A unified framework for group independent component analysis for multi-subject fMRI data
β Scribed by Y. Guo; G. Pagnoni
- Book ID
- 119587343
- Publisher
- Elsevier Science
- Year
- 2009
- Tongue
- English
- Weight
- 124 KB
- Volume
- 47
- Category
- Article
- ISSN
- 1053-8119
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π SIMILAR VOLUMES
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
## 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