Beltrachini Leandro, von Ellenrieder Nicolas, Muravchik Carlos H
Laboratorio de Electrónica Industrial, Control e Instrumentación, Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:1654-7. doi: 10.1109/IEMBS.2010.5626668.
We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low. We find sufficient conditions for the consistency of the shrinkage estimators and results concerning their numerical stability. We compare several shrinkage schemes and show how to improve the estimator by incorporating known structure of the covariance matrix.
我们提出了一种用于背景活动的脑电图空间协方差矩阵的收缩估计器。我们表明，当用于进行估计的可用数据数量较少时，这种估计器相对于最大似然估计器和样本协方差估计器具有一些优势。我们找到了收缩估计器一致性的充分条件以及关于其数值稳定性的结果。我们比较了几种收缩方案，并展示了如何通过纳入协方差矩阵的已知结构来改进估计器。
