% ica() - Signal processing functions of the EEGLAB toolbox%% TOOLBOX CREDIT:%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%      EEGLAB -- MATLAB functions for psychophysiological data analysis     %%%%        including an enhanced and automated version of the infomax         %%%%             algorithm for Independent Component Analysis (ICA)            %%%%                        of Bell & Sejnowski (1995).                        %%%%     by Scott Makeig, Arnaud Delorme, Colin Humphries, Sigurd Enghoff,     %%%%       with Tzyy-Ping Jung, Tony Bell, Martin McKeown, Luca Finelli        %%%%          Te-Won Lee, Benjamin Blankertz, Alex Dimitrov, et al.            %%%%   Swartz Center for Computational Neuroscience, INC, UCSD, Version 4.0    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % GENERAL ELECTROPHYSIOLOGICAL DATA PROCESSING TOOLS:%% Find abs peak frames and amplitudes:                            abspeak()% Change reference from common to average:                        averef()% Simple block average data epochs:                               blockave()% Make a 2-D scalp field movie:                                   eegmovie()% Frequency band filter data:                                     eegfilt()% View continuous data traces:                                    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()% View a 2-D or 3-D scalp-field movie:                            seemovie()% Compute and plot statistics of 1-D data                         signalstat()% Power spectral scalp topographies                               spectopo()% Plot time/frequency (ERSP/ITC) scalp topographies               tftopo()% Event-related time/frequency (ERSP, ITC) of single-trial data:  timef()% Event-related coherence of single-trial data:                   crossf()% View data scalp topography(s):                      topoplot(), compmap()% 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() % Fastest, most compact: system-call of binary runica()           binica()% Fast, compact Matlab MEX-file implementation of runica()        mexica() % Perform ICA analysis using 2nd & 4th-order cumulants (Cardoso)  jader()% Test ICA algorithm accuracy, varying data parameters:           testica()% Compare ICA weight matrices:                       matcorr() -> matperm() % 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() % View a projected ICA component (time course plus topo map):     compplot()% Squash or expand data into a PCA-defined subspace: pcsquash() -> pcexpand()% Plot selected time periods of component activations:            tree()%% GENERAL HELPER FUNCTIONS:%% Make plot axes pop up into zoomable windows on mouse click      axcopy()% Create possibly-overlapping subplot axes on a general grid      sbplot()% Plot custom colorbar                                            cbar()%% POP-UP TOOLBOX TUTORIAL                                         tutorial()%% REFERENCES:          %    http://sccn.ucsd.edu/eeglab/tutorial/%    http://sccn.ucsd.edu/eeglab/icabib.html % Further information: %    http://sccn.ucsd.edu/eeglab/icafaq.html %% SEND news/bugs/fixes/suggestions to: scott@sccn.ucsd.eduhelp ica% $Log: ica.m,v $% Revision 1.5  2002/11/15 03:20:42  arno% remove imagetopo function reference%% Revision 1.4  2002/11/15 02:34:01  arno% debug for web%% Revision 1.3  2002/08/13 17:01:54  scott% revised for 4.0%% Revision 1.2  2002/04/12 02:59:27  scott% fixed typo -sm%% 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