% STD_MAKETRIALINFO - create trial information structure using the
% .epoch structure of EEGLAB datasets
%
% Usage:
% >> [STUDY,trialinfo] = std_maketrialinfo(STUDY, ALLEEG);
% >> [~,trialinfo] = std_maketrialinfo([], EEG);
%
% Inputs:
% STUDY - EEGLAB STUDY set
% ALLEEG - vector of the EEG datasets included in the STUDY structure
%
% Inputs:
% STUDY - EEGLAB STUDY set updated. The fields which is created or
% updated is STUDY.datasetinfo.trialinfo
% trialinfo - Trial information structure (cell array)
%
% Authors: Arnaud Delorme, SCCN/INC/UCSD, April 2010
% Copyright (C) Arnaud Delorme arno@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,alltrialinfo] = std_maketrialinfo(STUDY, ALLEEG)
%% test if .epoch field exist in ALLEEG structure
noepochfield = cellfun(@isempty, { ALLEEG.epoch });
if any(noepochfield)
if all(noepochfield)
fprintf('Datasets are all continuous, so trial information is skipped.\n');
else
fprintf(2, 'Warning: inconsistent datasets (some are continuous and some are not). Please use [ALLEEG.trials] to see which ones and fix the problem.\n');
end
return;
end
for iEEG = 1:length(ALLEEG)
% fill in empty field values and fill in with values in the same epoch
ALLEEG(iEEG) = eeg_uniformepochinfo(ALLEEG(iEEG));
end
%% check if conversion of event is necessary
ff = {};
flagConvert = true;
for index = 1:length(ALLEEG), ff = union(ff, fieldnames(ALLEEG(index).event)); end
for iField = 1:length(ff)
fieldChar = zeros(1,length(ALLEEG))*NaN;
for index = 1:length(ALLEEG)
if isfield(ALLEEG(index).event, ff{iField})
if ischar(ALLEEG(index).event(1).(ff{iField}))
fieldChar(index) = 1;
else
fieldChar(index) = 0;
end
end
end
if ~all(fieldChar(~isnan(fieldChar)) == 1) && ~all(fieldChar(~isnan(fieldChar)) == 0)
% need conversion to char
for index = 1:length(ALLEEG)
if fieldChar(index) == 0
if flagConvert, disp('Warning: converting some event fields to strings - this may be slow'); flagConvert = false; end
for iEvent = 1:length(ALLEEG(index).event)
ALLEEG(index).event(iEvent).(ff{iField}) = num2str(ALLEEG(index).event(iEvent).(ff{iField}));
end
end
end
end
end
%% Make trial info
for index = 1:length(ALLEEG)
tmpevent = ALLEEG(index).event;
eventlat = abs(eeg_point2lat( [ tmpevent.latency ], [ tmpevent.epoch ], ALLEEG(index).srate, [ALLEEG(index).xmin ALLEEG(index).xmax]));
events = ALLEEG(index).event;
ff = fieldnames(events);
ff = setdiff_bc(ff, { 'latency' 'urevent' 'epoch' });
trialinfo = [];
% process time locking event fields
% ---------------------------------
indtle = find(eventlat == 0);
epochs = [ events(indtle).epoch ];
extractepoch = true;
% Double checking and changing threshold
if length(epochs) < ALLEEG(index).trials
indtle = find(eventlat < 0.02);
epochs = [ events(indtle).epoch ];
end
if length(epochs) ~= ALLEEG(index).trials
% special case where there are not the same number of time-locking
% event as there are data epochs
if length(unique(epochs)) ~= ALLEEG(index).trials
extractepoch = false;
disp('std_maketrialinfo: not the same number of time-locking events as trials, trial info ignored');
else
% pick one event per epoch
[tmp tmpind] = unique_bc(epochs(end:-1:1)); % reversing the array ensures the first event gets picked
tmpind = length(epochs)+1-tmpind;
indtle = indtle(tmpind);
if length(indtle) ~= ALLEEG(index).trials
extractepoch = false;
disp('std_maketrialinfo: not the same number of time-locking events as trials, trial info ignored');
end
end
end
if extractepoch
commands = {};
for f = 1:length(ff)
eval( [ 'eventvals = {events(indtle).' ff{f} '};' ]);
%if isnumeric(eventvals{1})
%eventvals = cellfun(@num2str, eventvals, 'uniformoutput', false);
% eventvals = [ eventvals{:} ];
%end
if isstruct(eventvals{1})
fprintf('Warning: Structure detected in event field - skipping\n')
else
commands = { commands{:} ff{f} eventvals };
end
end
trialinfo = struct(commands{:});
STUDY.datasetinfo(index).trialinfo = trialinfo;
end
alltrialinfo{index} = trialinfo;
% % same as above but 10 times slower
% for e = 1:length(ALLEEG(index).event)
% if eventlat(e) < 0.0005 % time locking event only
% epoch = events(e).epoch;
% for f = 1:length(ff)
% fieldval = getfield(events, {e}, ff{f});
% trialinfo = setfield(trialinfo, {epoch}, ff{f}, fieldval);
% end
% end
% end
end
% check HED field with unicode characters
% ---------------------------------------
if isfield(STUDY.datasetinfo, 'trialinfo')
if isfield(STUDY.datasetinfo(1).trialinfo(1), 'HED')
changeUnicode = false;
for iDat = 1:length(STUDY.datasetinfo)
for iTrial = 1:length(STUDY.datasetinfo(iDat).trialinfo)
STUDY.datasetinfo(iDat).trialinfo(iTrial).HED(STUDY.datasetinfo(iDat).trialinfo(iTrial).HED > 255) = 32;
changeUnicode = true;
end
end
if changeUnicode
disp('Unicode character detected in HED event field, removing them to improve stability');
end
end
end
Datasets
Models
