% STD_PRECOMP - Precompute measures (ERP, spectrum, ERSP, ITC) for channels or
% components in a study. If channels are interpolated before
% computing the measures, the updated EEG datasets are also saved
% to disk. Called by POP_PRECOMP. Follow with POP_PLOTSTUDY.
% See Example below.
% Usage:
% >> [STUDY ALLEEG customRes] = std_precomp(STUDY, ALLEEG, chanorcomp, 'key', 'val', ...);
%
% Required inputs:
% STUDY - an EEGLAB STUDY set of loaded EEG structures
% ALLEEG - ALLEEG vector of one or more loaded EEG dataset structures
% chanorcomp - ['components'|'channels'| or channel cell array] The string
% 'components' forces the program to precompute all selected
% measures for components. The string 'channels' forces the
% program to compute all measures for all channels.
% A channel cell array containing channel labels will precompute
% the selected measures. Note that the name of the channel is
% not case-sensitive.
% Optional inputs:
% 'design' - [integer] use specific study index design to compute measure.
% 'cell' - [integer] compute measure only for a give data file.
% 'erp' - ['on'|'off'] pre-compute ERPs for each dataset.
% 'spec' - ['on'|'off'] pre-compute spectrum for each dataset.
% Use 'specparams' to set spectrum parameters.
% 'ersp' - ['on'|'off'] pre-compute ERSP for each dataset.
% Use 'erspparams' to set time/frequency parameters.
% 'itc' - ['on'|'off'] pre-compute ITC for each dataset.
% Use 'erspparams' to set time/frequency parameters.
% 'scalp' - ['on'|'off'] pre-compute scalp maps for components.
% 'allcomps' - ['on'|'off'] compute ERSP/ITC for all components ('off'
% only use pre-selected components in the pop_study interface).
% 'erpparams' - [cell array] Parameters for the std_erp function. See
% std_erp for more information.
% 'specparams' - [cell array] Parameters for the std_spec function. See
% std_spec for more information.
% 'erspparams' - [cell array] Optional arguments for the std_ersp function.
% 'erpimparams' - [cell array] Optional argument for std_erpimage. See
% std_erpimage for the list of arguments.
% 'recompute' - ['on'|'off'] force recomputing ERP file even if it is
% already on disk.
% 'rmicacomps' - ['on'|'off'|'processica'] remove ICA components pre-selected in
% each dataset (EEGLAB menu item, "Tools > Reject data using ICA
% > Reject components by map). This option is ignored when
% precomputing measures for ICA clusters. Default is 'off'.
% 'processica' forces to process ICA components instead of
% removing them.
% 'rmclust' - [integer array] remove selected ICA component clusters.
% For example, ICA component clusters containing
% artifacts. This option is ignored when precomputing
% measures for ICA clusters.
% 'customfunc' - [function_handle] execute a specific function on each
% EEGLAB dataset of the selected STUDY design. The fist
% argument to the function is an EEGLAB dataset. The
% function take the same list of argument as the std_erp
% function. Note that the data is only returned in the
% output of this function and is not saved in a data file.
% 'customparams' - [cell array] Parameters for the custom function above.
%
% Obsolete input:
% 'savetrials' - ['on'] save single-trials ERSP. Requires a lot of disk
% space (dataset space on disk times 10) but allow for refined
% single-trial statistics. This option is obsolete. As of
% EEGLAB 14, measures can only be saved in single trial
% mode.
%
% Outputs:
% STUDY - the input STUDY set with pre-clustering data added,
% for use by POP_CLUST
% ALLEEG - the input ALLEEG vector of EEG dataset structures, modified
% by adding preprocessing data as pointers to Matlab files that
% hold the pre-clustering component measures.
% customRes - cell array of custom results (one cell for each pair of
% independent variables as defined in the STUDY design).
% If a custom file extension is specified, this variable
% is empty as the function assumes that the result is too
% large to hold in memory.
%
% Example:
% >> [STUDY ALLEEG customRes] = std_precomp(STUDY, ALLEEG, { 'cz' 'oz' }, 'interp', ...
% 'on', 'erp', 'on', 'spec', 'on', 'ersp', 'on', 'erspparams', ...
% { 'cycles' [ 3 0.5 ], 'alpha', 0.01, 'padratio' 1 });
%
% % This prepares, channels 'cz' and 'oz' in the STUDY datasets.
% % If a data channel is missing in one dataset, it will be
% % interpolated (see EEG_INTERP). The ERP, spectrum, ERSP, and
% % ITC for each dataset is then computed.
%
% Example of custom call:
% The function below computes the ERP of the EEG data for each channel and plots it.
% >> [STUDY ALLEEG customres] = std_precomp(STUDY, ALLEEG, 'channels', 'customfunc', @(EEG,varargin)(mean(EEG.data,3)'));
% >> std_plotcurve([1:size(customres{1},1)], customres, 'chanlocs', eeg_mergelocs(ALLEEG.chanlocs)); % plot data
%
% The function below uses a data file to store the information then read
% the data and eventyally plot it
% >> [STUDY ALLEEG customres] = std_precomp(STUDY, ALLEEG, 'channels', 'customfunc', @(EEG,varargin)(mean(EEG.data,3)), 'customfileext', 'tmperp');
% >> erpdata = std_readcustom(STUDY, ALLEEG, 'tmperp');
% >> std_plotcurve([1:size(erpdata{1})], erpdata, 'chanlocs', eeg_mergelocs(ALLEEG.chanlocs)); % plot data
%
% Authors: Arnaud Delorme, SCCN, INC, UCSD, 2006-
% Copyright (C) Arnaud Delorme, SCCN, INC, UCSD, 2006, arno@sccn.ucsd.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, ALLEEG, customRes ] = std_precomp(STUDY, ALLEEG, chanlist, varargin)
if nargin < 2
help std_precomp;
return;
end
if nargin == 2
chanlist = 'channels'; % default to clustering the whole STUDY
end
customRes = [];
Ncond = length(STUDY.condition);
if Ncond == 0
Ncond = 1;
end
g = finputcheck(varargin, { 'erp' 'string' { 'on','off' } 'off';
'interp' 'string' { 'on','off' } 'off';
'ersp' 'string' { 'on','off' } 'off';
'recompute' 'string' { 'on','off' } 'off';
'spec' 'string' { 'on','off' } 'off';
'erpim' 'string' { 'on','off' } 'off';
'scalp' 'string' { 'on','off' } 'off';
'allcomps' 'string' { 'on','off' } 'off';
'bids' 'string' { 'on','off' } 'off';
'itc' 'string' { 'on','off' } 'off';
'savetrials', 'string', {'on'}, 'on';
'rmicacomps', 'string', {'on', 'off', 'processica'}, 'off';
'cell' 'integer' [] [];
'design' 'integer' [] STUDY.currentdesign;
'rmclust' 'integer' [] [];
'rmbase' 'integer' [] [];
'specparams' 'cell' {} {};
'erpparams' 'cell' {} {};
'customfunc' {'function_handle' 'integer' } { { } {} } [];
'customparams' 'cell' {} {};
'customfileext' 'string' [] '';
'erpimparams' 'cell' {} {};
'erspparams' 'cell' {} {}}, 'std_precomp');
if ischar(g), error(g); end
if ~isempty(g.rmbase), g.erpparams = { g.erpparams{:} 'rmbase' g.rmbase }; end
if ~isempty(g.customfileext), error('customfileext option has been removed from this function. Let us know if this is something you need.'); end
if strcmpi(g.bids, 'on'), fileSuffix = [ '_' STUDY.task ]; else fileSuffix = ''; end
if length(unique([ALLEEG.srate])) > 1
fprintf(2, 'Some STUDY datasets'' sampling rate is inomogeneous. Some plotting functions will error.')
end
if any([ALLEEG.trials]) > 1
if length(unique([ALLEEG.pnts])) > 1
fprintf(2, 'Some STUDY datasets have more than one trial but trials'' lengths differ. Some functions will error.')
end
end
% union of all channel structures
% -------------------------------
computewhat = 'channels';
if ischar(chanlist)
if strcmpi(chanlist, 'channels')
chanlist = [];
else % components
computewhat = 'components';
if strcmpi(g.allcomps, 'on')
chanlist = {};
for index = 1:length(STUDY.datasetinfo)
chanlist = { chanlist{:} [1:size(ALLEEG(STUDY.datasetinfo(index).index).icaweights,1)] };
end
else
chanlist = { STUDY.datasetinfo.comps };
end
end
end
if isempty(chanlist)
alllocs = eeg_mergelocs(ALLEEG.chanlocs);
chanlist = { alllocs.labels };
elseif ~isnumeric(chanlist{1})
alllocs = eeg_mergelocs(ALLEEG.chanlocs);
[~, c1, c2] = intersect_bc( lower({ alllocs.labels }), lower(chanlist));
[~, c2] = sort(c2);
alllocs = alllocs(c1(c2));
end
% test if interp and reconstruct channel list
% -------------------------------------------
if strcmpi(computewhat, 'channels')
if strcmpi(g.interp, 'on')
STUDY.changrp = [];
STUDY = std_changroup(STUDY, ALLEEG, chanlist, 'interp');
g.interplocs = alllocs;
else
STUDY.changrp = [];
STUDY = std_changroup(STUDY, ALLEEG, chanlist);
g.interplocs = struct([]);
end
end
% components or channels
% ----------------------
if strcmpi(computewhat, 'channels')
curstruct = STUDY.changrp;
else
curstruct = STUDY.cluster;
end
% get subjects and sessions
allSubjects = { STUDY.datasetinfo.subject };
allSessions = { STUDY.datasetinfo.session };
uniqueSubjects = unique(allSubjects);
allSessions(cellfun(@isempty, allSessions)) = { 1 };
allSessions = cellfun(@num2str, allSessions, 'uniformoutput', false);
uniqueSessions = unique(allSessions);
% handle parallelization
% ----------------------
eeglab_options;
parstatus_changed = 0;
if ~option_parallel
if ~exist('gcp')
disp('Parallel toolbox not found - nothing to worry about (except slower computation in some cases)');
else
delete(gcp('nocreate'));
ps = parallel.Settings;
parstatus = ps.Pool.AutoCreate;
ps.Pool.AutoCreate = false;
parstatus_changed = 1;
end
else
if exist('gcp')
ps = parallel.Settings;
parstatus = ps.Pool.AutoCreate;
ps.Pool.AutoCreate = true;
parstatus_changed = 1;
end
end
% compute custom measure
% ----------------------
if ~isempty(g.customfunc)
parfor iSubj = 1:length(uniqueSubjects)
for iSess = 1:length(uniqueSessions)
inds1 = strmatch( uniqueSubjects{iSubj}, allSubjects, 'exact');
inds2 = strmatch( uniqueSessions{iSess}, allSessions, 'exact');
inds = intersect(inds1, inds2);
if ~isempty(inds)
filepath = STUDY.datasetinfo(inds(1)).filepath;
trialinfo = std_combtrialinfo(STUDY.datasetinfo, inds, [ALLEEG.trials]);
filebase = getfilename(filepath, uniqueSubjects{iSubj}, uniqueSessions{iSess}, fileSuffix, length(uniqueSessions) == 1);
addopts = { 'savetrials', g.savetrials, 'recompute', g.recompute, 'fileout', filebase, 'trialinfo', trialinfo };
if strcmpi(computewhat, 'channels')
[tmpchanlist, opts] = getchansandopts(STUDY, ALLEEG, chanlist, inds, g);
std_custom(ALLEEG(inds), g.customfunc, 'channels', tmpchanlist, opts{:}, addopts{:});
else
if length(inds)>1 && ~isequal(chanlist{inds})
error(['ICA decompositions must be identical if' 10 'several datasets are concatenated' 10 'for a given subject' ]);
end
std_custom(ALLEEG(inds), g.customfunc, 'components', chanlist{inds(1)}, g.customparams{:});
end
end
end
end
end
% compute ERPs
% ------------
if strcmpi(g.erp, 'on')
% check dataset consistency
% -------------------------
allPnts = [ALLEEG(:).pnts];
if iscell(allPnts), allPnts = [ allPnts{:} ]; end
% we can align time frames later - not necessary
% if length(unique(allPnts)) > 1
% error([ 'Cannot compute ERPs because datasets' 10 'do not have the same number of data points' ])
% end
parfor iSubj = 1:length(uniqueSubjects)
for iSess = 1:length(uniqueSessions)
inds1 = strmatch( uniqueSubjects{iSubj}, allSubjects, 'exact');
inds2 = strmatch( uniqueSessions{iSess}, allSessions, 'exact');
inds = intersect(inds1, inds2);
if ~isempty(inds)
filepath = STUDY.datasetinfo(inds(1)).filepath;
trialinfo = std_combtrialinfo(STUDY.datasetinfo, inds, [ALLEEG.trials]);
filebase = getfilename(filepath, uniqueSubjects{iSubj}, uniqueSessions{iSess}, fileSuffix, length(uniqueSessions) == 1);
addopts = { 'savetrials' g.savetrials 'recompute' g.recompute 'fileout' filebase 'trialinfo' trialinfo };
if strcmpi(computewhat, 'channels')
[tmpchanlist,opts] = getchansandopts(STUDY, ALLEEG, chanlist, inds, g);
std_erp(ALLEEG(inds), 'channels', tmpchanlist, opts{:}, addopts{:}, g.erpparams{:});
else
if length(inds)>1 && ~isequal(chanlist{inds})
error(['ICA decompositions must be identical if' 10 'several datasets are concatenated' 10 'for a given subject' ]);
end
std_erp(ALLEEG(inds), 'components', chanlist{inds(1)}, addopts{:}, g.erpparams{:});
end
end
end
end
if isfield(curstruct, 'erpdata')
curstruct = rmfield(curstruct, 'erpdata');
curstruct = rmfield(curstruct, 'erptimes');
end
end
% compute spectrum
% ----------------
if strcmpi(g.spec, 'on')
parfor iSubj = 1:length(uniqueSubjects)
for iSess = 1:length(uniqueSessions)
inds1 = strmatch( uniqueSubjects{iSubj}, allSubjects, 'exact');
inds2 = strmatch( uniqueSessions{iSess}, allSessions, 'exact');
inds = intersect(inds1, inds2);
if ~isempty(inds)
filepath = STUDY.datasetinfo(inds(1)).filepath;
trialinfo = std_combtrialinfo(STUDY.datasetinfo, inds, [ALLEEG.trials]);
filebase = getfilename(filepath, uniqueSubjects{iSubj}, uniqueSessions{iSess}, fileSuffix, length(uniqueSessions) == 1);
addopts = { 'savetrials', g.savetrials, 'recompute', g.recompute, 'fileout', filebase, 'trialinfo', trialinfo };
if strcmpi(computewhat, 'channels')
[tmpchanlist, opts] = getchansandopts(STUDY, ALLEEG, chanlist, inds, g);
std_spec(ALLEEG(inds), 'channels', tmpchanlist, opts{:}, addopts{:}, g.specparams{:});
else
if length(inds)>1 && ~isequal(chanlist{inds})
error(['ICA decompositions must be identical if' 10 'several datasets are concatenated' 10 'for a given subject' ]);
end
std_spec(ALLEEG(inds), 'components', chanlist{inds(1)}, addopts{:}, g.specparams{:});
end
end
end
end
if isfield(curstruct, 'specdata')
curstruct = rmfield(curstruct, 'specdata');
curstruct = rmfield(curstruct, 'specfreqs');
end
end
% compute spectrum
% ----------------
if strcmpi(g.erpim, 'on')
% check dataset consistency
% -------------------------
allPnts = [ALLEEG(:).pnts];
if iscell(allPnts), allPnts = [ allPnts{:} ]; end
if length(unique(allPnts)) > 1
error([ 'Cannot compute ERPs because datasets' 10 'do not have the same number of data points' ])
end
if isempty(g.erpimparams)
tmpparams = {};
elseif iscell(g.erpimparams)
tmpparams = g.erpimparams;
else
tmpparams = fieldnames(g.erpimparams); tmpparams = tmpparams';
tmpparams(2,:) = struct2cell(g.erpimparams);
end
for iSubj = 1:length(uniqueSubjects)
for iSess = 1:length(uniqueSessions)
inds1 = strmatch( uniqueSubjects{iSubj}, allSubjects, 'exact');
inds2 = strmatch( uniqueSessions{iSess}, allSessions, 'exact');
inds = intersect(inds1, inds2);
if ~isempty(inds)
filepath = STUDY.datasetinfo(inds(1)).filepath;
trialinfo = std_combtrialinfo(STUDY.datasetinfo, inds);
filebase = getfilename(filepath, uniqueSubjects{iSubj}, uniqueSessions{iSess}, fileSuffix, length(uniqueSessions) == 1);
addopts = { 'savetrials' g.savetrials 'recompute' g.recompute 'fileout' filebase 'trialinfo' trialinfo tmpparams{:} };
if strcmpi(computewhat, 'channels')
[tmpchanlist, opts] = getchansandopts(STUDY, ALLEEG, chanlist, inds, g);
std_erpimage(ALLEEG(inds), 'channels', tmpchanlist, opts{:}, addopts{:});
else
if length(inds)>1 && ~isequal(chanlist{inds})
error(['ICA decompositions must be identical if' 10 'several datasets are concatenated' 10 'for a given subject' ]);
end
std_erpimage(ALLEEG(inds), 'components', chanlist{inds(1)}, addopts{:});
end
end
end
end
if isfield(curstruct, 'erpimdata')
curstruct = rmfield(curstruct, 'erpimdata');
curstruct = rmfield(curstruct, 'erpimtimes');
curstruct = rmfield(curstruct, 'erpimtrials');
curstruct = rmfield(curstruct, 'erpimevents');
end
end
% compute ERSP and ITC
% --------------------
if strcmpi(g.ersp, 'on') || strcmpi(g.itc, 'on')
% check dataset consistency
allPnts = [ALLEEG(:).pnts];
if iscell(allPnts), allPnts = [ allPnts{:} ]; end
if length(unique(allPnts)) > 1
error([ 'Cannot compute ERPs because datasets' 10 'do not have the same number of data points' ])
end
% options
if strcmpi(g.ersp, 'on') && strcmpi(g.itc, 'on'), type = 'both';
elseif strcmpi(g.ersp, 'on') , type = 'ersp';
else type = 'itc';
end
if isempty(g.erspparams)
tmpparams = {};
elseif iscell(g.erspparams)
tmpparams = g.erspparams;
else
tmpparams = fieldnames(g.erspparams); tmpparams = tmpparams';
tmpparams(2,:) = struct2cell(g.erspparams);
end
for iSubj = 1:length(uniqueSubjects) % parfor inside function
for iSess = 1:length(uniqueSessions)
inds1 = strmatch( uniqueSubjects{iSubj}, allSubjects, 'exact');
inds2 = strmatch( uniqueSessions{iSess}, allSessions, 'exact');
inds = intersect(inds1, inds2);
if ~isempty(inds)
filepath = STUDY.datasetinfo(inds(1)).filepath;
trialinfo = std_combtrialinfo(STUDY.datasetinfo, inds);
filebase = getfilename(filepath, uniqueSubjects{iSubj}, uniqueSessions{iSess}, fileSuffix, length(uniqueSessions) == 1);
addopts = { 'savetrials' g.savetrials 'recompute' g.recompute 'fileout' filebase 'trialinfo' trialinfo tmpparams{:} };
if strcmpi(computewhat, 'channels')
[tmpchanlist, opts] = getchansandopts(STUDY, ALLEEG, chanlist, inds, g);
std_ersp(ALLEEG(inds), 'channels', tmpchanlist, opts{:}, addopts{:});
else
if length(inds)>1 && ~isequal(chanlist{inds})
error(['ICA decompositions must be identical if' 10 'several datasets are concatenated' 10 'for a given subject' ]);
end
std_ersp(ALLEEG(inds), 'components', chanlist{inds(1)}, addopts{:});
end
end
end
end
% remove saved data if any
if isfield(curstruct, 'erspdata')
curstruct = rmfield(curstruct, 'erspdata');
curstruct = rmfield(curstruct, 'ersptimes');
curstruct = rmfield(curstruct, 'erspfreqs');
end
if isfield(curstruct, 'itcdata')
curstruct = rmfield(curstruct, 'itcdata');
curstruct = rmfield(curstruct, 'itctimes');
curstruct = rmfield(curstruct, 'itcfreqs');
end
end
% set back default parallelization
% ----------------------
if parstatus_changed
ps.Pool.AutoCreate = parstatus;
end
% compute component scalp maps
% ----------------------------
if strcmpi(g.scalp, 'on') && ~strcmpi(computewhat, 'channels')
for index = 1:length(STUDY.datasetinfo)
% find duplicate
duplicate = false;
for index2 = 1:index-1
if isequal(ALLEEG(index).icawinv, ALLEEG(index2).icawinv)
duplicate = true;
end
end
trialinfo = [];
if isempty(STUDY.datasetinfo(index).condition) % weird bug 2018a
trialinfo.condition = [];
else
trialinfo.condition = STUDY.datasetinfo(index).condition;
end
trialinfo.group = STUDY.datasetinfo(index).group;
trialinfo.session = STUDY.datasetinfo(index).session;
fprintf('Computing/checking topo file for subject %s\n', STUDY.datasetinfo(index).subject);
std_topo(ALLEEG(index), chanlist{index}, 'recompute', g.recompute,'trialinfo', trialinfo, 'fileout', STUDY.datasetinfo(index).filepath);
end
if isfield(curstruct, 'topo')
curstruct = rmfield(curstruct, 'topo');
curstruct = rmfield(curstruct, 'topox');
curstruct = rmfield(curstruct, 'topoy');
curstruct = rmfield(curstruct, 'topoall');
curstruct = rmfield(curstruct, 'topopol');
end
end
% empty cache
% -----------
STUDY.cache = [];
% components or channels
% ----------------------
if strcmpi(computewhat, 'channels')
STUDY.changrp = curstruct;
else STUDY.cluster = curstruct;
end
return;
% get file base name
% ------------------
function filebase = getfilename(filepath, subj, sess, fileSuffix, onlyOneSession)
if onlyOneSession
filebase = fullfile(filepath, [ subj fileSuffix ] );
else
sesStr = [ '0' sess ];
filebase = fullfile(filepath, [ subj '_ses-' sesStr(end-1:end) fileSuffix ] );
end
% find components in cluster for specific dataset
% -----------------------------------------------
function rmcomps = getclustcomps(STUDY, rmclust, settmpind)
rmcomps = cell(1,length(settmpind));
for idat = 1:length(settmpind) % scan dataset for which to find component clusters
for rmi = 1:length(rmclust) % scan clusters
comps = STUDY.cluster(rmclust(rmi)).comps;
sets = STUDY.cluster(rmclust(rmi)).sets;
indmatch = find(sets(:) == settmpind(idat));
indmatch = ceil(indmatch/size(sets,1)); % get the column number
rmcomps{idat} = [rmcomps{idat} comps(indmatch(:)') ];
end
rmcomps{idat} = sort(rmcomps{idat});
end
% make option array and channel list (which depend on interp) for any type of measure
% ----------------------------------------------------------------------
function [tmpchanlist, opts] = getchansandopts(STUDY, ALLEEG, chanlist, idat, g)
opts = { };
if ~isempty(g.rmclust) || strcmpi(g.rmicacomps, 'on') || strcmpi(g.rmicacomps, 'processica')
rmcomps = cell(1,length(idat));
if ~isempty(g.rmclust)
rmcomps = getclustcomps(STUDY, g.rmclust, idat);
end
if strcmpi(g.rmicacomps, 'on')
for ind = 1:length(idat)
rmcomps{ind} = union_bc(rmcomps{ind}, find(ALLEEG(idat(ind)).reject.gcompreject));
end
elseif strcmpi(g.rmicacomps, 'processica')
for ind = 1:length(idat)
rmcomps{ind} = union_bc(rmcomps{ind}, find(~ALLEEG(idat(ind)).reject.gcompreject));
end
end
opts = { opts{:} 'rmcomps' rmcomps };
end
if strcmpi(g.interp, 'on')
tmpchanlist = chanlist;
allocs = eeg_mergelocs(ALLEEG.chanlocs);
[tmp1 tmp2 neworder] = intersect_bc( {allocs.labels}, chanlist);
[tmp1 ordertmp2] = sort(tmp2);
neworder = neworder(ordertmp2);
opts = { opts{:} 'interp' allocs(neworder) };
else
newchanlist = [];
tmpchanlocs = ALLEEG(idat(1)).chanlocs;
chanlocs = { tmpchanlocs.labels };
for i=1:length(chanlist)
newchanlist = [ newchanlist strmatch(chanlist{i}, chanlocs, 'exact') ];
end
tmpchanlocs = ALLEEG(idat(1)).chanlocs;
tmpchanlist = { tmpchanlocs(newchanlist).labels };
end
% compute full file names
% -----------------------
function res = computeFullFileName(filePaths, fileNames)
for index = 1:length(fileNames)
res{index} = fullfile(filePaths{index}, fileNames{index});
end
Datasets
Models
