% ica() - summary of the signal processing functions of the EEGLAB toolbox
%
% TOOLBOX CREDIT:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%          MATLAB functions for psychophysiological data analysis           %%
%%                   including use of enhanced versions of the               %%
%%                 Independent Component Analysis (ICA) algorithm            %%
%%                         of Bell & Sejnowski (1995).                       %%
%%        By Scott Makeig, Colin Humphries, Sigurd Enghof & Tzyy-Ping Jung,  %%
%%        with contributions from Tony Bell, Delorme Arnaud, Martin McKeown, %%
%%        Alex Dimitrov, Te-Won Lee, J-F Cardoso, Benjamin Blankertz et al.  %%
%%                        email: scott@salk.edu                              %%
%%               CNL/Salk Institute, 2000, Version 3.61                      %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 
% GENERAL ELECTROPHYSIOLOGICAL DATA PROCESSING TOOLS:
%
% Make plot axes pop up into zoomable windows on mouse click      axcopy()
% Find abs peak frames and amplitudes:                            abspeak()
% Change reference from common to average:                        averef()
% Simple block average data epochs:                               blockave()
% Plot custom colorbar                                            cbar()
% Make a 2-D scalp field movie:                                   eegmovie()
% Frequency band filter data:                                     eegfilt()
% View continuous data traces:                         eegplotg(),eegplot()
% Average data epochs (with windowing options):                   erpave()
% Display raw or smoothed single data epochs:          erpimage(),erpimages()
% Re-align event-related epochs to given events:                  eventlock()
% Plot one or more field maps on 3-D head model(s):    headplot(),compheads()
% Construct a movie of a moving field on a 3-D head model:        headmovie()
% Compute and view log power spectra of single data epochs:       logspec()
% Select chans,frames,epochs of concatenated data epochs:         matsel()
% Perform moving averaging on data:                               movav()
% Plot a multichannel data epoch on a single axis:                ploterp()
% Perform principal component analysis (PCA) via SVD              pcasvd()
% Perform nonlinear (post-PCA) rotations:    varimax(), promax(), qrtimax()
% View concatenated multichannel data epochs:                     plotdata()
% View concatenated data epochs in topographic arrangement:       plottopo()
% Plot a data epoch with topoplots at selected time points:       timtopo()
% Change the data sampling rate:                                  resample()
% Remove baseline means from data epochs:                         rmbase()
% Regress out EEG data artifacts:                                 rmart()
% View a 2-D or 3-D scalp-field movie:                            seemovie()
% Time/frequency (ERSP, ITC) averages of single-trial data:       timef()
% Iter-channel coherence averages of single-trial data:           crossf()
% View data scalp topography(s):                       topoplot(),compmap()
% View images using scalp topography info:                        imagetopo()
% Convert Cartesian (x,y,z) channel locs to topoplot() format:    cart2topo()
% Convert 2-D topoplot() channel locs to 3-D headplot() format:   topo2sph()
% Convert 2-D headplot() channel locs to 2-D topoplot() format:   sph2topo()
% 
% SPECIFIC ICA TOOLS: 
%
% Perform ICA analysis using logistic infomax or extended-infomax runica() 
% Fast, compact Matlab MEX-file implementation of runica()        mexica() 
% Fastest, most compact: system-call of binary runica()           binica()
% Perform ICA analysis using 2nd & 4th-order cumulants (Cardoso)  jader()
% Test ICA algorithm accuracy, varying data parameters:           testica()
% Plot data and component envelopes:                    envproj() envtopo() 
% Compute component activations:                                  icaact()
% Compute component variances on scalp:                           icavar()
% Make activations all rms-positive:                              posact()
% Compute component projections:                                  icaproj() 
% Plot the data decomposition:                      plotproj() -> chanproj()
% Plot the data decomposition using plotopo():                    projtopo()
% Sort ICA components by max projected latency and variance:      compsort()
% Sort ICA components by mean projected variance only:            varsort() 
% Compare ICA weight matrices:                       matcorr() -> matperm() 
% Plot selected time periods of component activations:            tree()
% View a projected ICA component (time course plus topo map):     compplot()
% Squash or expand data into a PCA-defined subspace: pcsquash()-> expproj()
%                                                              -> pcexpand()
% TOOLBOX DEMO                                                    icademo()
% TOOLBOX TUTORIAL                                                tutorial()
%
% REFERENCES:          
%    http://www.sccn.ucsd.edu/eeglab/icabib.html 
% Further information: 
%    http://www.sccn.ucsd.edu/eeglab/icafaq.html 
% Please send news/bugs/fixes/suggestions to: 
%    scott@salk.edu

help ica

% $Log: ica.m,v $
% Revision 1.1  2002/04/05 17:36:45  jorn
% Initial revision
%

% Begun: May 7, 1996
% Current version: Mon Aug 21 13:35:49 PDT 2000
% 01-25-02 reformated help & license, added links -ad 

