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Estimating likelihood or entropy rate is one of the key issues in many signal processing problems. Mutual information rate, which leads to the minimization of entropy rate, provides a natural cost for achieving blind source separation (BSS). In many complex-valued BSS applications, the latent sources are non-Gaussian, noncircular, and possess sample dependence. Consequently, an effective estimator...
Functional magnetic resonance imaging (fMRI) data are originally acquired as complex-valued images, which motivates the use of complex-valued data analysis methods. Due to the high dimension and high noise level of fMRI data, order selection and dimension reduction are important procedures for multivariate analysis methods such as independent component analysis (ICA). In this work, we develop a complex-valued...
We propose a new entropy rate estimator for a second and/or higher-order correlated source by modeling it as the output of a linear filter, which can be mixed-phase, driven by Gaussian or non-Gaussian noise. Based on this estimator, we develop a new spatiotemporal blind source separation (BSS) algorithm, full BSS (FBSS), by minimizing the entropy rate of separated sources. FBSS provides more flexibilities...
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