ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Many results are reported in the field of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. However, 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. In this article, 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 the experiments with MEG data, we show this approach is effective.