Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

Independent component analysis (ICA) and blind source separation (BSS) methods are
increasingly used to separate individual brain and non-brain source signals mixed by …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Abstract Analysis of causal effects between continuous-valued variables typically uses either
autoregressive models or structural equation models with instantaneous effects. Estimation …

Investigating brain waves collected by an electroencephalogram (EEG) can be useful in
understanding human psychosocial conditions such as stress, emotional exhaustion …

We present new measures of the causal direction, or direction of effect, between two non-
Gaussian random variables. They are based on the likelihood ratio under the linear non …

Independent Component Analysis (ICA) plays an important role in biomedical engineering.
Indeed, the complexity of processes involved in biomedicine and the lack of reference …

Abstract The Gravity Recovery and Climate Experiment (GRACE) products provide valuable
information about total water storage variations over the whole globe. Since GRACE detects …

In independent component analysis it is assumed that the components of the observed
random vector are linear combinations of latent independent random variables, and the aim …

We propose an analytical approximation for the dependence of the effective charge on the
bare charge for spherical and cylindrical macro-ions as a function of the size of the colloid …