"This monograph provides a delightful tour, through the foothills
of linear algebra to the higher echelons of independent components analysis, in
a graceful and deceptively simple way. Its careful construction, introducing
concepts as they are needed, discloses the fundamentals of source separation in
a remarkably accessible and comprehensive fashion."

--Karl J. Friston, University College London

"This fantastic book provides a broad introduction to both the
theory and applications of independent component analysis. I recommend it to
any student interested in exploring this emerging field."

--Martin J. McKeown, Associate Professor of Medicine (Neurology),
University of British Columbia

"Independent component analysis is a recent and powerful addition
to the methods that scientists and engineers have available to explore large
data sets in high-dimensional spaces. This book is a clearly written
introduction to the foundations of ICA and the practical issues that arise in
applying it to a wide range of problems."

--Terrence J. Sejnowski, Howard Hughes Medical Institute, Salk Institute
for Biological Studies, and University of California, San Diego

**Book Description**

Independent component analysis (ICA) is becoming an increasingly
important tool for analyzing large data sets. In essence, ICA separates an
observed set of signal mixtures into a set of statistically independent
component signals, or source signals. In so doing, this powerful method can
extract the relatively small amount of useful information typically found in
large data sets. The applications for ICA range from speech processing, brain
imaging, and electrical brain signals to telecommunications and stock
predictions.

In *Independent Component Analysis*, Jim Stone presents the
essentials of ICA and related techniques (projection pursuit and complexity
pursuit) in a tutorial style, using intuitive examples described in simple
geometric terms. The treatment fills the need for a basic primer on ICA that
can be used by readers of varying levels of mathematical sophistication,
including engineers, cognitive scientists, and neuroscientists who need to know
the essentials of this evolving method.

An overview establishes the strategy implicit in ICA in terms of its
essentially physical underpinnings and describes how ICA is based on the key
observations that different physical processes generate outputs that are
statistically independent of each other. The book then describes what Stone
calls "the mathematical nuts and bolts" of how ICA works. Presenting
only essential mathematical proofs, Stone guides the reader through an
exploration of the fundamental characteristics of ICA.

Topics covered include the geometry of mixing and unmixing; methods for
blind source separation; and applications of ICA, including voice mixtures,
EEG, fMRI, and fetal heart monitoring. The appendixes provide a vector matrix
tutorial, plus basic demonstration computer code that allows the reader to see
how each mathematical method described in the text translates into working
Matlab computer code.