T数据）至30%（3 T数据）的情况下，BCG伪影显示重叠。在1.5 T扫描仪上记录的EEG/fMRI数据的BCG伪影显示HBI和BCG振幅之间的小的负相关性。总之，所提出的方法提供了一个实质性的改善的EEG信号的质量没有过多的计算机功率或额外的硬件比标准EEG兼容设备。(C)2012 Elsevier Inc. All rights reserved.
Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time.Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Goncalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3 T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz.Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5 T scanner and 15 patients with epilepsy recorded in a 3 T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5 T data) to 30% (3 T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5 T scanner show a small negative correlation between HBI and BCG amplitude.In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment. (C) 2012 Elsevier Inc. All rights reserved.