In independent component analysis (ICA), principal component analysis (PCA) is generally used to reduce the raw data to a few principal components (PCs) through eigenvector decomposition (EVD) on the data covariance matrix. Although this works for spatial ICA (sICA) on moderately sized fMRI data, it
β¦ LIBER β¦
A unified framework for group independent component analysis for multi-subject fMRI data
β Scribed by Ying Guo; Giuseppe Pagnoni
- Book ID
- 118491246
- Publisher
- Elsevier Science
- Year
- 2008
- Tongue
- English
- Weight
- 772 KB
- Volume
- 42
- 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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