% pop_runica() - Run an ICA decomposition of an EEG dataset using runica(), 
%                binica(), or another ICA or other linear decomposition. 
% Usage:
%   >> OUT_EEG = pop_runica( EEG ); % pops-up a data entry window
%   >> OUT_EEG = pop_runica( EEG, 'key', 'val' ); % no pop_up
%
% Graphic interface:
%   "ICA algorithm to use" - [edit box] The ICA algorithm to use for 
%                 ICA decomposition. Command line equivalent: 'icatype'
%   "Commandline options" - [edit box] Command line options to forward
%                 to the ICA algorithm. Command line equivalent: 'options' 
% Inputs:
%   EEG         - input EEG dataset or array of datasets
%
% Optional inputs:
%   'icatype'   - ['runica'|'binica'|'jader'|'fastica'] ICA algorithm 
%                 to use for the ICA decomposition. The nature of any 
%                 differences in the results of these algorithms have 
%                 not been well characterized. {default: binica(), if
%                 found, else runica()}
%   'dataset'   - [integer array] dataset index or indices.
%   'chanind'   - [integer array or cell array] subset of channel indices 
%                 for running the ICA decomposition. Alternatively, you may
%                 also enter channel types here in a cell array.
%   'concatenate' - ['on'|'off'] 'on' concatenate all input datasets 
%                 (assuming there are several). 'off' run ICA independently
%                 on each dataset. Default is 'on'.
%   'key','val' - ICA algorithm options (see ICA routine help messages).
% 
% Adding a new algorithm:
% Add the algorithm to the list of algorithms line 366 to 466, for example
%
%    case 'myalgo', [EEG.icaweights] = myalgo( tmpdata, g.options{:} );
%
% where "myalgo" is the name of your algorithm (and Matlab function). 
% tmpdata is the 2-D array containing the EEG data (channels x points) and
% g.options{} contains custom options for your algorithm (there is no
% predetermined format for these options). The output EEG.icaweights is the
% mixing matrix (or inverse of the unmixing matrix).
%
% Note:
% 1) Infomax (runica, binica) is the ICA algorithm we use most. It is based 
%    on Tony Bell's infomax algorithm as implemented for automated use by 
%    Scott Makeig et al. using the natural gradient of Amari et al. It can 
%    also extract sub-Gaussian sources using the (recommended) 'extended' option 
%    of Lee and Girolami. Function runica() is the all-Matlab version; function 
%    binica() calls the (1.5x faster) binary version (a separate download) 
%    translated into C from runica() by Sigurd Enghoff.
% 2) jader() calls the JADE algorithm of Jean-Francois Cardoso. This is 
%    included in the EEGLAB toolbox by his permission. See >> help jader
% 3) To run fastica(), download the fastICA toolbox from its website,
%    http://www.cis.hut.fi/projects/ica/fastica/, and make it available 
%    in your Matlab path. According to its authors, default parameters
%    are not optimal: Try args 'approach', 'sym' to estimate components 
%    in parallel.
%
% Outputs:
%   OUT_EEG = The input EEGLAB dataset with new fields icaweights, icasphere 
%             and icachansind (channel indices). 
%
% Author: Arnaud Delorme, CNL / Salk Institute, 2001
%
% See also: runica(), binica(), jader(), fastica()

% Copyright (C) 2001 Arnaud Delorme, Salk Institute, arno@salk.edu
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% (at your option) any later version.
%
% This program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program; if not, write to the Free Software
% Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

% 01-25-02 reformated help & license -ad 
% 03-07-02 add the eeglab options -ad
% 03-18-02 add other decomposition options -ad
% 03-19-02 text edition -sm

function [ALLEEG, com] = pop_runica( ALLEEG, varargin )

com = '';
if nargin < 1   
    help pop_runica;
    return;
end;

% find available algorithms
% -------------------------
allalgs   = { 'runica' 'binica' 'jader' 'jadeop' 'jade_td_p' 'MatlabshibbsR' 'fastica' ...
              'tica' 'erica' 'simbec' 'unica' 'amuse' 'fobi' 'evd' 'evd24' 'sons' 'sobi' 'ng_ol' ...
              'acsobiro' 'acrsobibpf' 'pearson_ica' 'egld_ica' 'eeA' 'tfbss' 'icaML' 'icaMS' }; % do not use egld_ica => too slow
selectalg = {};
linenb    = 1;
count     = 1;
for index = length(allalgs):-1:1
    if exist(allalgs{index}) ~= 2 & exist(allalgs{index}) ~= 6
        allalgs(index) = [];
    end;
end;

% special AMICA
% -------------
selectamica = 0;
defaultopts = [ '''extended'', 1' ] ;
if nargin > 1
    if isstr(varargin{1})
        if strcmpi(varargin{1}, 'selectamica')
            selectamica = 1;
            allalgs = { 'amica' allalgs{:} };
            defaultopts = sprintf('''outdir'', ''%s''', fullfile(pwd, 'amicaout'));
        elseif strcmpi(varargin{1}, 'selectamicaloc')
            selectamica = 1;
            allalgs = { 'amica' allalgs{:} };
            defaultopts = sprintf('''outdir'', ''%s'', ''qsub'', ''off''', fullfile(pwd, 'amicaout'));
        end;
    end;
end;

% popup window parameters
% -----------------------
fig = [];
if nargin < 2 | selectamica
    commandchans = [ 'tmpchans = get(gcbf, ''userdata'');' ...
                     'tmpchans = tmpchans{1};' ...
                     'set(findobj(gcbf, ''tag'', ''chantype''), ''string'', ' ...
                     '       int2str(pop_chansel( tmpchans )));' ...
                     'clear tmpchans;' ];
    commandtype = ['tmptype = get(gcbf, ''userdata'');' ...
                   'tmptype = tmptype{2};' ...
                   'if ~isempty(tmptype),' ...
                   '    [tmps,tmpv, tmpstr] = listdlg2(''PromptString'',''Select type(s)'', ''ListString'', tmptype);' ...
				   '    if tmpv' ...
				   '        set(findobj(''parent'', gcbf, ''tag'', ''chantype''), ''string'', tmpstr);' ...
				   '    end;' ...
                   'else,' ...
                   '    warndlg2(''No channel type'', ''No channel type'');' ...
                   'end;' ...
				   'clear tmps tmpv tmpstr tmptype tmpchans;' ];
    cb_ica = [ 'if get(gcbo, ''value'') < 3, ' ...
               '     set(findobj(gcbf, ''tag'', ''params''), ''string'', ''''''extended'''', 1'');' ...
               'else set(findobj(gcbf, ''tag'', ''params''), ''string'', '''');' ...
               'end;' ];
               
    promptstr    = { { 'style' 'text'       'string' 'ICA algorithm to use (click to select)' } ...
                     { 'style' 'listbox'    'string' strvcat(allalgs{:}) 'callback', cb_ica } ...
                     { 'style' 'text'       'string' 'Commandline options (See help messages)' } ...
                     { 'style' 'edit'       'string' defaultopts 'tag' 'params' } ...
                     { 'style' 'text'       'string' 'Channel type(s) or channel indices' } ...
                     { 'style' 'edit'       'string' '' 'tag' 'chantype' }  ...
                     { 'style' 'pushbutton' 'string' '... types' 'callback' commandtype } ...
                     { 'style' 'pushbutton' 'string' '... channels' 'callback' commandchans } };
    geometry = { [2 1.5] [2 1.5] [2 1 1 1] };
    if length(ALLEEG) > 1
        cb1 = 'set(findobj(''parent'', gcbf, ''tag'', ''concat2''), ''value'', 0);';
        cb2 = 'set(findobj(''parent'', gcbf, ''tag'', ''concat1''), ''value'', 0);';
        promptstr = { promptstr{:}, ...
                     { 'style' 'text'       'string' 'Concatenate all datasets (check=yes; uncheck=run ICA on each dataset)?' }, ...
                     { 'style' 'checkbox'   'string' '' 'value' 0 'tag' 'concat1' 'callback' cb1 }, ...
                     { 'style' 'text'       'string' 'Concatenate datasets for the same subject and session (check=yes)?' }, ...
                     { 'style' 'checkbox'   'string' '' 'value' 1 'tag' 'concat2' 'callback' cb2 } };
        geometry = { geometry{:} [ 2 0.2 ] [ 2 0.2 ]};
    end;                 
    % channel types
    % -------------
    if isfield(ALLEEG(1).chanlocs, 'type'), 
        tmpchanlocs = ALLEEG(1).chanlocs;
        alltypes = { tmpchanlocs.type };
        indempty = cellfun('isempty', alltypes);
        alltypes(indempty) = '';
        try, 
            alltypes = unique(alltypes);
        catch, 
            alltypes = '';
        end;
    else
        alltypes = '';
    end;
    
    % channel labels
    % --------------
    if ~isempty(ALLEEG(1).chanlocs)
        tmpchanlocs = ALLEEG(1).chanlocs;        
        alllabels = { tmpchanlocs.labels };
    else
        for index = 1:ALLEEG(1).nbchan
            alllabels{index} = int2str(index);
        end;
    end;
    
    % gui
    % ---
    result       = inputgui( 'geometry', geometry, 'uilist', promptstr, ...
                             'helpcom', 'pophelp(''pop_runica'')', ...
                             'title', 'Run ICA decomposition -- pop_runica()', 'userdata', { alllabels alltypes } );
    if length(result) == 0 return; end;        
    options = { 'icatype' allalgs{result{1}} 'dataset' [1:length(ALLEEG)] 'options' eval( [ '{' result{2} '}' ]) };
    if ~isempty(result{3})
        if ~isempty(str2num(result{3})), options = { options{:} 'chanind' str2num(result{3}) };
        else                             options = { options{:} 'chanind' parsetxt(result{3}) }; 
        end;
    end;
    if length(result) > 3
        options = { options{:} 'concatenate' fastif(result{4}, 'on', 'off') };
        options = { options{:} 'concatcond'  fastif(result{5}, 'on', 'off') };
    end;
else 
    if mod(length(varargin),2) == 1
        options = { 'icatype' varargin{1:end} };
    else
        options = varargin;
    end;
end;

% decode input arguments
% ----------------------
[ g addoptions ] = finputcheck( options, { 'icatype'        'string'  allalgs   'runica'; ...
                            'dataset'        'integer' []        [1:length(ALLEEG)];
                            'options'        'cell'    []        {};
                            'concatenate'    'string'  { 'on','off' }   'off';
                            'concatcond'     'string'  { 'on','off' }   'off';
                            'chanind'        { 'cell','integer' } { [] [] }        [];}, ...
                            'pop_runica', 'ignore');
if isstr(g), error(g); end;
if ~isempty(addoptions), g.options = { g.options{:} addoptions{:}}; end;

% select datasets, create new big dataset if necessary
% ----------------------------------------------------
if length(g.dataset) == 1
    EEG = ALLEEG(g.dataset);
    EEG = eeg_checkset(EEG, 'loaddata');
elseif length(ALLEEG) > 1 & ~strcmpi(g.concatenate, 'on') & ~strcmpi(g.concatcond, 'on')
    [ ALLEEG com ] = eeg_eval( 'pop_runica', ALLEEG, 'warning', 'off', 'params', ...
           { 'icatype' g.icatype 'options' g.options 'chanind' g.chanind } );
    return;
elseif length(ALLEEG) > 1 & strcmpi(g.concatcond, 'on')
    allsubjects = { ALLEEG.subject };
    allsessions = { ALLEEG.session };
    allgroups   = { ALLEEG.group };
    alltags     = zeros(1,length(allsubjects));
    if any(cellfun('isempty', allsubjects))
        disp('Aborting: Subject names missing from at least one dataset.');
        return;
    end;
    dats = {};
    for index = 1:length(allsubjects)
        if ~alltags(index)
            allinds = strmatch(allsubjects{index}, allsubjects, 'exact');
            rmind = [];
            % if we have different sessions they will not be concatenated
            for tmpi = setdiff(allinds,index)'
                if ~isequal(allsessions(index), allsessions(tmpi)), rmind = [rmind tmpi];
                %elseif ~isequal(allgroups(index), allgroups(tmpi)), rmind = [rmind tmpi]; 
                end;
            end;
            allinds = setdiff(allinds, rmind);
            fprintf('Found %d datasets for subject ''%s''\n', length(allinds), allsubjects{index});
            dats = { dats{:} allinds };
            alltags(allinds) = 1;
        end;
    end;
    fprintf('**************************\nNOW RUNNING ALL DECOMPOSITIONS\n****************************\n');
    for index = 1:length(dats)
        ALLEEG(dats{index}) = pop_runica(ALLEEG(dats{index}), 'icatype', g.icatype, ...
            'options', g.options, 'chanind', g.chanind, 'concatenate', 'on');
        for idat = 1:length(dats{index})
            ALLEEG(dats{index}(idat)).saved = 'off';
            pop_saveset(ALLEEG(dats{index}(idat)), 'savemode', 'resave');
            ALLEEG(dats{index}(idat)).saved = 'on';
        end;
    end;
    com = sprintf('%s = pop_runica(%s, %s);', inputname(1),inputname(1), ...
              vararg2str({ 'icatype' g.icatype 'concatcond' 'on' 'options' g.options }) );
    return;
else
    disp('Concatenating datasets...');
    EEG = ALLEEG(g.dataset(1));
    
    % compute total data size
    % -----------------------
    totalpnts = 0;
    for i = g.dataset
        totalpnts = totalpnts+ALLEEG(g.dataset(i)).pnts*ALLEEG(g.dataset(i)).trials;
    end;
    EEG.data = zeros(EEG.nbchan, totalpnts);
    
    % copy data
    % ---------
    cpnts = 1;
    for i = g.dataset
        tmplen = ALLEEG(g.dataset(i)).pnts*ALLEEG(g.dataset(i)).trials;
        TMP = eeg_checkset(ALLEEG(g.dataset(i)), 'loaddata');
        EEG.data(:,cpnts:cpnts+tmplen-1) = reshape(TMP.data, size(TMP.data,1), size(TMP.data,2)*size(TMP.data,3));
        cpnts = cpnts+tmplen;
    end;
    EEG.icaweights = [];
    EEG.trials = 1;
    EEG.pnts   = size(EEG.data,2);
end;    

% Store and then remove current EEG ICA weights and sphere
% ---------------------------------------------------
fprintf('\n');
if ~isempty(EEG.icaweights)
    fprintf('Saving current ICA decomposition in "EEG.etc.oldicaweights" (etc.).\n');
    if ~isfield(EEG,'etc'), EEG.etc = []; end;
    if ~isfield(EEG.etc,'oldicaweights')
        EEG.etc.oldicaweights = {};
        EEG.etc.oldicasphere = {};
        EEG.etc.oldicachansind = {};
    end;
    tmpoldicaweights  = EEG.etc.oldicaweights;
    tmpoldicasphere   = EEG.etc.oldicasphere;
    tmpoldicachansind = EEG.etc.oldicachansind;
    EEG.etc.oldicaweights = { EEG.icaweights    tmpoldicaweights{:} };
    EEG.etc.oldicasphere  = { EEG.icasphere     tmpoldicasphere{:}  };
    EEG.etc.oldicachansind  = { EEG.icachansind tmpoldicachansind{:}  };
    fprintf('               Decomposition saved as entry %d.\n',length(EEG.etc.oldicaweights));
end
EEG.icaweights = [];
EEG.icasphere  = [];
EEG.icawinv    = [];
EEG.icaact     = [];

% select sub_channels
% -------------------
if isempty(g.chanind)
    g.chanind = 1:EEG.nbchan;
end;
if iscell(g.chanind)
    g.chanind = eeg_chantype(EEG.chanlocs, g.chanind);
end;
EEG.icachansind = g.chanind;

% is pca already an option?
% -------------------------
pca_opt = 0;
for i = 1:length(g.options)
    if isstr(g.options{i})
        if strcmpi(g.options{i}, 'pca')
            pca_opt = 1;
        end;
    end;
end;

%------------------------------
% compute ICA on a definite set
% -----------------------------
tmpdata = reshape( EEG.data(g.chanind,:,:), length(g.chanind), EEG.pnts*EEG.trials);
tmprank = getrank(tmpdata(:,1:min(3000, size(tmpdata,2))));
tmpdata = tmpdata - repmat(mean(tmpdata,2), [1 size(tmpdata,2)]); % zero mean 
if ~strcmpi(lower(g.icatype), 'binica')
    try
        disp('Attempting to convert data matrix to double precision for more accurate ICA results.')
        tmpdata = double(tmpdata);
        tmpdata = tmpdata - repmat(mean(tmpdata,2), [1 size(tmpdata,2)]); % zero mean (more precise than single precision)
    catch
        disp('*************************************************************')
        disp('Not enough memory to convert data matrix to double precision.')
        disp('All computations will be done in single precision. Matlab 7.x')
        disp('under 64-bit Linux and others is imprecise in this mode.')
        disp('We advise use of "binica" instead of "runica."')
        disp('*************************************************************')
    end;
end;
switch lower(g.icatype)
    case 'runica' 
        try, if ismatlab, g.options = {  g.options{:}, 'interupt', 'on' }; end; catch, end; 
        if tmprank == size(tmpdata,1) | pca_opt
            [EEG.icaweights,EEG.icasphere] = runica( tmpdata, 'lrate', 0.001,  g.options{:} );
        else 
            if nargin < 2
                uilist = { { 'style' 'text' 'string' [ 'EEGLAB has detected that the rank of your data matrix' 10 ...
                                                       'is lower the number of input data channels. This might' 10 ...
                                                       'be because you are including a reference channel or' 10 ...
                                                       'because you are running a second ICA decomposition.' 10 ...
                                                       sprintf('The proposed dimension for ICA is %d (out of %d channels).', tmprank, size(tmpdata,1)) 10 ...
                                                       'Rank computation may be innacurate so you may edit this' 10 ...
                                                       'number below. If you do not understand, simply press OK.' ] } { } ...
                           { 'style' 'text' 'string' 'Proposed rank:' } ...
                           { 'style' 'edit' 'string' num2str(tmprank) } };
                res = inputgui('uilist', uilist, 'geometry', { [1] [1] [1 1] }, 'geomvert', [6 1 1]);
                if isempty(res), return; end;
                tmprank = str2num(res{1});
                g.options = [g.options  { 'pca' tmprank }];
            else
                g.options = [g.options  {'pca' tmprank }]; % automatic for STUDY (batch processing)
            end;
            disp(['Data rank (' int2str(tmprank) ') is smaller than the number of channels (' int2str(size(tmpdata,1)) ').']);
            [EEG.icaweights,EEG.icasphere] = runica( tmpdata, 'lrate', 0.001, g.options{:} );
        end;
     case 'binica'
        icadefs;
        fprintf(['Warning: If the binary ICA function does not work, check that you have added the\n' ...
                 'binary file location (in the EEGLAB directory) to your Unix /bin directory (.cshrc file)\n']);
        if exist(ICABINARY) ~= 2
            error('Pop_runica(): binary ICA executable not found. Edit icadefs.m file to specify the ICABINARY location');
        end;
        tmprank = getrank(tmpdata(:,1:min(3000, size(tmpdata,2))));
        if tmprank == size(tmpdata,1) | pca_opt
            [EEG.icaweights,EEG.icasphere] = binica( tmpdata, 'lrate', 0.001, g.options{:} );
        else 
            disp(['Data rank (' int2str(tmprank) ') is smaller than the number of channels (' int2str(size(tmpdata,1)) ').']);
            [EEG.icaweights,EEG.icasphere] = binica( tmpdata, 'lrate', 0.001, 'pca', tmprank, g.options{:} );
        end;
    case 'amica' 
        tmprank = getrank(tmpdata(:,1:min(3000, size(tmpdata,2))));
        fprintf('Now Running AMICA\n');
        if length(g.options) > 1
            if isstr(g.options{2})
                fprintf('See folder %s for outputs\n', g.options{2});
            end;
        end;
        fprintf('To import results, use menu item "Tools > Run AMICA > Load AMICA components\n');
        modres = runamica( tmpdata, [], size(tmpdata,1), size(tmpdata,2), g.options{:} );
        if ~isempty(modres)
            EEG.icaweights = modres.W;
            EEG.icasphere  = modres.S;
        else
            return;
        end;
     case 'pearson_ica' 
        if isempty(g.options)
            disp('Warning: EEGLAB default for pearson ICA is 1000 iterations and epsilon=0.0005');
            [tmp EEG.icaweights] = pearson_ica( tmpdata, 'maxNumIterations', 1000,'epsilon',0.0005);
        else    
            [tmp EEG.icaweights] = pearson_ica( tmpdata, g.options{:});
        end;
     case 'egld_ica', disp('Warning: This algorithm is very slow!!!');
                      [tmp EEG.icaweights] = egld_ica( tmpdata, g.options{:} );
     case 'tfbss' 
        if  isempty(g.options)
             [tmp EEG.icaweights] = tfbss( tmpdata, size(tmpdata,1), 8, 512 );
        else    
             [tmp EEG.icaweights] = tfbss( tmpdata, g.options{:} );
        end;
     case 'jader',         [EEG.icaweights] = jader( tmpdata, g.options{:} );
     case 'matlabshibbsr', [EEG.icaweights] = MatlabshibbsR( tmpdata, g.options{:} );
     case 'eea',           [EEG.icaweights] = eeA( tmpdata, g.options{:} );
     case 'icaml',         [tmp EEG.icawinv] = icaML( tmpdata, g.options{:} );
     case 'icams',         [tmp EEG.icawinv] = icaMS( tmpdata, g.options{:} );
     case 'fastica',       [ ICAcomp, EEG.icawinv, EEG.icaweights] = fastica( tmpdata, 'displayMode', 'off', g.options{:} );
     case { 'tica' 'erica' 'simbec' 'unica' 'amuse' 'fobi' 'evd' 'sons' ...
            'jadeop' 'jade_td_p' 'evd24' 'sobi' 'ng_ol' 'acsobiro' 'acrsobibpf' } 
        fig = figure('tag', 'alg_is_run', 'visible', 'off');
        
        if isempty(g.options), g.options = { size(tmpdata,1) }; end;
        switch lower(g.icatype)
         case 'tica',     EEG.icaweights = tica( tmpdata, g.options{:} );
         case 'erica',    EEG.icaweights = erica( tmpdata, g.options{:} );
         case 'simbec',   EEG.icaweights = simbec( tmpdata, g.options{:} );
         case 'unica',    EEG.icaweights = unica( tmpdata, g.options{:} );
         case 'amuse',    EEG.icaweights = amuse( tmpdata );
         case 'fobi',     [tmp EEG.icaweights] = fobi( tmpdata, g.options{:} );
         case 'evd',      EEG.icaweights = evd( tmpdata, g.options{:} );
         case 'sons',     EEG.icaweights = sons( tmpdata, g.options{:} );
         case 'jadeop',   EEG.icaweights = jadeop( tmpdata, g.options{:} );
         case 'jade_td_p',EEG.icaweights = jade_td_p( tmpdata, g.options{:} );
         case 'evd24',    EEG.icaweights = evd24( tmpdata, g.options{:} );
         case 'sobi',     EEG.icawinv = sobi( EEG.data, g.options{:} );
         case 'ng_ol',    [tmp EEG.icaweights] = ng_ol( tmpdata, g.options{:} );
         case 'acsobiro', EEG.icawinv = acsobiro( EEG.data, g.options{:} );
         case 'acrsobibpf', EEG.icawinv = acrsobibpf( tmpdata, g.options{:} );
        end;
        clear tmp;
        close(fig);
     otherwise, error('Pop_runica: unrecognized algorithm');
end;

% update weight and inverse matrices etc...
% -----------------------------------------
if ~isempty(fig), try, close(fig); catch, end; end;
if isempty(EEG.icaweights)
    EEG.icaweights = pinv(EEG.icawinv);
end;
if isempty(EEG.icasphere)
    EEG.icasphere  = eye(size(EEG.icaweights,2));
end;
if isempty(EEG.icawinv)
    EEG.icawinv    = pinv(EEG.icaweights*EEG.icasphere); % a priori same result as inv
end;

% copy back data to datasets if necessary
% ---------------------------------------
if length(g.dataset) > 1
    for i = g.dataset
        ALLEEG(i).icaweights = EEG.icaweights;
        ALLEEG(i).icasphere  = EEG.icasphere;
        ALLEEG(i).icawinv    = EEG.icawinv;
        ALLEEG(i).icachansind = g.chanind;
    end;            
    ALLEEG = eeg_checkset(ALLEEG);
else
    EEG = eeg_checkset(EEG);
    ALLEEG = eeg_store(ALLEEG, EEG, g.dataset);
end;

if nargin < 2 || selectamica
    com = sprintf('%s = pop_runica(%s, %s);', inputname(1), inputname(1),  vararg2str(g.options) ); %vararg2str({ 'icatype' g.icatype 'dataset' g.dataset 'options' g.options }) );
end;

return;

function tmprank2 = getrank(tmpdata);
    
    tmprank = rank(tmpdata);
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    %Here: alternate computation of the rank by Sven Hoffman
    %tmprank = rank(tmpdata(:,1:min(3000, size(tmpdata,2)))); old code
    covarianceMatrix = cov(tmpdata', 1);
    [E, D] = eig (covarianceMatrix);
    rankTolerance = 1e-7;
    tmprank2=sum (diag (D) > rankTolerance);
    if tmprank ~= tmprank2
        fprintf('Warning: fixing rank computation inconsistency (%d vs %d) most likely because running under Linux 64-bit Matlab', tmprank, tmprank2);
        tmprank2 = max(tmprank, tmprank2);
    end;
            
            
