% STD_ERSPPLOT - plot STUDY cluster ERSPs. Displays either mean cluster ERSPs,
% or else all cluster component ERSPs plus the mean cluster
% ERSP in one figure per condition. The ERSPs can be plotted
% only if component ERSPs were computed and saved in the
% EEG datasets in the STUDY. These may either be computed
% during pre-clustering using the gui-based function
% POP_PRECLUST, or via the equivalent commandline functions
% EEG_CREATEDATA and EEG_PRECLUST. Called by POP_CLUSTEDIT.
% Usage:
% >> [STUDY] = std_erspplot(STUDY, ALLEEG, key1, val1, key2, val2);
% >> [STUDY erspdata ersptimes erspfreqs pgroup pcond pinter] = ...
% std_erspplot(STUDY, ALLEEG ...);
%
% Inputs:
% STUDY - STUDY set comprising some or all of the EEG datasets in ALLEEG.
% ALLEEG - global vector of EEG structures for the datasets included
% in the STUDY. ALLEEG for a STUDY set is typically created
% using LOAD_ALLEEG.
% either 'channels' or 'cluster' inputs are also mandatory.
%
% Optional inputs for channel plotting:
% 'channels' - [numeric vector] specific channel group to plot. By
% default, the grand mean channel ERSP is plotted (using the
% same format as for the cluster component means described above)
% 'subject' - [numeric vector] In 'changrp' mode (above), index of
% the subject(s) to plot. Else by default, plot all components
% in the cluster.
% 'plotsubjects' - ['on'|'off'] When 'on', plot ERSP of all subjects.
% 'noplot' - ['on'|'off'] When 'on', only return output values. Default
% is 'off'.
%
% Optional inputs:
% 'clusters' - [numeric vector|'all'] indices of clusters to plot.
% If no component indices ('comps' below) are given, the average
% ERSPs of the requested clusters are plotted in the same figure,
% with ERSPs for different conditions (and groups if any) plotted
% in different colors. In 'comps' (below) mode, ERSP for each
% specified cluster are plotted in separate figures (one per
% condition), each overplotting cluster component ERSP plus the
% average cluster ERSP in bold. Note this parameter has no effect
% if the 'comps' option (below) is used. {default: 'all'}
% 'comps' - [numeric vector|'all'] indices of the cluster components to plot.
% Note that 'comps', 'all' is equivalent to 'plotsubjects', 'on'.
%
% Other optional inputs:
% 'plotmode' - ['normal'|'condensed'|'none'] 'normal' -> plot in a new figure;
% 'condensed' -> plot all curves in the current figure in a
% condensed fashion. 'none' toggles off plotting {default: 'normal'}
% 'key','val' - All optional inputs to POP_SPECPARAMS are also accepted here
% to plot subset of time, statistics etc. The values used by default
% are the ones set using POP_SPECPARAMS and stored in the
% STUDY structure.
% Output:
% STUDY - the input STUDY set structure with the plotted cluster
% mean ERSPs stored to allow quick replotting
% erspdata - [cell] ERSP data for each condition, group and subjects.
% size of cell array is [nconds x ngroups]. Size of each element
% is [freqs x times x subjects] for data channels or
% [freqs x times x components] for component clusters. This
% array may be gicen as input directly to the STATCOND f
% unction or STD_STATS function to compute statistics.
% ersptimes - [array] ERSP time point latencies.
% erspfreqs - [array] ERSP point frequency values.
% pgroup - [array or cell] p-values group statistics. Output of the
% STATCOND function.
% pcond - [array or cell] condition statistics. Output of the STATCOND
% function.
% pinter - [array or cell] groups x conditions statistics. Output of
% STATCOND function.
%
% Important note: This data implement baseline correction and proper
% scaling.
%
% Example:
% >> [STUDY] = std_erspplot(STUDY,ALLEEG, 'clusters', 'all', ...
% 'mode', 'together');
% % Plot the mean ERSPs of all clusters in STUDY together
% % on the same figure.
%
% Known limitations: when plotting multiple clusters, the output
% contains the last plotted cluster.
%
% See also: POP_CLUSTEDIT, POP_PRECLUST, EEG_CREATEDATA, EEG_PRECLUST, POP_CLUSTEDIT
%
% Authors: Arnaud Delorme, CERCO, August, 2006
% Copyright (C) Arnaud Delorme, arno@salk.edu
%
% This file is part of EEGLAB, see http://www.eeglab.org
% for the documentation and details.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
function [STUDY, allersp, alltimes, allfreqs, pgroup, pcond, pinter, events] = std_erspplot(STUDY, ALLEEG, varargin)
if nargin < 2
help std_erspstatplot;
return;
end
% find datatype and default options
% ---------------------------------
dtype = 'ersp';
for ind = 1:2:length(varargin)
if strcmpi(varargin{ind}, 'datatype')
dtype = varargin{ind+1};
end
end
if strcmpi(dtype, 'erpim')
STUDY = pop_erpimparams(STUDY, varargin{:});
params = STUDY.etc.erpimparams;
else STUDY = pop_erspparams( STUDY, varargin{:});
params = STUDY.etc.erspparams;
end
% get parameters
% --------------
statstruct.etc = STUDY.etc;
statstruct = pop_statparams(statstruct, varargin{:});
% potentially missing fields
% --------------------------
fields = { 'freqrange' [];
'topofreq' [];
'topotrial' [];
'trialrange' []
'concatenate' 'off';
'colorlimits' [];
'ersplim' [];
'itclim' [];
'maskdata' 'off';
'subbaseline' 'off' };
for ind=1:size(fields,1)
if ~isfield(params, fields{ind,1}),
params = setfield(params, fields{ind,1}, fields{ind,2});
end
end
% decode input parameters
% -----------------------
options = mystruct(varargin);
options = myrmfield( options, myfieldnames(params));
options = myrmfield( options, myfieldnames(statstruct.etc.statistics));
options = myrmfield( options, { 'threshold' 'statistics' } ); % for backward compatibility
[ opt, moreparams ] = finputcheck( options, { ...
'design' 'integer' [] STUDY.currentdesign;
'caxis' 'real' [] [];
'statmode' , 'string' , [] , '';
'channels' , 'cell' , [] , {};
'clusters' 'integer' [] [];
'datatype' 'string' { 'itc','ersp','pac' 'erpim' } 'ersp';
'plottf' 'real' [] [];
'mode', 'string', [], '';
'comps', {'integer','string'}, [], [];
'plotsubjects' 'string' { 'on','off' } 'off';
'noplot' 'string' { 'on','off' } 'off';
'plotmode' 'string' { 'normal','condensed','none' } 'normal';
'subject' 'string' [] '' }, ...
'std_erspstatplot', 'ignore');
if ischar(opt), error(opt); end
if strcmpi(opt.noplot, 'on'), opt.plotmode = 'none'; end
if isempty(opt.caxis)
if strcmpi(opt.datatype, 'ersp')
opt.caxis = params.ersplim;
elseif strcmpi(opt.datatype, 'itc') && ~isempty(params.itclim)
opt.caxis = [-params.itclim(end) params.itclim(end)];
end
end
allconditions = {};
allgroups = {};
condname = '';
groupname = '';
if length(STUDY.design(opt.design).variable) > 0, allconditions = STUDY.design(opt.design).variable(1).value; condname = STUDY.design(opt.design).variable(1).label; end
if length(STUDY.design(opt.design).variable) > 1, allgroups = STUDY.design(opt.design).variable(2).value; groupname = STUDY.design(opt.design).variable(2).label; end
% for backward compatibility
% --------------------------
if strcmpi(opt.datatype, 'erpim')
params.topofreq = params.topotrial;
opt.caxis = params.colorlimits;
valunit = 'trials';
else
valunit = 'Hz';
end
if isempty(opt.plottf) && ~isempty(params.topofreq) && ~isempty(params.topotime) && ~isnan(params.topofreq(1)) && ~isnan(params.topotime(1))
params.plottf = [ params.topofreq(1) params.topofreq(end) params.topotime(1) params.topotime(end) ];
else params.plottf = opt.plottf;
end
%if strcmpi(opt.mode, 'comps'), opt.plotsubjects = 'on'; end %deprecated
stats = statstruct.etc.statistics;
stats.fieldtrip.channelneighbor = struct([]); % assumes one channel or 1 component
if isempty(STUDY.design(opt.design).variable)
stats.paired = { };
else
stats.paired = { STUDY.design(opt.design).variable(:).pairing };
end
if strcmpi(stats.singletrials, 'off') && ((~isempty(opt.subject) || ~isempty(opt.comps)))
if strcmpi(stats.condstats, 'on') || strcmpi(stats.groupstats, 'on')
stats.groupstats = 'off';
stats.condstats = 'off';
disp('No statistics for single subject/component');
end
end
if length(opt.comps) == 1
stats.condstats = 'off'; stats.groupstats = 'off';
disp('Statistics cannot be computed for single component');
end
alpha = fastif(strcmpi(stats.mode, 'eeglab'), stats.eeglab.alpha, stats.fieldtrip.alpha);
mcorrect = fastif(strcmpi(stats.mode, 'eeglab'), stats.eeglab.mcorrect, stats.fieldtrip.mcorrect);
method = fastif(strcmpi(stats.mode, 'eeglab'), stats.eeglab.method, ['Fieldtrip ' stats.fieldtrip.method ]);
plottfopt = { ...
'ersplim', opt.caxis, ...
'threshold', alpha, ...
'effect', stats.effect, ...
'maskdata', params.maskdata ...
'averagemode' params.averagemode };
if ~isempty(params.plottf) && length(opt.channels) < 5 && isempty(opt.clusters)
warndlg2(strvcat('ERSP/ITC parameters indicate that you wish to plot scalp maps', 'Select at least 5 channels to plot topography'));
allersp = {}; alltimes = []; allfreqs = []; pgroup = []; pcond = []; pinter = []; events = [];
return;
end
% plot single scalp map
% ---------------------
if ~isempty(opt.channels)
if isempty(params.plottf) && length(opt.channels) > 1 && strcmpi(stats.singletrials, 'on')
error('Cannot plot several channels on the same figure when using single trial statistics');
end
[STUDY, allersp, alltimes, allfreqs, events, paramsersp] = std_readdata(STUDY, ALLEEG, 'channels', opt.channels, 'timerange', params.timerange, ...
'freqrange', params.freqrange, 'subject', opt.subject, 'singletrials', stats.singletrials, 'design', opt.design, 'datatype', opt.datatype, 'subbaseline', params.subbaseline);
% 'concatenate', params.concatenate NOT TAKEN INTO ACCOUNT
unitPower = newtimefpowerunit(paramsersp);
if strcmpi(opt.datatype, 'ersp') && strcmpi(params.subbaseline, 'off')
if strcmpi(stats.singletrials, 'off')
% rational for baseline
% - no baseline calculation or log transformation at reading time (except single trial baseline if any)
% - if not single trial and no common baseline, remove baseline and transform data here in each condition (before stats)
% - otherwise, do so after baseline removal
paramsersp.singletrials = stats.singletrials;
paramsersp.commonbase = params.subbaseline;
[allersp,basesamples,basevals] = newtimefbaseln(allersp, alltimes, paramsersp);
else
opt.subbaseline = 'on';
disp('Warning: when using single-trial statistics, a common baseline is forced across all conditions');
end
end
%[STUDY allersp alltimes allfreqs tmp events unitPower] = std_readerp(STUDY, ALLEEG, 'channels', opt.channels, 'infotype', opt.datatype, 'subject', opt.subject, ...
% 'singletrials', stats.singletrials, 'subbaseline', params.subbaseline, 'timerange', params.timerange, 'freqrange', params.freqrange, 'design', opt.design, 'concatenate', params.concatenate);
%tic
%[STUDY allersp alltimes allfreqs tmp events unitPower] = std_readersp(STUDY, ALLEEG, 'channels', opt.channels, 'infotype', opt.datatype, 'subject', opt.subject, ...
% 'singletrials', stats.singletrials, 'subbaseline', params.subbaseline, 'timerange', params.timerange, 'freqrange', params.freqrange, 'design', opt.design, 'concatenate', params.concatenate);
%toc
% average single trials
% ---------------------
if strcmpi(opt.datatype, 'ersp')
if strcmpi(params.subbaseline, 'on')
disp('Computing common baseline has changed since EEGLAB 14: averaging baselines is now');
disp('performed before log-transformation of the baseline - in a similar way that baseline');
disp('is averaged across trials (log transformation is only performed at the end for display)');
% see above for rational for baseline
paramsersp.singletrials = stats.singletrials;
paramsersp.commonbase = params.subbaseline;
allersp = newtimefbaseln(allersp, alltimes, paramsersp);
else
paramsersp.singletrials = stats.singletrials;
allersp = cellfun(@(x)newtimefbaseln(x, alltimes, paramsersp), allersp, 'uniformoutput', false);
end
% transform to log (except single trials where the transformation
% is after taking the average - which is after doing stats
if strcmpi(stats.singletrials, 'off')
if ~isfield(paramsersp, 'scale') || strcmpi(paramsersp.scale, 'log')
allersp = cellfun(@(x)10*log10(x), allersp, 'uniformoutput', false);
end
end
end
% select specific time and freq
% -----------------------------
if ~isempty(params.plottf)
if length(params.plottf) < 3
params.plottf(3:4) = params.plottf(2);
params.plottf(2) = params.plottf(1);
end
[~, fi1] = min(abs(allfreqs-params.plottf(1)));
[~, fi2] = min(abs(allfreqs-params.plottf(2)));
[~, ti1] = min(abs(alltimes-params.plottf(3)));
[~, ti2] = min(abs(alltimes-params.plottf(4)));
for index = 1:length(allersp(:))
allersp{index} = mean(mean(allersp{index}(fi1:fi2,ti1:ti2,:,:),1),2);
allersp{index} = reshape(allersp{index}, [1 size(allersp{index},3) size(allersp{index},4) ]);
end
% prepare channel neighbor matrix for Fieldtrip
statstruct = std_prepare_neighbors(statstruct, ALLEEG, 'channels', opt.channels);
stats.fieldtrip.channelneighbor = statstruct.etc.statistics.fieldtrip.channelneighbor;
params.plottf = { params.plottf(1:2) params.plottf(3:4) };
[pcond, pgroup, pinter] = std_stat(allersp, stats);
if (~isempty(pcond) && length(pcond{1}) == 1) || (~isempty(pgroup) && length(pgroup{1}) == 1), pcond = {}; pgroup = {}; pinter = {}; end % single subject STUDY
else
[pcond, pgroup, pinter] = std_stat(allersp, stats);
if (~isempty(pcond ) && (size( pcond{1},1) == 1 || size( pcond{1},2) == 1)) || ...
(~isempty(pgroup) && (size(pgroup{1},1) == 1 || size(pgroup{1},2) == 1))
pcond = {}; pgroup = {}; pinter = {};
disp('No statistics possible for single subject STUDY');
end % single subject STUDY
end
if strcmpi(stats.singletrials, 'on')
% For ITC this is optional but it does not change anything
if strcmpi(opt.datatype, 'ersp')
if ndims(allersp{1}) == 4, for ind = 1:length(allersp(:)), allersp{ind} = mean(allersp{ind},4); end; end
if ndims(allersp{1}) == 3, for ind = 1:length(allersp(:)), allersp{ind} = mean(allersp{ind},3); end; end
if strcmpi(opt.datatype, 'ersp') && (~isfield(paramsersp, 'scale') || strcmpi(paramsersp.scale, 'log'))
allersp = cellfun(@(x)10*log10(x), allersp, 'uniformoutput', false);
end
elseif strcmpi(opt.datatype, 'itc')
if ~isfield(params, 'itctype'), params.itctype = 'phasecoher'; end
for iDat = 1:length(allersp(:))
allersp{iDat} = newtimefitc(allersp{iDat}, params.itctype);
allersp{iDat} = abs(allersp{iDat});
end
end
end
% Average channels
if ~strcmpi(params.averagechan, 'off') && length(opt.channels) > 1
for index = 1:length(allersp(:))
if strcmpi(params.averagemode, 'ave')
allersp{index} = squeeze(mean(allersp{index},3));
else
disp('Computing RMS while preserving sign');
tfsign = sign(squeeze(mean(allersp{index},3)));
allersp{index} = squeeze(sqrt(mean(allersp{index}.^2,3))).*tfsign;
end
end
end
% plot specific channel(s)
% ------------------------
if ~strcmpi(opt.plotmode, 'none')
locsOri = eeg_mergelocs(ALLEEG.chanlocs);
locs = locsOri(std_chaninds(STUDY, opt.channels));
% in case channels are being averaged
if ~strcmpi(params.averagechan, 'off') && length(opt.channels) > 1
if length(opt.channels) ~= length(locsOri)
chanlabels = { locs.labels };
chanlabels(2,:) = {','};
chanlabels(2,end) = {''};
locs(1).labels = [ chanlabels{:} ];
else
locs(1).labels = 'All channels';
end
locs(2:end) = [];
end
if ~isempty(params.plottf) % incomtible with averagechan above
alltitles = std_figtitle('threshold', alpha, 'mcorrect', mcorrect, 'condstat', stats.condstats, 'cond2stat', stats.groupstats, ...
'statistics', method, 'condnames', allconditions, 'cond2names', allgroups, 'chanlabels', { locs.labels }, ...
'subject', opt.subject, 'valsunit', { valunit 'ms' }, 'vals', params.plottf, 'datatype', upper(opt.datatype), ...
'effect', stats.effect, 'factor1', condname, 'factor2', groupname);
std_chantopo(allersp, 'groupstats', pgroup, 'condstats', pcond, 'interstats', pinter, 'caxis', opt.caxis, ...
'chanlocs', locs, 'threshold', alpha, 'titles', alltitles, 'effect', stats.effect);
else
if length(locs) > 1, opt.plottopo = 'on'; else opt.plottopo = 'off'; end
if length(locs) == 1 && size(allersp{1},3) > 1
% channels should be in 3rd dim; reshape data to put subjects in the 4th dim if number of channels is 1
for index = 1:length(allersp(:))
allersp{index} = reshape(allersp{index}, size(allersp{index},1), size(allersp{index},2), 1, size(allersp{index},3));
end
end
% nc = ceil(sqrt(length(opt.channels)));
% nr = ceil(length(opt.channels)/nc);
% for index = 1:length(locs)
% if length(opt.channels) > 1, try, subplot(nr,nc,index, 'align'); catch, subplot(nr,nc,index); end; end
% tmpersp = cell(size(allersp));
% for ind = 1:length(allersp(:))
% if ~isempty(allersp{ind})
% tmpersp{ind} = squeeze(allersp{ind}(:,:,index,:));
% tmpersp{ind} = permute(tmpersp{ind}, [2 1 3]); % somehow time/freq are swapped in ntimes = nfreqs
% end
% end
% for ind = 1:length(allersp(:))
% if ~isempty(allersp{ind})
% allersp{ind} = permute(allersp{ind}, [2 1 3 4]); % somehow time/freq are swapped for tftopo
% end
% end
alltitles = std_figtitle('threshold', alpha, 'mcorrect', mcorrect, 'condstat', stats.condstats, 'cond2stat', stats.groupstats, ...
'statistics', method, 'condnames', allconditions, 'cond2names', allgroups, ...
'subject', opt.subject, 'datatype', upper(opt.datatype), 'plotmode', opt.plotmode, ...
'effect', stats.effect, 'factor1', condname, 'factor2', groupname);
std_plottf(alltimes, allfreqs, allersp, 'datatype', opt.datatype, 'titles', alltitles, ...
'groupstats', pgroup, 'condstats', pcond, 'interstats', pinter, 'plottopo', opt.plottopo, 'plotmode', ...
opt.plotmode, 'unitcolor', unitPower, 'chanlocs', locs, 'events', events, plottfopt{:});
% end
end
end
else
if length(opt.clusters) > 1 && strcmpi(stats.singletrials, 'on')
error('Cannot plot several components on the same figure when using single trial statistics');
end
if length(opt.clusters) > 1 && ~strcmpi(opt.plotmode, 'none'), figure; opt.plotmode = 'condensed'; end
nc = ceil(sqrt(length(opt.clusters)));
nr = ceil(length(opt.clusters)/nc);
comp_names = {};
if length(opt.clusters) > 1 && ( strcmpi(stats.condstats, 'on') || strcmpi(stats.groupstats, 'on'))
stats.condstats = 'off'; stats.groupstats = 'off';
end
for index = 1:length(opt.clusters)
[STUDY, allersp, alltimes, allfreqs, events, paramsersp] = std_readdata(STUDY, ALLEEG, 'clusters', opt.clusters(index), 'datatype', opt.datatype, ...
'component', opt.comps, 'singletrials', stats.singletrials, 'subbaseline', params.subbaseline, 'timerange', params.timerange, 'freqrange', params.freqrange, 'design', opt.design, 'concatenate', params.concatenate);
if length(opt.clusters) > 1, try, subplot(nr,nc,index, 'align'); catch, subplot(nr,nc,index); end; end
unitPower = newtimefpowerunit(paramsersp);
% plot specific component
% -----------------------
if ~isempty(opt.comps)
comp_names = { STUDY.cluster(opt.clusters(index)).comps(opt.comps) };
opt.subject = STUDY.datasetinfo(STUDY.cluster(opt.clusters(index)).sets(1,opt.comps)).subject;
end
% average single trials
% ---------------------
if strcmpi(opt.datatype, 'ersp')
if strcmpi(params.subbaseline, 'on')
disp('Computing common baseline has changed since EEGLAB 14: averaging baselines is now');
disp('performed before log-transformation of the baseline - in a similar way that baseline');
disp('is averaged across trials (log transformation is only performed at the end for display)');
% see above for rational for baseline
paramsersp.singletrials = stats.singletrials;
paramsersp.commonbase = params.subbaseline;
allersp = newtimefbaseln(allersp, alltimes, paramsersp);
else
paramsersp.singletrials = stats.singletrials;
allersp = cellfun(@(x)newtimefbaseln(x, alltimes, paramsersp), allersp, 'uniformoutput', false);
end
% transform to log (except single trials)
if strcmpi(stats.singletrials, 'off')
if ~isfield(paramsersp, 'scale') || strcmpi(paramsersp.scale, 'log')
allersp = cellfun(@(x)10*log10(x), allersp, 'uniformoutput', false);
end
end
end
% statistics
% ----------
[pcond pgroup pinter] = std_stat(allersp, stats);
if (~isempty(pcond ) && (size( pcond{1},1) == 1 || size( pcond{1},2) == 1)) || ...
(~isempty(pgroup) && (size(pgroup{1},1) == 1 || size(pgroup{1},2) == 1)),
pcond = {}; pgroup = {}; pinter = {};
disp('No statistics possible for single subject STUDY');
end % single subject STUDY
if strcmpi(stats.singletrials, 'on')
if strcmpi(opt.datatype, 'ersp')
if ndims(allersp{1}) == 4, for ind = 1:length(allersp(:)), allersp{ind} = mean(allersp{ind},4); end; end
if ndims(allersp{1}) == 3, for ind = 1:length(allersp(:)), allersp{ind} = mean(allersp{ind},3); end; end
if ~isfield(paramsersp, 'scale') || strcmpi(paramsersp.scale, 'log')
allersp = cellfun(@(x)10*log10(x), allersp, 'uniformoutput', false);
end
elseif strcmpi(opt.datatype, 'itc')
if ~isfield(params, 'itctype'), params.itctype = 'phasecoher'; end
for iDat = 1:length(allersp(:))
allersp{iDat} = newtimefitc(allersp{iDat}, params.itctype);
allersp{iDat} = abs(allersp{iDat});
end
end
end
% plot specific component
% -----------------------
if index == length(opt.clusters), opt.legend = 'on'; end
if ~strcmpi(opt.plotmode, 'none')
alltitles = std_figtitle('threshold', alpha, 'mcorrect', mcorrect, 'condstat', stats.condstats, 'cond2stat', stats.groupstats, ...
'statistics', method, 'condnames', allconditions, 'cond2names', allgroups, 'clustname', STUDY.cluster(opt.clusters(index)).name, 'compnames', comp_names, ...
'subject', opt.subject, 'datatype', upper(opt.datatype), 'plotmode', opt.plotmode, ...
'effect', stats.effect, 'factor1', condname, 'factor2', groupname);
std_plottf(alltimes, allfreqs, allersp, 'datatype', opt.datatype, ...
'groupstats', pgroup, 'condstats', pcond, 'interstats', pinter, 'plotmode', ...
opt.plotmode, 'titles', alltitles, ...
'events', events, 'unitcolor', unitPower, 'chanlocs', ALLEEG(1).chanlocs, plottfopt{:});
end
end
end
% remove fields and ignore fields who are absent
% ----------------------------------------------
function s = myrmfield(s, f);
for index = 1:length(f)
if isfield(s, f{index})
s = rmfield(s, f{index});
end
end
% convert to structure (but take into account cells)
% --------------------------------------------------
function s = mystruct(v);
for index=1:length(v)
if iscell(v{index})
v{index} = { v{index} };
end
end
try
s = struct(v{:});
catch, error('Parameter error'); end
% convert to structure (but take into account cells)
% --------------------------------------------------
function s = myfieldnames(v);
s = fieldnames(v);
if isfield(v, 'eeglab')
s2 = fieldnames(v.eeglab);
s = { s{:} s2{:} };
end
if isfield(v, 'fieldtrip')
s3 = fieldnames(v.fieldtrip);
for index=1:length(s3)
s3{index} = [ 'fieldtrip' s3{index} ];
end
s = { s{:} s3{:} };
end
Datasets
Models
