Probabilistic independent component analysis for functional magnetic resonance imaging.
We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian an...
| Главные авторы: | , |
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| Формат: | Journal article |
| Язык: | English |
| Опубликовано: |
2004
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