Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and
β¦ LIBER β¦
Wavelet domain independent component analysis of fMRI data
β Scribed by Shigeru Muraki; Kayako Matsuo; Toshiharu Nakai
- Book ID
- 119584520
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 113 KB
- Volume
- 11
- Category
- Article
- ISSN
- 1053-8119
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## Abstract Independent component analysis (ICA) has become a popular tool for functional magnetic resonance imaging (fMRI) data analysis. Conventional ICA algorithms including Infomax and FASTβICA algorithms employ the underlying assumption that data can be decomposed into statistically independen
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