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                 Independent Component Analysis (ICA)
                 ------------------------------------

                            Version V0.04


                                README



INTRODUCTION

The ica program provides source separation for a number or mixed signals
where the number of recordings is equal to the number of the sources. A
good description of the ICA algorithm used can be found in [1]. The non-
linearity used to perform the separation is based on either the exp()
or tanh() function.

Input to the program can come from one of four different file
formats. These include 16 bit integers, 32 bit floating point numbers,
64 bit floating point numbers and the European Data Format (EDF). In
the case of the integer and the floating point numbers, these are
stored such that the all the values from a particular point in time
are stored together. The input data does not have to be zero meaned
before being used by this program. The original unmixing matrix can
also be set otherwise a unity matrix is used.

The output from the program is optionally the unmixed input stored in
a file as either 16 bit integers, 32 bit floating point numbers or 64
bit floating point numbers.  The resultant unmixing matrix can also be
saved to a file.

The progress of the program is displayed for each loop through the data.
After the input data has been processed and the unmixed version saved to a
file, the RMS value of the covariance matrix for the unmixed and the mixed
signals will be calculated. This is just to give a rough indication on how
well the data has been separated. Note that the input data does not get
sphered. The displaying of the RMS covariance is optional and is
reliant on the unmixed output being saved to a file.


INSTALLATION

The installation instructions are found in the file INSTALL. This
includes compilation from source code.


BUGS

There may be some. This is an early release (hence the small version
number). I have not even had time to put it into RCS yet. Please
let me know of any bugs or any suggestions that will make the
program for useful.

ACKNOWLEDGMENTS

This program was first written while visiting the Computational
Neurobiology Laboratory (CNL) at the Salk Institute for Biological
Studies in La Jolla, CA, USA. I would like to thank the head of
the laboratory, Terry Sejnowski, for allowing me to visit and to
Tony Bell for his input.

 
REFERENCES

  [1]  A.J. Bell & T.J. Sejnowski (1995). An information-maximization
       approach to blind separation and blind deconvolution,
       Neural Computation 7:1129-1159.


COPYRIGHT

This program has been written by:

     Peter Stepien

     Computer Engineering Laboratory (CEL)
     School of Electrical & Information Engineering
     The University of Sydney
     SYDNEY NSW 2006 Australia

     (Email: pstepien@sedal.usyd.edu.au)

All comments and suggestions are most welcome. Please do not
distribute modified versions of this software. Instead, notify the
author of any problems so that they can be included in future
releases. You may distribute unmodified versions of this software
for research and non-profit use.