Independent component analysis for noisy data - MEG data analysis
Independent component analysis for noisy data - MEG data analysis
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DOI：
10.1016/s0893-6080(00)00071-x
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发表时间：
2000-12-01
期刊：
影响因子：
7.8
通讯作者：
Toyama, K
中科院分区：
文献类型：
--
作者：
Ikeda, S;Toyama, K
独立成分分析（伊卡）是分析多变量数据的一种新的、简单的、强有力的思想。成功的应用之一是神经生物学数据分析，如脑电图（EEG），磁共振成像（MRI）和脑磁图（MEG）。然而，许多问题仍然存在。在大多数情况下，神经生物学数据包含大量的传感器噪声，并且独立分量的数量是未知的。在这篇文章中，我们讨论了一种方法来分离噪声污染的数据，而不知道独立分量的数量。伊卡的一个著名的两阶段方法是通过主成分分析（PCA）对数据进行预处理，然后估计必要的旋转矩阵。由于主成分分析不能很好地处理噪声数据，我们实现了一个因子分析模型进行预处理。在新的预处理中，估计源的数量和传感器噪声的量。在预处理之后，使用伊卡方法估计旋转矩阵。通过对脑磁图数据的实验，证明了该方法的有效性。(C)2000爱思唯尔科技有限公司版权所有。
Independent component analysis (ICA) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such as electroencephalography (EEG), magnetic resonance imaging (MRI), and magnetoencephalography (MEG). However, many problems remain. In most cases, neurobiological data contain a lot of sensor noise, and the number of independent components is unknown. In this article, we discuss an approach to separate noise-contaminated data without knowing the number of independent components. A well-known two stage approach to ICA is to pre-process the data by principal component analysis (PCA), and then the necessary rotation matrix is estimated. Since PCA does not work well for noisy data, we implement a factor analysis model for pre-processing. In the new pre-processing, the number of sources and the amount of sensor noise are estimated. After the preprocessing, the rotation matrix is estimated using an ICA method. Through the experiments with MEG data, we show this approach is effective. (C) 2000 Elsevier Science Ltd. All rights reserved.