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pop_prop_extended.m
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pop_prop_extended.m
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function [fh, EEG, com] = pop_prop_extended(EEG, typecomp, chanorcomp, winhandle, spec_opt, erp_opt, scroll_event, classifier_name, varargin)
% POP_PROP_EXTENDED See various common properties of an EEG channel or component
% Creates a figure containing a scalp topography or channel location,
% erpimage of the data, power spectral density creaty by spectopo(), a
% scrolling plot of activity with events and epoch markings overlaid, and
% dipole locations and residual variance if showing component properties
% and dipole information is already present in the EEG structure. If
% called with only the first two arguments, a GUI opens to select the
% rest. If only one argument is given, typecomp will be set to channels
% (1) and the GUI will open.
%
% Inputs
% EEG: EEGLAB EEG structure
% typecomp: 0 for component, 1 for channel
% chanorcomp: channel or component index to plot
% winhandle: pass NaN, used for compatability with component rejection
% spec_opt: cell array of options which are passed to spectopo()
% erp_opt: cell array of options which are passed to erpimage()
% scroll_event: 0 to hide events in scroll plot, 1 to show them
% classifier_name: string indicating which component classifier to
% use (must match a field name in EEG.etc.ic_classification)
% varargin: do not use this.
%
% Outputs:
% fh: handle for figure used
% EEG: EEG structure
%
% Notes: for the dipole plot, you need EEG.dipfit precalculated
%
% See also: spectopo(), erpimage(), scrollplot(), topoplot()
%
% TODO Notes: remove all axes(ax* called in erpimage (2793 2836 3515 [3475])
% also fix axcopy in the same way
% make erpimage, spectopo, and dipplot accept axhandle inputs.
%
% starting from pop_prop:
% updated by Ramon Martinez-Cancino and Luca Pion-Tonachini (2015)
% updated again by Luca Pion-Tonachini (2017)
% setup
if nargin < 1
help pop_prop_extended;
return;
end;
if nargin < 5
spec_opt = {};
end;
if nargin < 6
erp_opt = {};
end;
if nargin < 7
scroll_event = 1;
end;
if nargin < 8
classifier_name = '';
if ~typecomp && isfield(EEG.etc, 'ic_classification')
classifiers = fieldnames(EEG.etc.ic_classification);
if ~isempty(classifiers)
if any(strcmpi(classifiers, 'ICLabel'));
classifier_name = 'ICLabel';
else
classifier_name = classifiers{1};
end
end
end
end;
if nargin == 1
typecomp = 1; % defaults
chanorcomp = 1;
end;
if nargin == 10
% from callback
EEG = chanorcomp;
typecomp = winhandle;
chanorcomp = spec_opt;
winhandle = erp_opt;
spec_opt = scroll_event;
erp_opt = classifier_name;
scroll_event = varargin{1};
classifier_name = varargin{2};
varargin = {};
end
if typecomp == 0 && isempty(EEG.icaweights)
error('No ICA weights recorded for this dataset -- first run ICA on it');
end;
if nargin == 2
promptstr = { fastif(typecomp,'Channel index(ices) to plot:','Component index(ices) to plot:') ...
'Spectral options (see spectopo() help):','Erpimage options (see erpimage() help):' ...
[' Draw events over scrolling ' fastif(typecomp,'channel','component') ' activity']};
inistr = { '1' '''freqrange'', [2 80]' '', 1};
stylestr = {'edit', 'edit', 'edit', 'checkbox'};
% labels when available
if ~typecomp && isfield(EEG.etc, 'ic_classification')
classifiers = fieldnames(EEG.etc.ic_classification);
if ~isempty(classifiers)
iclabel_ind = find(strcmpi(classifiers, 'ICLabel'));
promptstr = [promptstr {classifiers}];
inistr = [inistr {fastif(isempty(iclabel_ind), 1, iclabel_ind)}];
stylestr = [stylestr {'popupmenu'}];
end
end
result = inputdlg3( 'prompt', promptstr,'style', stylestr, 'default', inistr, ...
'title', [fastif(typecomp,'Channel','Component') ' properties - pop_prop_extended()']);
if size( result, 1 ) == 0
return; end
chanorcomp = eval( [ '[' result{1} ']' ] );
spec_opt = eval( [ '{' result{2} '}' ] );
erp_opt = eval( [ '{' result{3} '}' ] );
scroll_event = result{4};
if ~typecomp && isfield(EEG.etc, 'ic_classification') && ~isempty(classifiers)
classifiers = fieldnames(EEG.etc.ic_classification);
classifier_name = classifiers{result{5}};
end
end;
% plotting several component properties
% -------------------------------------
if length(chanorcomp) > 1
for index = chanorcomp
pop_prop_extended(EEG, typecomp, index, nan, spec_opt, erp_opt, scroll_event, classifier_name, varargin{:}); % call recursively for each chanorcomp
end;
com = sprintf('pop_prop_extended( %s, %d, [%s], NaN, %s, %s, %d, ''%s''', inputname(1), ...
typecomp, int2str(chanorcomp), vararg2str({spec_opt}), vararg2str({erp_opt}), scroll_event, classifier_name);
if ~isempty(varargin)
com = [com sprintf(', %s', vararg2str(varargin))];
end
com = [com ');'];
return;
end;
if chanorcomp < 1 || chanorcomp > EEG.nbchan % should test for > number of components ??? -sm
error('Component index out of range');
end;
% initiialize figure
try
icadefs;
catch
BACKCOLOR = [0.9300 0.9600 1.0000];
end
if typecomp
basename = ['Channel ' EEG.chanlocs(chanorcomp).labels ];
else
basename = ['IC' int2str(chanorcomp) ];
end
fh = figure('name', [basename ' - pop_prop_extended()'],...
'color', BACKCOLOR,...
'numbertitle', 'off',...
'PaperPositionMode','auto',...
'Visible', 'off', ...
'ToolBar', 'none',...
'MenuBar','none');
pos = get(fh,'position');
set(fh,'Position', [pos(1)-1200+pos(3) pos(2)-700+pos(4) 1200 700]);
% initialize ica data
if ~typecomp
if ~isempty(EEG.icaact)
icaacttmp = EEG.icaact(chanorcomp, :, :);
else
icaacttmp = eeg_getdatact(EEG, 'component', chanorcomp);
end
end
% check for labels. if they exist, shorten scroll and plot them
if ~typecomp && isfield(EEG.etc, 'ic_classification') && ~isempty(classifier_name)
classifiers = fieldnames(EEG.etc.ic_classification);
classifier_name = classifiers{strcmpi(classifiers, classifier_name)};
if size(EEG.etc.ic_classification.(classifier_name).classifications, 1) ...
~= size(EEG.icawinv, 2)
warning(['The number of ICs do not match the number of IC classifications. This will result in incorrectly plotted labels. Please rerun ' classifier_name])
end
nclass = length(EEG.etc.ic_classification.(classifier_name).classes);
labelax = axes('Parent', fh, 'Position', [0.32 0.6389 0.035 0.28]);
yoffset = 0.5;
xoffset = 0.01;
barh(EEG.etc.ic_classification.(classifier_name).classifications(chanorcomp, end:-1:1), 'y')
axis(labelax, [-xoffset, 1, 1 - yoffset, nclass + yoffset])
set(labelax, 'YTickLabel', EEG.etc.ic_classification.(classifier_name).classes(end:-1:1), ...
'XGrid', 'on', 'XTick', 0:0.5:1)
xlabel 'Probability'
title(classifier_name)
for it = 1:nclass
text(0.5, it, sprintf('%.1f%%', EEG.etc.ic_classification.(classifier_name).classifications(chanorcomp, end - it + 1) * 100), ...
'fontsize', 11, 'HorizontalAlignment', 'center', ...
'Parent', labelax)
end
scroll_position = [0.4 0.7389 0.5929 0.18];
else
scroll_position = [0.3712 0.7389 0.5641 0.18];
end
% plot time series
% datax = axes('Parent', fh, 'position',,'units','normalized');
datax = axes('Parent', fh, 'Position',scroll_position,'units','normalized');
scrollax = uicontrol('Parent', fh, 'Style', 'Slider', ...
'Units', 'Normalized', 'Position', [scroll_position(1) 0.6389 scroll_position(3) 0.025]);
if ~scroll_event
EEG.event = []; end
if typecomp
scrollplot(EEG.times, single(EEG.data(chanorcomp, :, :)), 5, EEG.event, fh, datax, scrollax);
tstitle_h = title('Channel Time Series', 'fontsize', 14, 'FontWeight', 'Normal');
else
scrollplot(EEG.times, single(icaacttmp), 5, EEG.event, fh, datax, scrollax);
tstitle_h = title(['Scrolling IC' int2str(chanorcomp) ' Activity'], 'fontsize', 14, 'FontWeight', 'Normal');
end
set(tstitle_h,'FontSize',14, 'Position', get(tstitle_h, 'Position'), 'units', 'normalized');
set(datax,'FontSize',12);
xlabel(datax,'Time (ms)','fontsize', 14);
ylabel(datax,'uV');
% plot scalp map
axes('Parent', fh, 'position',[0.0143 0.6331 0.3121 0.3267],'units','normalized');
if typecomp
topoplot( chanorcomp, EEG.chanlocs, 'chaninfo', EEG.chaninfo, ...
'electrodes','off', 'style', 'blank', 'emarkersize1chan', 12); axis square;
title(['Channel ' EEG.chanlocs(chanorcomp).labels], 'fontsize', 14, 'FontWeight', 'Normal');
else
topoplot(EEG.icawinv(:,chanorcomp), EEG.chanlocs, ...
'chaninfo', EEG.chaninfo, 'electrodes','on'); axis square;
title(['IC' num2str(chanorcomp)], 'fontsize', 14, 'FontWeight', 'Normal');
end
% plot pvaf
if ~typecomp
maxsamp = 1e5;
n_samp = min(maxsamp, EEG.pnts*EEG.trials);
try
samp_ind = randperm(EEG.pnts*EEG.trials, n_samp);
catch
samp_ind = randperm(EEG.pnts*EEG.trials);
samp_ind = samp_ind(1:n_samp);
end
if ~isempty(EEG.icachansind)
icachansind = EEG.icachansind;
else
icachansind = 1:EEG.nbchan;
end
datavar = mean(var(EEG.data(icachansind, samp_ind), [], 2));
projvar = mean(var(EEG.data(icachansind, samp_ind) - ...
EEG.icawinv(:, chanorcomp) * icaacttmp(1, samp_ind), [], 2));
pvafval = 100 *(1 - projvar/ datavar);
pvaf = num2str(pvafval, '%3.1f');
text(0.5, -0.12, {['{% scalp data var. accounted for}: ' pvaf '%']}, ...
'fontsize', 13,'Units','Normalized', 'HorizontalAlignment', 'center');
end
% % plot labels
% if ~typecomp && isfield(EEG.etc, 'ic_classification')
% classifier = classifiers{1}; % TODO: gui option for this
% [slabels, sind] = sort(EEG.etc.ic_classification.(classifier).classifications(chanorcomp, :), 'ascend');
% text(0.5, -0.2, sprintf('%s: %s %.1f%%, %s %.1f%%', classifier, ...
% EEG.etc.ic_classification.(classifier).classes{sind(end)}, 100 * slabels(end), ...
% EEG.etc.ic_classification.(classifier).classes{sind(end - 1)}, 100 * slabels(end - 1)), ...
% 'fontsize', 13,'Units','Normalized', 'HorizontalAlignment', 'center');
% end
% plot erpimage
herp = axes('Parent', fh, 'position',[0.0643 0.1102 0.2421 0.3850],'units','normalized');
eeglab_options;
if EEG.trials > 1 % epoched data
axis(herp, 'off')
EEG.times = linspace(EEG.xmin, EEG.xmax, EEG.pnts);
if EEG.trials < 6
ei_smooth = 1;
else
ei_smooth = 1;
end
if typecomp == 1 % plot channel
offset = nan_mean(EEG.data(chanorcomp,:));
erp=nan_mean(squeeze(EEG.data(chanorcomp,:,:))')-offset;
erp_limits=get_era_limits(erp);
[t1,t2,t3,t4,axhndls] = erpimage( EEG.data(chanorcomp,:)-offset, ones(1,EEG.trials)*10000, EEG.times*1000, ...
'', ei_smooth, 1, 'caxis', 2/3, 'cbar','erp','erp_vltg_ticks',erp_limits, erp_opt{:});
else % plot component
offset = nan_mean(icaacttmp(:));
era = nan_mean(squeeze(icaacttmp)')-offset;
era_limits = get_era_limits(era);
[t1,t2,t3,t4,axhndls] = erpimage( icaacttmp-offset, ones(1,EEG.trials)*10000, EEG.times*1000, ...
'', ei_smooth, 1, 'caxis', 2/3, 'cbar','erp','erp_vltg_ticks',era_limits, erp_opt{:});
end;
title(['Epoched IC' int2str(chanorcomp) ' Activity'], 'fontsize', 14, 'FontWeight', 'Normal');
lab = text(1.27, .95,'RMS uV per scalp channel');
else % continuoous data
ERPIMAGELINES = 200; % show 200-line erpimage
while size(EEG.data,2) < ERPIMAGELINES*EEG.srate
ERPIMAGELINES = 0.9 * ERPIMAGELINES;
end
ERPIMAGELINES = round(ERPIMAGELINES);
if ERPIMAGELINES > 2 % give up if data too small
if ERPIMAGELINES < 6
ei_smooth = 1;
else
ei_smooth = 3;
end
erpimageframes = floor(size(EEG.data,2)/ERPIMAGELINES);
erpimageframestot = erpimageframes*ERPIMAGELINES;
eegtimes = linspace(0, erpimageframes-1, length(erpimageframes));
if typecomp == 1 % plot channel
offset = nan_mean(EEG.data(chanorcomp,:));
% Note: we don't need to worry about ERP limits, since ERPs
% aren't visualized for continuous data
[t1,t2,t3,t4,axhndls] = erpimage( reshape(EEG.data(chanorcomp,1:erpimageframestot),erpimageframes,ERPIMAGELINES)-offset, ones(1,ERPIMAGELINES)*10000, eegtimes , ...
'', ei_smooth, 1, 'caxis', 2/3, 'cbar', erp_opt{:});
else % plot component
offset = nan_mean(icaacttmp(:));
[t1,t2,t3,t4,axhndls] = erpimage(reshape(icaacttmp(:,1:erpimageframestot),erpimageframes,ERPIMAGELINES)-offset,ones(1,ERPIMAGELINES)*10000, eegtimes , ...
'', ei_smooth, 1, 'caxis', 2/3, 'cbar', erp_opt{:});
end
try
ylabel(axhndls{1}, 'Data');
catch
ylabel(axhndls(1), 'Data');
end
title('Continuous Data', 'fontsize', 14, 'FontWeight', 'Normal');
lab = text(1.27, .85,'RMS uV per scalp channel');
else
axis off;
text(0.1, 0.3, [ 'No erpimage plotted' 10 'for small continuous data']);
end
end
if exist('axhndls', 'var')
try
% 2014+
axhndls{1}.FontSize = 12;
axhndls{1}.YLabel.FontSize = 14;
set(axhndls{2},'position', get(axhndls{2},'position') - [0.01 0 0.02 0]);
try
axhndls{3}.FontSize = 12;
axhndls{3}.XLabel.FontSize = 14; %#ok<NASGU>
catch
axhndls{1}.XLabel.FontSize = 14; %#ok<NASGU>
end
catch
% 2013-
set(axhndls(1), 'FontSize', 12)
set(get(axhndls(1), 'Ylabel'), 'FontSize', 14)
set(axhndls(2),'position', get(axhndls(2),'position') - [0.01 0 0.02 0], ...
'Fontsize', 12)
if ~isnan(axhndls(3))
set(axhndls(3), 'FontSize', 12)
set(get(axhndls(3), 'Xlabel'), 'FontSize', 14)
else
set(get(axhndls(1), 'Xlabel'), 'FontSize', 14)
end
end
set(lab, 'rotation', -90, 'FontSize', 12)
end
% plot spectrum
try
hfreq = axes('Parent', fh, 'position', [0.5765 0.1109 0.3587 0.4336], 'units', 'normalized');
if typecomp
spectopo( EEG.data(chanorcomp,:), EEG.pnts, EEG.srate, spec_opt{:} );
title(hfreq,'Channel Activity Power Spectrum','units','normalized', 'fontsize', 14, 'FontWeight', 'Normal');
else
spectopo( icaacttmp(1, :), EEG.pnts, EEG.srate, 'mapnorm', EEG.icawinv(:,chanorcomp), spec_opt{:} );
title(hfreq,['IC' int2str(chanorcomp) ' Activity Power Spectrum'],'units','normalized', 'fontsize', 14, 'FontWeight', 'Normal');
end
set(get(hfreq, 'ylabel'), 'string', 'Power 10*log_{10}(uV^2/Hz)', 'fontsize', 14);
set(get(hfreq, 'xlabel'), 'string', 'Frequency (Hz)', 'fontsize', 14, 'fontweight', 'normal');
set(hfreq, 'fontsize', 14, 'fontweight', 'normal');
xlims = xlim;
hfreqline = findobj(hfreq, 'type', 'line');
xdata = get(hfreqline, 'xdata');
ydata = get(hfreqline, 'ydata');
ind = xdata >= xlims(1) & xdata <= xlims(2);
axis on;
axis([xlims min(ydata(ind)) max(ydata(ind))])
box on;
grid on;
catch e
cla(hfreq);
disp(e)
text(0.1, 0.3, [ 'Error: no spectrum plotted' 10 ' make sure you have the ' 10 'signal processing toolbox']);
end
% Defining path for system
eeglabpath = which('eeglab.m');
pathtmp = fileparts(eeglabpath);
dipfits = dir(fullfile(pathtmp, 'plugins', 'dipfit*'));
[~, dipfit_order] = sort(cellfun(@(c) str2double(c(7:end)), {dipfits.name}), 'descend');
for it_dipfit_version = dipfit_order
dipfit_folder = fullfile(pathtmp, 'plugins', dipfits(it_dipfit_version).name);
meshdatapath = fullfile(dipfit_folder, 'standard_BEM', 'standard_vol.mat');
mripath = fullfile(dipfit_folder, 'standard_BEM', 'standard_mri.mat');
if ~typecomp && exist(meshdatapath,'file') == 2 && exist(mripath,'file') == 2
% dipplot
if isfield(EEG, 'dipfit') && ~isempty(EEG.dipfit)
try
rv = num2str(EEG.dipfit.model(chanorcomp).rv*100, '%.1f');
catch
rv = 'N/A';
end
dip_background = axes('Parent', fh, 'position', [0.41 0.1 0.1 0.1557*3+0.0109], ...
'units', 'normalized', 'XLim', [0 1], 'Ylim', [0 1]);
patch([0 0 1 1], [0 1 1 0], 'k', 'parent', dip_background)
axis(dip_background, 'off')
colors = {'g', 'm', 'y'};
% axial
ax(1) = axes('Parent', fh, 'position', [0.41 0.1109 0.1 0.1557], 'units', 'normalized');
axis equal off
dipplot(EEG.dipfit.model(chanorcomp), ...
'meshdata', meshdatapath, ...
'mri', mripath, ...
'normlen', 'on', 'coordformat', 'MNI', 'axistight', 'on', 'gui', 'off', 'view', [0 0 1], 'pointout', 'on');
temp = axes('Parent', fh, 'position', [0.41 0.1109 0.1 0.1557], 'units', 'normalized');
copyobj(allchild(ax(1)),temp);
delete(ax(1))
ax(1) = temp;
axis equal off
temp = get(ax(1),'children');
ind = find(strcmp('line', get(temp, 'type')));
for it = 1:length(ind)
if mod(it, 2)
set(temp(ind(it)), 'markersize', 15, 'color', colors{ceil(it / 2)})
else
set(temp(ind(it)), 'linewidth', 2, 'color', colors{ceil(it / 2)})
end
end
% coronal
ax(2) = axes('Parent', fh, 'position', [0.41 0.2666 0.1 0.1557], 'units', 'normalized');
axis equal off
copyobj(allchild(ax(1)),ax(2));
view([0 -1 0])
axis equal off
temp = get(ax(2),'children');
ind = find(strcmp('line', get(temp, 'type')));
for it = 1:length(ind)
if mod(it, 2)
set(temp(ind(it)), 'markersize', 15, 'color', colors{ceil(it / 2)})
else
set(temp(ind(it)), 'linewidth', 2, 'color', colors{ceil(it / 2)})
end
end
% sagital
ax(3) = axes('Parent', fh, 'position', [0.41 0.4223 0.1 0.1557], 'units', 'normalized');
axis equal off
copyobj(allchild(ax(1)),ax(3));
view([1 0 0])
axis equal off
temp = get(ax(3),'children');
ind = find(strcmp('line', get(temp, 'type')));
for it = 1:length(ind)
if mod(it, 2)
set(temp(ind(it)), 'markersize', 15, 'color', colors{ceil(it / 2)})
else
set(temp(ind(it)), 'linewidth', 2, 'color', colors{ceil(it / 2)})
end
end
% dipole text
dip_title = title(dip_background, 'Dipole Position', 'FontWeight', 'Normal');
set(dip_title,'FontSize',14);
set(fh, 'CurrentAxes', ax(1))
if size(EEG.dipfit.model(chanorcomp).momxyz, 1) == 2
dmr = norm(EEG.dipfit.model(chanorcomp).momxyz(1,:)) ...
/ norm(EEG.dipfit.model(chanorcomp).momxyz(2,:));
if dmr<1
dmr = 1/dmr; end
text(-50,-173,{['RV: ' rv '%']; ['DMR:' num2str(dmr,'%.1f')]})
else
text(-50,-163,['RV: ' rv '%'])
end
% exit loop over dipfit versions
break
end
end
end
% final figure adjustments
rotate3d(fh, 'off');
set(fh, 'color', BACKCOLOR, 'visible', 'on')
% display buttons
% ---------------
if ~exist('winhandle', 'var')
winhandle = nan; end
if isobject(winhandle) || ~isnan(winhandle)
COLREJ = '[1 0.6 0.6]';
COLACC = '[0.75 1 0.75]';
bottom = 0.005;
height = 0.04;
% CANCEL button
% -------------
h = uicontrol(gcf, 'Style', 'pushbutton', 'backgroundcolor', GUIBUTTONCOLOR, 'string', 'Cancel', 'Units','Normalized','Position',[0.2 bottom 0.1 height], 'callback', 'close(gcf);');
% VALUE button
% -------------
hval = uicontrol(gcf, 'Style', 'pushbutton', 'backgroundcolor', GUIBUTTONCOLOR, 'string', 'Values', 'Units','Normalized', 'Position', [0.325 bottom 0.1 height]);
% REJECT button
% -------------
if ~isempty(EEG.reject.gcompreject)
status = EEG.reject.gcompreject(chanorcomp);
else
status = 0;
end;
hr = uicontrol(gcf, 'Style', 'pushbutton', 'backgroundcolor', eval(fastif(status,COLREJ,COLACC)), ...
'string', fastif(status, 'REJECT', 'ACCEPT'), 'Units','Normalized', 'Position', [0.45 bottom 0.1 height], 'userdata', status, 'tag', 'rejstatus');
command = [ 'set(gcbo, ''userdata'', ~get(gcbo, ''userdata''));' ...
'if get(gcbo, ''userdata''),' ...
' set( gcbo, ''backgroundcolor'',' COLREJ ', ''string'', ''REJECT'');' ...
'else ' ...
' set( gcbo, ''backgroundcolor'',' COLACC ', ''string'', ''ACCEPT'');' ...
'end;' ];
set( hr, 'callback', command);
% HELP button
% -------------
h = uicontrol(gcf, 'Style', 'pushbutton', 'backgroundcolor', GUIBUTTONCOLOR, 'string', 'HELP', 'Units','Normalized', 'Position', [0.575 bottom 0.1 height], 'callback', 'pophelp(''pop_prop'');');
% OK button
% ---------
command = [ 'global EEG;' ...
'tmpstatus = get( findobj(''parent'', gcbf, ''tag'', ''rejstatus''), ''userdata'');' ...
'EEG.reject.gcompreject(' num2str(chanorcomp) ') = tmpstatus;' ];
if winhandle ~= 0
command = [ command ...
sprintf('if tmpstatus set(%3.15f, ''backgroundcolor'', %s); else set(%3.15f, ''backgroundcolor'', %s); end;', ...
winhandle, COLREJ, winhandle, COLACC)];
end;
command = [ command 'close(gcf); clear tmpstatus' ];
h = uicontrol(gcf, 'Style', 'pushbutton', 'string', 'OK', 'backgroundcolor', GUIBUTTONCOLOR, 'Units','Normalized', 'Position',[0.7 bottom 0.1 height], 'callback', command);
% draw the figure for statistical values
% --------------------------------------
index = num2str( chanorcomp );
command = [ ...
'figure(''MenuBar'', ''none'', ''name'', ''Statistics of the component'', ''numbertitle'', ''off'');' ...
'' ...
'pos = get(gcf,''Position'');' ...
'set(gcf,''Position'', [pos(1) pos(2) 340 340]);' ...
'pos = get(gca,''position'');' ...
'q = [pos(1) pos(2) 0 0];' ...
's = [pos(3) pos(4) pos(3) pos(4)]./100;' ...
'axis off;' ...
'' ...
'txt1 = sprintf(''(\n' ...
'Entropy of component activity\t\t%2.2f\n' ...
'> Rejection threshold \t\t%2.2f\n\n' ...
' AND \t\t\t----\n\n' ...
'Kurtosis of component activity\t\t%2.2f\n' ...
'> Rejection threshold \t\t%2.2f\n\n' ...
') OR \t\t\t----\n\n' ...
'Kurtosis distibution \t\t\t%2.2f\n' ...
'> Rejection threhold\t\t\t%2.2f\n\n' ...
'\n' ...
'Current thesholds sujest to %s the component\n\n' ...
'(after manually accepting/rejecting the component, you may recalibrate thresholds for future automatic rejection on other datasets)'',' ...
'EEG.stats.compenta(' index '), EEG.reject.threshentropy, EEG.stats.compkurta(' index '), ' ...
'EEG.reject.threshkurtact, EEG.stats.compkurtdist(' index '), EEG.reject.threshkurtdist, fastif(EEG.reject.gcompreject(' index '), ''REJECT'', ''ACCEPT''));' ...
'' ...
'uicontrol(gcf, ''Units'',''Normalized'', ''Position'',[-11 4 117 100].*s+q, ''Style'', ''frame'' );' ...
'uicontrol(gcf, ''Units'',''Normalized'', ''Position'',[-5 5 100 95].*s+q, ''String'', txt1, ''Style'',''text'', ''HorizontalAlignment'', ''left'' );' ...
'h = uicontrol(gcf, ''Style'', ''pushbutton'', ''string'', ''Close'', ''Units'',''Normalized'', ''Position'', [35 -10 25 10].*s+q, ''callback'', ''close(gcf);'');' ...
'clear txt1 q s h pos;' ];
set( hval, 'callback', command);
if isempty( EEG.stats.compenta )
set(hval, 'enable', 'off');
end;
com = sprintf('pop_prop_extended( %s, %d, [%s], 0, %s, %s, %d, ''%s''', inputname(1), ...
typecomp, int2str(chanorcomp), vararg2str({spec_opt}), vararg2str({erp_opt}), scroll_event, classifier_name);
if ~isempty(varargin)
com = [com sprintf(', %s', vararg2str(varargin))];
end
com = [com ');'];
else
com = sprintf('pop_prop_extended( %s, %d, [%s], NaN, %s, %s, %d, ''%s''', inputname(1), ...
typecomp, int2str(chanorcomp), vararg2str({spec_opt}), vararg2str({erp_opt}), scroll_event, classifier_name);
if ~isempty(varargin)
com = [com sprintf(', %s', vararg2str(varargin))];
end
com = [com ');'];
end;
drawnow;
function era_limits=get_era_limits(era)
%function era_limits=get_era_limits(era)
%
% Returns the minimum and maximum value of an event-related
% activation/potential waveform (after rounding according to the order of
% magnitude of the ERA/ERP)
%
% Inputs:
% era - [vector] Event related activation or potential
%
% Output:
% era_limits - [min max] minimum and maximum value of an event-related
% activation/potential waveform (after rounding according to the order of
% magnitude of the ERA/ERP)
mn=min(era);
mx=max(era);
mn=orderofmag(mn)*round(mn/orderofmag(mn));
mx=orderofmag(mx)*round(mx/orderofmag(mx));
era_limits=[mn mx];
function ord=orderofmag(val)
%function ord=orderofmag(val)
%
% Returns the order of magnitude of the value of 'val' in multiples of 10
% (e.g., 10^-1, 10^0, 10^1, 10^2, etc ...)
% used for computing erpimage trial axis tick labels as an alternative for
% plotting sorting variable
val=abs(val);
if val>=1
ord=1;
val=floor(val/10);
while val>=1,
ord=ord*10;
val=floor(val/10);
end
return;
else
ord=1/10;
val=val*10;
while val<1,
ord=ord/10;
val=val*10;
end
return;
end
% inputdlg3() - A comprehensive gui automatic builder. This function takes
% text, type of GUI and default value and builds
% automatically a simple graphic interface.
%
% Usage:
% >> [outparam outstruct] = inputdlg3( 'key1', 'val1', 'key2', 'val2', ... );
%
% Inputs:
% 'prompt' - cell array of text
% 'style' - cell array of style for each GUI. Default is edit.
% 'default' - cell array of default values. Default is empty.
% 'tags' - cell array of tag text. Default is no tags.
% 'tooltip' - cell array of tooltip texts. Default is no tooltip.
%
% Output:
% outparam - list of outputs. The function scans all lines and
% add up an output for each interactive uicontrol, i.e
% edit box, radio button, checkbox and listbox.
% userdat - 'userdata' value of the figure.
% strhalt - the function returns when the 'userdata' field of the
% button with the tag 'ok' is modified. This returns the
% new value of this field.
% outstruct - returns outputs as a structure (only tagged ui controls
% are considered). The field name of the structure is
% the tag of the ui and contain the ui value or string.
%
% Note: the function also adds three buttons at the bottom of each
% interactive windows: 'CANCEL', 'HELP' (if callback command
% is provided) and 'OK'.
%
% Example:
% res = inputdlg3('prompt', { 'What is your name' 'What is your age' } );
% res = inputdlg3('prompt', { 'Chose a value below' 'Value1|value2|value3' ...
% 'uncheck the box' }, ...
% 'style', { 'text' 'popupmenu' 'checkbox' }, ...
% 'default',{ 0 2 1 });
%
% Author: Arnaud Delorme, Tim Mullen, Christian Kothe, SCCN, INC, UCSD
%
% See also: supergui(), eeglab()
% Copyright (C) Arnaud Delorme, SCCN, INC, UCSD, 2010, [email protected]
%
% 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
function [result, userdat, strhalt, resstruct] = inputdlg3( varargin);
if nargin < 2
help inputdlg3;
return;
end;
% check input values
% ------------------
[opt addopts] = finputcheck(varargin, { 'prompt' 'cell' [] {};
'style' 'cell' [] {};
'default' 'cell' [] {};
'tag' 'cell' [] {};
'tooltip','cell' [] {}}, 'inputdlg3', 'ignore');
if isempty(opt.prompt), error('The ''prompt'' parameter must be non empty'); end;
if isempty(opt.style), opt.style = cell(1,length(opt.prompt)); opt.style(:) = {'edit'}; end;
if isempty(opt.default), opt.default = cell(1,length(opt.prompt)); opt.default(:) = {0}; end;
if isempty(opt.tag), opt.tag = cell(1,length(opt.prompt)); opt.tag(:) = {''}; end;
% creating GUI list input
% -----------------------
uilist = {};
uigeometry = {};
outputind = ones(1,length(opt.prompt));
for index = 1:length(opt.prompt)
if strcmpi(opt.style{index}, 'edit')
uilist{end+1} = { 'style' 'text' 'string' opt.prompt{index} };
uilist{end+1} = { 'style' 'edit' 'string' opt.default{index} 'tag' opt.tag{index} 'tooltip' opt.tag{index}};
uigeometry{index} = [2 1];
else
uilist{end+1} = { 'style' opt.style{index} 'string' opt.prompt{index} 'value' opt.default{index} 'tag' opt.tag{index} 'tooltip' opt.tag{index}};
uigeometry{index} = [1];
end;
if strcmpi(opt.style{index}, 'text')
outputind(index) = 0;
end;
end;
[tmpresult, userdat, strhalt, resstruct] = inputgui('uilist', uilist,'geometry', uigeometry, addopts{:});
try
result = cell(1,length(opt.prompt));
result(find(outputind)) = tmpresult;
catch
result = [];
end