Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model [EEG/MEG]
Estimated generalized least squares electromagnetic source analysis based on a parametric noise covariance model [EEG/MEG]
复制标题
基于参数噪声协方差模型的估计广义最小二乘电磁源分析 [EEG/MEG]
DOI：
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发表时间：
2001
影响因子：
4.6
通讯作者：
P. Molenaar
中科院分区：
文献类型：
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作者：
L. Waldorp;H. Huizenga;C. Dolan;P. Molenaar
估计广义最小二乘（EGLS）电磁源分析被用来降低噪声和相关数据。标准EGLS需要多次试验来准确估计噪声协方差，从而准确估计源参数。或者，噪声协方差可以参数化建模。仅需要估计描述噪声协方差的模型参数，因此需要较少的试验。这种方法被称为参数EGLS（PEGLS）。在本文中，PEGLS的开发和它的性能进行了测试，在模拟研究和伪实证研究。
Estimated generalized least squares (EGLS) electromagnetic source analysis is used to downweight noisy and correlated data. Standard EGLS requires many trials to accurately estimate the noise covariances and, thus, the source parameters. Alternatively, the noise covariances can be modeled parametrically. Only the parameters of the model describing the noise covariances need to be estimated and, therefore, less trials are required. This method is referred to as parametric EGLS (PEGLS). In this paper, PEGLS is developed and its performance is tested in a simulation study and in a pseudoempirical study.