Independent component analysis for noisy data -- MEG data analysis

10 years 11 months ago
Independent component analysis for noisy data -- MEG data analysis
ICA (independent component analysis) is a new, simple and powerful idea for analyzing multi-variant data. One of the successful applications is neurobiological data analysis such as EEG (electroencephalography), MRI (magnetic resonance imaging), and MEG (magnetoencephalography). But there remain a lot of problems. In most cases, neurobiological data contain a lot of sensory 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 PCA (principal component analysis), 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 the sources and the amount of the sensory noise are estimated. After the pre-processing, the rotation matrix is estimated using an ...
Shiro Ikeda, Keisuke Toyama
Added 19 Dec 2010
Updated 19 Dec 2010
Type Journal
Year 2000
Where NN
Authors Shiro Ikeda, Keisuke Toyama
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