ICALAB Toolboxes |
ICALAB for Signal Processing and ICALAB for Image Processing are two
independent demo packages for MATLAB that implement a number of efficient
algorithms for ICA (independent component analysis) employing HOS
(higher order statistics), BSS (blind source separation)
employing SOS (second order statistics) and LP (linear prediction),
and BSE (blind signal extraction) employing various SOS
and HOS methods.
After the data preprocessing, this package can also be used also for MICA (multidimensional independent component analysis) and NIBSS (non independent blind source separation).
The main features of both packages are an easy-to-use graphical user interface, and implementation of computationally powerful and efficient algorithms.
The important and unique features of our ICALAB toolboxes are preprocessing and postprocessing tools (see below).
Conceptual model of ICALAB Toolbox.
Actual optional PREPROCESSING tools include: Principal Component Analysis (PCA), prewhitening, filtering: High Pass Filtering (HPF), Low Pass Filtering (LPF), Subband filters (Butterworth, Chebyshev, Elliptic) with adjustable order of filters, frequency subbands and the number of subbands.
POSTPROCESSING tools actually includes: Deflation and Reconstruction
("cleaning") of original raw data by removing undesirable components, noise or
artifacts.
Moreover, the ICALAB Toolboxes have flexible and extendable structure with ;the
possibility to extend the toolbox by the users by adding their own algorithms.
The algorithms can perform not only ICA ;but also Second Order Statistics Blind
Source Separation (BSS) Sparse Component Analysis (SCA), Nonnegative Matrix
Factorization (NMF), Smooth Component Analysis (SmoCA), Factor Analysis (FA)
and any other possible matrix factorization of the form X=HS+N or
Y=WX where H=W+ is a mixing matrix or a matrix of basis
vectors.
The ICA/BSS algorithms are pure mathematical formulas, powerful, but rather mechanical procedures: There is not very much left for the user to do after the
machinery has been optimally implemented. The successful and efficient use of
the ICALAB strongly depends on a priori knowledge, common sense and
appropriate use of the preprocessing and postprocessing tools. In other words,
it is preprocessing of data and postprocessing of models where expertise is
truly needed (see the
book).
On the other hand, the assumed linear mixing models must be valid at least
approximately and original sources signals should have specified statistical
properties.
Remark: ICALAB is limited to load 160 sources or sensor signals (channels),
so you can analyze now large scale problems.
If you need more sources please let us know to 
.
The ICALAB Toolboxes have been developed and designed by:
Andrzej Cichocki, Shun-ichi Amari, Krzysztof Siwek, Toshihisa Tanaka
Sergio Cruces, Pando Georgiev, Zbigniew Leonowicz, Hovagim Bakardjian, Tomasz Rutkowski, Seungjin Choi, Adel Belouchrani, Allan Barros, Ruck Thawonmas, Tetsuya Hoya, Wakako Hashimoto, Yasushi Terazono and Tomomi Watanabe in cooperation with other members of the Laboratory for Advanced Brain Signal Processing.
The graphic design, data visualization, user interface, extensive testing and integration of most of the existing algorithms have been implemented in MATLAB� by Krzysztof Siwek, Andrzej Cichocki, Toshihisa Tanaka, Tomasz Rutkowski and Tomomi Watanabe.
The ICALAB packages run on MATLAB 5.3 or higher.
MATLAB� is a registered trademark of The MathWorks, Inc.
| ICALAB for Signal Processing | ICALAB for Image Processing |
| on-line help | on-line help |
| download page (ver. 2.2) | download page (ver. 2.0) |
| sample benchmarks | sample benchmarks |
| NEW! FAQ, Tips & Tricks | |
DISCLAIMER:
NEITHER THE AUTHORS NOR THEIR EMPLOYERS ACCEPT ANY RESPONSIBILITY OR LIABILITY FOR LOSS OR DAMAGE OCCASIONED TO ANY PERSON OR PROPERTY THROUGH USING SOFTWARE, MATERIALS, INSTRUCTIONS, METHODS OR IDEAS CONTAINED HEREIN, OR ACTING OR REFRAINING FROM ACTING AS A RESULT OF SUCH USE. THE AUTHORS EXPRESSLY DISCLAIM ALL IMPLIED WARRANTIES, INCLUDING MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE. THERE WILL BE NO DUTY ON THE AUTHORS TO CORRECT ANY ERRORS OR DEFECTS IN THE SOFTWARE.
THIS SOFTWARE AND THE DOCUMENTATIONS ARE THE PROPERTY OF THE AUTHORS AND SHOULD ONLY BE USED FOR SCIENTIFIC AND EDUCATIONAL PURPOSES.
ALL SOFTWARE IS PROVIDED FREE AND IT IS NOT SUPPORTED. THE AUTHORS ARE, HOWEVER, HAPPY TO RECEIVE COMMENTS, CRITICISM AND SUGGESTIONS ADDRESSED TO .
For information about the algorithms, see the above on-line helps and the book.
Please use the following citation formats:
