ICA for noisy neurobiological data
ICA for noisy neurobiological data
复制标题
用于噪声神经生物学数据的 ICA
DOI：
10.1109/ijcnn.2000.860755
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Keisuke Toyama
中科院分区：
文献类型：
--
作者：
Shiro Ikeda;Keisuke Toyama
伊卡（独立成分分析）是一种分析多变量数据的技术。在脑电、磁共振成像、脑磁图等神经生物学数据分析领域，伊卡已取得了大量的研究成果。但问题依然存在。在大多数神经生物学数据中，存在大量的噪声，并且独立分量的数目是未知的，这给许多伊卡算法带来了困难。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。其思想是用因子分析代替PCA（主成分分析），它被用作许多伊卡算法的预处理。在新的预处理中，估计源的数目和噪声的量。在预处理之后，使用伊卡算法估计分离矩阵和混合系统。通过对脑磁图数据的实验，证明了该方法的有效性。
ICA (independent component analysis) is a technique for analyzing multi-variant data. Lots of results are reported in the field of neurobiological data analysis such as EEG (electroencephalography), MRI (magnetic resonance imaging), and MEG (magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there is a large amount of noise, and the number of independent components is unknown which gives difficulties for many ICA algorithms. We discuss an approach to separate noise-contaminated data without knowing the number of independent components. The idea is to replace PCA (principal component analysis), which is used as the preprocessing of many ICA algorithms, with factor analysis. In the new preprocessing, the number of the sources and the amount of the noise are estimated. After the preprocessing, an ICA algorithm is used to estimate the separation matrix and mixing system. Through experiments with MEG data, we show this approach is effective.