Independent Component Analysis in the Presence of Noise in fMRI
Cordes D, Nandy R
University of Washington
A noisy version of Independent Component Analysis (noisy ICA) is applied to simulated and real fMRI data. The noise covariance is explicitly modeled by a simple autoregressive (AR) model of order 1. The unmixing matrix of the data is determined using a variant of the FastICA algorithm based on Gaussian moments. The sources are estimated using the principle of maximum likelihood by modeling the source densities as asymmetric exponential functions. Complications of noisy ICA and degree of improvement in estimating fMRI sources are investigated.