Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering
Spatial projection as a preprocessing step for EEG source reconstruction using spatiotemporal Kalman filtering
复制标题
空间投影作为使用时空卡尔曼滤波进行脑电图源重建的预处理步骤
DOI：
10.1109/embc.2017.8037294
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
M. Siniatchkin
中科院分区：
文献类型：
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作者：
L. Hamid;A. Al Farawn;I. Merlet;N. Japaridze;U. Heute;U. Stephani;A. Galka;F. Wendling;M. Siniatchkin
无创脑电图（EEG）或脑磁图（MEG）通过源成像重建的脑源在高分辨率记录的情况下可能会被信息冗余所扭曲。诸如空间投影之类的降维方法可以用来缓解这个问题。在这篇原理证明论文中，我们应用空间投影来解决基于状态空间建模的时空卡尔曼滤波（STKF）重构源时的信息冗余问题。我们比较了将空间投影纳入STKF算法的两种方法，并根据其在源定位方面的性能（准确估计源位置，缺乏伪源，计算速度和状态空间模型参数估计所需的优化步骤较少）选择最佳方法。我们使用基于神经元种群模型的最先进的模拟EEG数据，其中源的数量和位置是已知的，以验证STKF的源重建结果。在STKF算法中引入空间投影，解决了信息冗余的问题，实现了正确的源定位，没有假源，减少了STKF分析的总体计算时间。结果使得STKF分析高密度EEG、MEG或同时进行的MEG-EEG数据更加可行。
The reconstruction of brain sources from non-invasive electroencephalography (EEG) or magnetoencephalography (MEG) via source imaging can be distorted by information redundancy in case of high-resolution recordings. Dimensionality reduction approaches such as spatial projection may be used to alleviate this problem. In this proof-of-principle paper we apply spatial projection to solve the problem of information redundancy in case of source reconstruction via spatiotemporal Kalman filtering (STKF), which is based on state-space modeling. We compare two approaches for incorporating spatial projection into the STKF algorithm and select the best approach based on its performance in source localization with respect to accurate estimation of source location, lack of spurious sources, computational speed and small number of required optimization steps in state-space model parameter estimation. We use state-of-the-art simulated EEG data based on neuronal population models, for which the number and location of sources is known, to validate the source reconstruction results of the STKF. The incorporation of spatial projection into the STKF algorithm solved the problem of information redundancy, resulting in correct source localization with no spurious sources, and decreased the overall computational time in STKF analysis. The results help make STKF analyses of high-density EEG, MEG or simultaneous MEG-EEG data more feasible.