Probabilistic Independent Component Analysis in FMRI

@inproceedings{Beckmannit2001ProbabilisticIC,
  title={Probabilistic Independent Component Analysis in FMRI},
  author={C. F. Beckmannit},
  year={2001}
}
  • C. F. Beckmannit
  • Published 2001
Independent Component Analysis (ICA) is classically performed using a square and noise-free mixing model, where the number of observations equals the number of source processes. In FMRI, both assumptions are known to be invalid and conflict with the model assumptions of the standard general linear model. We employ a probabilistic ICA (PICA) model for FMRI data that allows for more general non-square mixing in the presence of isotropic Gaussian noise and compare its performance against the… CONTINUE READING
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