Preface.

Introduction.

MATHEMATICAL PRELIMINARIES.

Random Vectors and Independence.

Gradients and Optimization Methods.

Estimation Theory.

Information Theory.

Principal Component Analysis and Whitening.

BASIC INDEPENDENT COMPONENT ANALYSIS.

What is Independent Component Analysis?

ICA by Maximization of Nongaussianity.

ICA by Maximum Likelihood Estimation.

ICA by Minimization of Mutual Information.

ICA by Tensorial Methods.

ICA by Nonlinear Decorrelation and Nonlinear PCA.

Practical Considerations.

Overview and Comparison of Basic ICA Methods.

EXTENSIONS AND RELATED METHODS.

Noisy ICA.

ICA with Overcomplete Bases.

Nonlinear ICA.

Methods using Time Structure.

Convolutive Mixtures and Blind Deconvolution.

Other Extensions.

APPLICATIONS OF ICA.

Feature Extraction by ICA.

Brain Imaging Applications.

Telecommunications.

Other Applications.

References.

Index.