Paper and software for Independent Component Analysis of Evoked Brain Responses

[scott@salk.edu]

  "Blind separation of auditory event-related brain responses 
                  into independent components" 

                     S. Makeig, T-P. Jung, 
             D. Ghahremani, A.J. Bell & T.J. Sejnowski
                        (In press, PNAS)

Advance copies of this paper are available for online review and/or
download (151K). Independent component analysis (ICA) is a method for
decomposing multichannel data into a sum of temporally independent
components. In the paper, we apply an enhanced version of the ICA
algorithm of Bell & Sejnowski (1995) to decomposition of brain responses 
to auditory targets in a vigilance experiment. We demonstrate the 
nature and stability of the decomposition and discuss its utility 
for analysis of event-related response potentials. 

The URL is:
           http://www.cnl.salk.edu/~scott/PNAS.html

================================================================

       Matlab Toolbox for Independent Component Analysis
                of Electrophysiological Data
                          by
                      Scott Makeig
    Tony Bell, Tzyy-Ping Jung, Colin Humphries, Te-Won Lee 
                   Terrence Sejnowski
           Computational Neurobiology Laboratory
               Salk Institute, La Jolla CA
                      
A toolbox of routines written under Matlab for Independent Component
Analysis (ICA) and display of electrophysiological (EEG or MEG) data is
available for download. This software implements the ICA algorithm of
Bell & Sejnowski (1995) for use with multichannel physiological data,
particularly event-related or spontaneous EEG (or MEG) data. The algorithm
separates data into a sum of components whose time courses are maximally 
independent of one another and whose spatial projections to the scalp 
are fixed throughout the analysis epoch. The decomposition routine 
(runica.m) also can implement an extended ICA algorithm (Lee, Girolami 
and Sejnowski) for separating mixtures of sub-Gaussian as well as sparse 
(super-Gaussian) components.  

Applications to ERP and EEG data including comparison of conditions and
elimination of artifacts have been addressed in a series of papers and
abstracts available through a related bibliography page.  Another page
answers Frequently Asked Questions about applying ICA to psychophysiological 
data.

Graphics routines include general-purpose functions for viewing either
averaged or spontaneous EEG data and for making and viewing animations of
shifting scalp distributions. Other routines are useful for sorting and
displaying the time courses, scalp topographies, and scalp projections
of ICA components. A demonstration routine (icademo.m) and directory page
(ica.m) are included. The software has been written under Matlab 4.2c.
A version for Matlab 5.0 will be released later.

To download the toolbox in Unix (compress) or PC (zip) formats (~155K):
  http://www.cnl.salk.edu/~scott/ica-download-form.html

For further on-line information:
  http://www.cnl.salk.edu/~scott/icafaq.html - frequently asked questions
  http://www.cnl.salk.edu/~scott/icabib.html - bibliography of applications

Email: scott at salk.edu  Scott Makeig

___________________________________________________________________
    Scott Makeig http://www.cnl.salk.edu/~scott (619) 553-8414
    Comp. Neurobiol. Lab., Salk Institute   |   scott at salk.edu 
    UCSD Department of Neurosciences     |    smakeig at ucsd.edu
    Naval Health Research Center      |   makeig at nhrc.navy.mil