% EEG_CHECKSET - check the consistency of the fields of an EEG dataset
% Also: See EEG dataset structure field descriptions below.
%
% Usage: >> [EEGOUT,changes] = eeg_checkset(EEG); % perform all checks
% except 'makeur'
% >> [EEGOUT,changes] = eeg_checkset(EEG, 'keyword'); % perform 'keyword' check(s)
%
% Inputs:
% EEG - EEGLAB dataset structure or (ALLEEG) array of EEG structures
%
% Optional keywords:
% 'icaconsist' - if EEG contains several datasets, check whether they have
% the same ICA decomposition
% 'epochconsist' - if EEG contains several datasets, check whether they have
% identical epoch lengths and time limits.
% 'chanconsist' - if EEG contains several datasets, check whether they have
% the same number of channels and channel labels.
% 'data' - check whether EEG contains data (EEG.data)
% 'loaddata' - load data array (if necessary)
% 'savedata' - save data array (if necessary - see EEG.saved below)
% 'contdata' - check whether EEG contains continuous data
% 'epoch' - check whether EEG contains epoched or continuous data
% 'ica' - check whether EEG contains an ICA decomposition
% 'besa' - check whether EEG contains component dipole locations
% 'event' - check whether EEG contains an event array
% 'makeur' - remake the EEG.urevent structure
% 'checkur' - check whether the EEG.urevent structure is consistent
% with the EEG.event structure
% 'chanlocsize' - check the EEG.chanlocs structure length; show warning if
% necessary.
% 'chanlocs_homogeneous' - check whether EEG contains consistent channel
% information; if not, correct it.This option
% calls eeg_checkchanlocs.
% 'eventconsistency' - check whether EEG.event information are consistent;
% rebuild event* subfields of the 'EEG.epoch' structure
% (can be time consuming).
% Outputs:
% EEGOUT - output EEGLAB dataset or dataset array
% changes - change code: 'no' = no changes; 'yes' = the EEG
% structure was modified
%
% ===========================================================
% The structure of an EEG dataset under EEGLAB (as of v5.03):
%
% Basic dataset information:
% EEG.setname - descriptive name|title for the dataset
% EEG.filename - filename of the dataset file on disk
% EEG.filepath - filepath (directory/folder) of the dataset file(s)
% EEG.trials - number of epochs (or trials) in the dataset.
% If data are continuous, this number is 1.
% EEG.pnts - number of time points (or data frames) per trial (epoch).
% If data are continuous (trials=1), the total number
% of time points (frames) in the dataset
% EEG.nbchan - number of channels
% EEG.srate - data sampling rate (in Hz)
% EEG.xmin - epoch start latency|time (in sec. relative to the
% time-locking event at time 0)
% EEG.xmax - epoch end latency|time (in seconds)
% EEG.times - vector of latencies|times in milliseconds (one per time point)
% EEG.ref - ['common'|'averef'|integer] reference channel type or number
% EEG.history - cell array of ascii pop-window commands that created
% or modified the dataset
% EEG.comments - comments about the nature of the dataset (edit this via
% menu selection Edit > About this dataset)
% EEG.etc - miscellaneous (technical or temporary) dataset information
% EEG.saved - ['yes'|'no'] 'no' flags need to save dataset changes before exit
%
% The data:
% EEG.data - two-dimensional continuous data array (chans, frames)
% ELSE, three-dim. epoched data array (chans, frames, epochs)
%
% The channel locations sub-structures:
% EEG.chanlocs - structure array containing names and locations
% of the channels on the scalp
% EEG.urchanlocs - original (ur) dataset chanlocs structure containing
% all channels originally collected with these data
% (before channel rejection)
% EEG.chaninfo - structure containing additional channel info
% EEG.ref - type of channel reference ('common'|'averef'|+/-int]
% EEG.splinefile - location of the spline file used by HEADPLOT to plot
% data scalp maps in 3-D
%
% The event and epoch sub-structures:
% EEG.event - event structure containing times and nature of experimental
% events recorded as occurring at data time points
% EEG.urevent - original (ur) event structure containing all experimental
% events recorded as occurring at the original data time points
% (before data rejection)
% EEG.epoch - epoch event information and epoch-associated data structure array (one per epoch)
% EEG.eventdescription - cell array of strings describing event fields.
% EEG.epochdescription - cell array of strings describing epoch fields.
% --> See the http://sccn.ucsd.edu/eeglab/maintut/eeglabscript.html for details
%
% ICA (or other linear) data components:
% EEG.icasphere - sphering array returned by linear (ICA) decomposition
% EEG.icaweights - unmixing weights array returned by linear (ICA) decomposition
% EEG.icawinv - inverse (ICA) weight matrix. Columns gives the projected
% topographies of the components to the electrodes.
% EEG.icaact - ICA activations matrix (components, frames, epochs)
% Note: [] here means that 'compute_ica' option has been set
% to 0 under 'File > Memory options' In this case,
% component activations are computed only as needed.
% EEG.icasplinefile - location of the spline file used by HEADPLOT to plot
% component scalp maps in 3-D
% EEG.chaninfo.icachansind - indices of channels used in the ICA decomposition
% EEG.dipfit - array of structures containing component map dipole models
%
% Variables indicating membership of the dataset in a studyset:
% EEG.subject - studyset subject code
% EEG.group - studyset group code
% EEG.condition - studyset experimental condition code
% EEG.run - studyset run number
% EEG.session - studyset session number
%
% Variables used for manual and semi-automatic data rejection:
% EEG.specdata - data spectrum for every single trial
% EEG.specica - data spectrum for every single trial
% EEG.stats - statistics used for data rejection
% EEG.stats.kurtc - component kurtosis values
% EEG.stats.kurtg - global kurtosis of components
% EEG.stats.kurta - kurtosis of accepted epochs
% EEG.stats.kurtr - kurtosis of rejected epochs
% EEG.stats.kurtd - kurtosis of spatial distribution
% EEG.reject - statistics used for data rejection
% EEG.reject.entropy - entropy of epochs
% EEG.reject.entropyc - entropy of components
% EEG.reject.threshold - rejection thresholds
% EEG.reject.icareject - epochs rejected by ICA criteria
% EEG.reject.gcompreject - rejected ICA components
% EEG.reject.sigreject - epochs rejected by single-channel criteria
% EEG.reject.elecreject - epochs rejected by raw data criteria
%
% Author: Arnaud Delorme, CNL / Salk Institute, 2001
%
% See also: EEGLAB
% Copyright (C) 2001 Arnaud Delorme, Salk Institute, 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.
% 01-25-02 reformated help & license -ad
% 01-26-02 chandeg events and trial condition format -ad
% 01-27-02 debug when trial condition is empty -ad
% 02-15-02 remove icawinv recompute for pop_epoch -ad & ja
% 02-16-02 remove last modification and test icawinv separately -ad
% 02-16-02 empty event and epoch check -ad
% 03-07-02 add the eeglab options -ad
% 03-07-02 corrected typos and rate/point calculation -ad & ja
% 03-15-02 add channel location reading & checking -ad
% 03-15-02 add checking of ICA and epochs with pop_up windows -ad
% 03-27-02 recorrected rate/point calculation -ad & sm
function [EEGFINAL, res] = eeg_checkset( EEG, varargin )
msg = '';
res = 'no';
com = sprintf('EEG = eeg_checkset( EEG );');
if nargin < 1
help eeg_checkset;
return;
end
EEGFINAL = EEG;
warning backtrace off
if isempty(EEG), return; end
if ~isfield(EEG, 'data'), return; end
% checking multiple datasets
% --------------------------
if length(EEG) > 1
if length(EEG) > 5000
disp('Too many datasets, aborting check')
return;
end
if nargin > 1
switch varargin{1}
case 'epochconsist' % test epoch consistency
% ----------------------
res = 'no';
datasettype = unique_bc( [ EEG.trials ] );
if datasettype(1) == 1 && length(datasettype) == 1, return; % continuous data
elseif datasettype(1) == 1, return; % continuous and epoch data
end
allpnts = unique_bc( [ EEG.pnts ] );
allxmin = unique_bc( [ EEG.xmin ] );
if length(allpnts) == 1 && length(allxmin) == 1, res = 'yes'; end
return;
case 'chanconsist' % test channel number and name consistency
% ----------------------------------------
res = 'yes';
chanlen = unique_bc( [ EEG.nbchan ] );
anyempty = unique_bc( cellfun( 'isempty', { EEG.chanlocs }) );
if length(chanlen) == 1 && all(anyempty == 0)
tmpchanlocs = EEG(1).chanlocs;
channame1 = { tmpchanlocs.labels };
for i = 2:length(EEG)
tmpchanlocs = EEG(i).chanlocs;
channame2 = { tmpchanlocs.labels };
if length(intersect(channame1, channame2)) ~= length(channame1), res = 'no'; end
end
else res = 'no';
end
% Field 'datachan in 'urchanlocs' is removed, if exist
if isfield(EEG, 'urchanlocs') && ~all(cellfun(@isempty,{EEG.urchanlocs})) && isfield([EEG.urchanlocs], 'datachan')
[EEG.urchanlocs] = deal(rmfield([EEG.urchanlocs], 'datachan'));
end
return;
case 'icaconsist' % test ICA decomposition consistency
% ----------------------------------
res = 'yes';
anyempty = unique_bc( cellfun( 'isempty', { EEG.icaweights }) );
if length(anyempty) == 1 && anyempty(1) == 0
ica1 = EEG(1).icawinv;
for i = 2:length(EEG)
if ~isequal(EEG(1).icawinv, EEG(i).icawinv)
res = 'no';
end
end
else res = 'no';
end
return;
end
end
end
% reading these option take time because
% of disk access
% --------------
eeglab_options;
% rename fields for backward compatibility in future versions
if isfield(EEG, 'nchans')
for iEEG = 1:length(EEG)
EEG(iEEG).nbchan = EEG.nchans;
end
end
if isfield(EEG, 'chanlocs')
for iEEG = 1:length(EEG)
if isfield(EEG(iEEG).chanlocs, 'label')
for iChan = 1:length(EEG(iEEG).chanlocs)
EEG(iEEG).chanlocs(iChan).labels = EEG(iEEG).chanlocs(iChan).label;
end
EEG(iEEG).chanlocs = rmfield(EEG(iEEG).chanlocs, 'label');
end
end
end
% standard checking
% -----------------
ALLEEG = EEG;
for inddataset = 1:length(ALLEEG)
EEG = ALLEEG(inddataset);
% additional checks
% -----------------
res = -1; % error code
if ~isempty( varargin)
for index = 1:length( varargin )
switch varargin{ index }
case 'data',; % already done at the top
case 'contdata',
if EEG.trials > 1
errordlg2(strvcat('Error: function only works on continuous data'), 'Error');
return;
end
case 'ica',
if isempty(EEG.icaweights)
errordlg2(strvcat('Error: no ICA decomposition. use menu "Tools > Run ICA" first.'), 'Error');
return;
end
case 'epoch',
if EEG.trials == 1
errordlg2(strvcat('Extract epochs before running that function', 'Use Tools > Extract epochs'), 'Error');
return
end
case 'besa',
if ~isfield(EEG, 'sources')
errordlg2(strvcat('No dipole information', '1) Export component maps: Tools > Localize ... BESA > Export ...' ...
, '2) Run BESA to localize the equivalent dipoles', ...
'3) Import the BESA dipoles: Tools > Localize ... BESA > Import ...'), 'Error');
return
end
case 'event',
if isempty(EEG.event)
errordlg2(strvcat('Requires events. You need to add events first.', ...
'Use "File > Import event info" or "File > Import epoch info"', ...
'Install plugin VidEd to manually add events as you scroll the data.' ), 'Error');
return;
end
case 'chanloc',
tmplocs = EEG.chanlocs;
if isempty(tmplocs) || ~isfield(tmplocs, 'theta') || all(cellfun('isempty', { tmplocs.theta }))
errordlg2( strvcat('This functionality requires channel location information.', ...
'Enter the channel file name via "Edit > Edit dataset info".', ...
'For channel file format, see ''>> help readlocs'' from the command line.'), 'Error');
return;
end
case 'chanlocs_homogeneous',
tmplocs = EEG.chanlocs;
if isempty(tmplocs) || ~isfield(tmplocs, 'theta') || all(cellfun('isempty', { tmplocs.theta }))
errordlg2( strvcat('This functionality requires channel location information.', ...
'Enter the channel file name via "Edit > Edit dataset info".', ...
'For channel file format, see ''>> help readlocs'' from the command line.'), 'Error');
return;
end
if ~isfield(EEG.chanlocs, 'X') || isempty(EEG.chanlocs(1).X)
EEG = eeg_checkchanlocs(EEG);
% EEG.chanlocs = convertlocs(EEG.chanlocs, 'topo2all');
res = ['EEG = eeg_checkset(EEG, ''chanlocs_homogeneous''); ' ];
end
case 'chanlocsize',
if ~isempty(EEG.chanlocs)
if length(EEG.chanlocs) > EEG.nbchan
questdlg2(strvcat('Warning: there is one more electrode location than', ...
'data channels. EEGLAB will consider the last electrode to be the', ...
'common reference channel. If this is not the case, remove the', ...
'extra channel'), 'Warning', 'Ok', 'Ok');
end
end
case 'makeur',
if ~isempty(EEG.event)
if isfield(EEG.event, 'urevent'),
EEG.event = rmfield(EEG.event, 'urevent');
disp('eeg_checkset note: re-creating the original event table (EEG.urevent)');
else
disp('eeg_checkset note: creating the original event table (EEG.urevent)');
end
EEG.urevent = EEG.event;
for index = 1:length(EEG.event)
EEG.event(index).urevent = index;
end
end
case 'checkur',
if ~isempty(EEG.event)
if isfield(EEG.event, 'urevent') && ~isempty(EEG.urevent)
urlatencies = [ EEG.urevent.latency ];
[newlat tmpind] = sort(urlatencies);
if ~isequal(newlat, urlatencies)
EEG.urevent = EEG.urevent(tmpind);
[tmp tmpind2] = sort(tmpind);
for index = 1:length(EEG.event)
EEG.event(index).urevent = tmpind2(EEG.event(index).urevent);
end
end
end
end
case 'eventconsistency',
%[EEG res] = eeg_checkset(EEG);
if ~isempty(EEG.event)
% check events (slow)
% ------------
if isfield(EEG.event, 'type')
eventInds = arrayfun(@(x)isempty(x.type), EEG.event);
if any(eventInds)
eventInds = find(eventInds);
eventInds = eventInds(:)'; % make row vector
if all(arrayfun(@(x)isnumeric(x.type), EEG.event))
for ind = eventInds, EEG.event(ind).type = NaN; end
else for ind = eventInds, EEG.event(ind).type = 'empty'; end
end
end
if ~all(arrayfun(@(x)ischar(x.type), EEG.event)) && ~all(arrayfun(@(x)isnumeric(x.type), EEG.event))
disp('Warning: converting all event types to strings');
for ind = 1:length(EEG.event)
EEG.event(ind).type = num2str(EEG.event(ind).type);
end
EEG = eeg_checkset(EEG, 'eventconsistency');
end
end
% Removing events with NaN latency
% --------------------------------
if isfield(EEG.event, 'latency')
nanindex = find(isnan([ EEG.event.latency ]));
if ~isempty(nanindex)
EEG.event(nanindex) = [];
trialtext = '';
for inan = 1:length(nanindex)
trialstext = [trialtext ' ' num2str(nanindex(inan))];
end
disp(sprintf(['eeg_checkset: Event(s) with NaN latency were deleted \nDeleted event index(es):[' trialstext ']']));
end
end
% remove the events which latency are out of boundary
% ---------------------------------------------------
if isempty(EEG.event), return; end
if isfield(EEG.event, 'latency')
if isfield(EEG.event, 'type')
if eeg_isboundary(EEG.event(1)) && isfield(EEG.event, 'duration')
if EEG.event(1).duration < 1
EEG.event(1) = [];
elseif EEG.event(1).latency > 0 && EEG.event(1).latency < 1
EEG.event(1).latency = 0.5;
end
end
end
try
tmpevent = EEG.event;
alllatencies = [tmpevent.latency];
catch
error('Checkset: error empty latency entry for new events added by user');
end
I1 = find(alllatencies < 0.5);
I2 = find(alllatencies > EEG.pnts*EEG.trials+1); % The addition of 1 was included
% because, if data epochs are extracted from -1 to
% time 0, this allow to include the last event in
% the last epoch (otherwise all epochs have an
% event except the last one
if (length(I1) + length(I2)) > 0
fprintf('eeg_checkset warning: %d/%d events had out-of-bounds latencies and were removed\n', ...
length(I1) + length(I2), length(EEG.event));
EEG.event(union(I1, I2)) = [];
end
end
if isempty(EEG.event), return; end
% save information for non latency fields updates
% -----------------------------------------------
difffield = [];
if ~isempty(EEG.event) && isfield(EEG.event, 'epoch')
% remove fields with empty epochs
% -------------------------------
removeevent = [];
try
tmpevent = EEG.event;
allepochs = [ tmpevent.epoch ];
removeevent = find( allepochs < 1 | allepochs > EEG.trials);
if ~isempty(removeevent)
disp([ 'eeg_checkset warning: ' int2str(length(removeevent)) ' event had invalid epoch numbers and were removed']);
end
catch
for indexevent = 1:length(EEG.event)
if isempty( EEG.event(indexevent).epoch ) || ~isnumeric(EEG.event(indexevent).epoch) ...
|| EEG.event(indexevent).epoch < 1 || EEG.event(indexevent).epoch > EEG.trials
removeevent = [removeevent indexevent];
disp([ 'eeg_checkset warning: event ' int2str(indexevent) ' has an invalid epoch number: removed']);
end
end
end
EEG.event(removeevent) = [];
end
if isempty(EEG.event), return; end
% Duration set to 0 if empty
% --------------------------
if isfield(EEG.event, 'duration')
emptyDur = cellfun(@isempty, { EEG.event.duration });
if any(emptyDur)
for indexevent = find(emptyDur)
EEG.event(indexevent).duration = 0;
end
end
end
% uniformize fields (str or int) if necessary
% -------------------------------------------
fnames = fieldnames(EEG.event);
for fidx = 1:length(fnames)
fname = fnames{fidx};
if ~strcmpi(fname, 'mffkeys') && ~strcmpi(fname, 'mffkeysbackup')
tmpevent = EEG.event;
allvalues = { tmpevent.(fname) };
try
% find indices of numeric values among values of this event property
valreal = ~cellfun('isclass', allvalues, 'char');
catch
valreal = mycellfun('isclass', allvalues, 'double');
end
format = 'ok';
if ~all(valreal) % all valreal ok
format = 'str';
if all(valreal == 0) % all valreal=0 ok
format = 'ok';
end
end
if strcmp(format, 'str')
fprintf('eeg_checkset note: event field format ''%s'' made uniform\n', fname);
allvalues = cellfun(@num2str, allvalues, 'uniformoutput', false);
[EEG.event(valreal).(fname)] = deal(allvalues{find(valreal)});
end
end
end
% check boundary events
% ---------------------
tmpevent = EEG.event;
if isfield(tmpevent, 'type') && ~isnumeric(tmpevent(1).type)
boundsInd = eeg_findboundaries(EEG);
if ~isempty(boundsInd)
bounds = [ tmpevent(boundsInd).latency ];
% remove last event if necessary
if EEG.trials==1 %this if block added by James Desjardins (Jan 13th, 2014)
if round(bounds(end)-0.5) > size(EEG.data,2), EEG.event(boundsInd(end)) = []; bounds(end) = []; end; % remove final boundary if any
end
% The first boundary below need to be kept for
% urevent latency calculation
% if bounds(1) < 0, EEG.event(bounds(1)) = []; end; % remove initial boundary if any
indDoublet = find(bounds(2:end)-bounds(1:end-1)==0);
if ~isempty(indDoublet)
disp('Warning: duplicate boundary event removed');
if isfield(EEG.event, 'duration')
for indBound = 1:length(indDoublet)
EEG.event(boundsInd(indDoublet(indBound)+1)).duration = EEG.event(boundsInd(indDoublet(indBound)+1)).duration+EEG.event(boundsInd(indDoublet(indBound))).duration;
end
end
EEG.event(boundsInd(indDoublet)) = [];
end
end
end
if isempty(EEG.event), return; end
% check that numeric format is double (Matlab 7)
% -----------------------------------
allfields = fieldnames(EEG.event);
if ~isempty(EEG.event)
for index = 1:length(allfields)
tmpval = EEG.event(1).(allfields{index});
if isnumeric(tmpval) && ~isa(tmpval, 'double')
for indexevent = 1:length(EEG.event)
tmpval = getfield(EEG.event, { indexevent }, allfields{index} );
EEG.event = setfield(EEG.event, { indexevent }, allfields{index}, double(tmpval));
end
end
end
end
% check duration field, replace empty by 0
% ----------------------------------------
if isfield(EEG.event, 'duration')
tmpevent = EEG.event;
try
valempt = cellfun('isempty' , { tmpevent.duration });
catch
valempt = mycellfun('isempty', { tmpevent.duration });
end
if any(valempt)
for index = find(valempt)
EEG.event(index).duration = 0;
end
end
end
% resort events
% -------------
if isfield(EEG.event, 'latency')
try
if isfield(EEG.event, 'epoch')
TMPEEG = pop_editeventvals(EEG, 'sort', { 'epoch' 0 'latency' 0 });
else
TMPEEG = pop_editeventvals(EEG, 'sort', { 'latency' 0 });
end
if ~isequal(TMPEEG.event, EEG.event)
EEG = TMPEEG;
disp('eeg_checkset note: events'' order (re)sorted by time');
end
catch
disp('eeg_checkset: problem when attempting to resort event latencies.');
end
end
% check latency of first event
% ----------------------------
if ~isempty(EEG.event)
if isfield(EEG.event, 'latency')
if EEG.event(1).latency < 0.5
EEG.event(1).latency = 0.5;
end
end
end
% build epoch structure
% ---------------------
try,
if EEG.trials > 1 && ~isempty(EEG.event)
% erase existing event-related fields
% ------------------------------
if ~isfield(EEG,'epoch')
EEG.epoch = [];
end
if ~isempty(EEG.epoch)
if length(EEG.epoch) ~= EEG.trials
disp('Warning: number of epoch entries does not match number of dataset trials;');
disp(' user-defined epoch entries will be erased.');
EEG.epoch = [];
else
fn = fieldnames(EEG.epoch);
EEG.epoch = rmfield(EEG.epoch,fn(strncmp('event',fn,5)));
end
end
% set event field
% ---------------
tmpevent = EEG.event;
eventepoch = [tmpevent.epoch];
epochevent = cell(1,EEG.trials);
destdata = epochevent;
EEG.epoch(length(epochevent)).event = [];
for k=1:length(epochevent)
epochevent{k} = find(eventepoch==k);
end
tmpepoch = EEG.epoch;
[tmpepoch.event] = epochevent{:};
EEG.epoch = tmpepoch;
maxlen = max(cellfun(@length,epochevent));
% copy event information into the epoch array
% -------------------------------------------
eventfields = fieldnames(EEG.event)';
eventfields = eventfields(~strcmp(eventfields,'epoch'));
tmpevent = EEG.event;
for k = 1:length(eventfields)
fname = eventfields{k};
switch fname
case 'latency'
sourcedata = round(eeg_point2lat([tmpevent.(fname)],[tmpevent.epoch],EEG.srate, [EEG.xmin EEG.xmax]*1000, 1E-3) * 10^8 )/10^8;
sourcedata = num2cell(sourcedata);
case 'duration'
sourcedata = num2cell([tmpevent.(fname)]/EEG.srate*1000);
otherwise
sourcedata = {tmpevent.(fname)};
end
if maxlen == 1
destdata = cell(1,length(epochevent));
destdata(~cellfun('isempty',epochevent)) = sourcedata([epochevent{:}]);
else
for l=1:length(epochevent)
destdata{l} = sourcedata(epochevent{l});
end
end
tmpepoch = EEG.epoch;
[tmpepoch.(['event' fname])] = destdata{:};
EEG.epoch = tmpepoch;
end
end
catch
errordlg2(['Warning: minor problem encountered when generating' 10 ...
'the EEG.epoch structure (used only in user scripts)']); return;
end
end
case { 'loaddata' 'savedata' 'chanconsist' 'icaconsist' 'epochconsist' }, res = '';
otherwise, error('eeg_checkset: unknown option');
end
end
end
res = [];
% check name consistency
% ----------------------
if ~isempty(EEG.setname)
if ~ischar(EEG.setname)
EEG.setname = '';
else
if size(EEG.setname,1) > 1
disp('eeg_checkset warning: invalid dataset name, removed');
EEG.setname = '';
end
end
else
EEG.setname = '';
end
% checking history and convert if necessary
% -----------------------------------------
if isfield(EEG, 'history') && size(EEG.history,1) > 1
allcoms = cellstr(EEG.history);
EEG.history = deblank(allcoms{1});
for index = 2:length(allcoms)
EEG.history = [ EEG.history 10 deblank(allcoms{index}) ];
end
end
% read data if necessary
% ----------------------
if ischar(EEG.data) && nargin > 1
if strcmpi(varargin{1}, 'loaddata')
EEG.data = eeg_getdatact(EEG);
end
end
% save data if necessary
% ----------------------
if nargin > 1
% datfile available?
% ------------------
datfile = 0;
if isfield(EEG, 'datfile')
if ~isempty(EEG.datfile)
datfile = 1;
end
end
% save data
% ---------
if strcmpi(varargin{1}, 'savedata') && option_storedisk
error('eeg_checkset: cannot call savedata any more');
% the code below is deprecated
if ~ischar(EEG.data) % not already saved
disp('Writing previous dataset to disk...');
if datfile
tmpdata = reshape(EEG.data, EEG.nbchan, EEG.pnts*EEG.trials);
floatwrite( tmpdata', fullfile(EEG.filepath, EEG.datfile), 'ieee-le');
EEG.data = EEG.datfile;
end
EEG.icaact = [];
% saving dataset
% --------------
filename = fullfile(EEG(1).filepath, EEG(1).filename);
if ~ischar(EEG.data) && option_single, EEG.data = single(EEG.data); end
v = version;
if str2num(v(1)) >= 7, save( filename, '-v6', '-mat', 'EEG'); % Matlab 7
else save( filename, '-mat', 'EEG');
end
if ~ischar(EEG.data), EEG.data = 'in set file'; end
% res = sprintf('%s = eeg_checkset( %s, ''savedata'');', inputname(1), inputname(1));
res = ['EEG = eeg_checkset( EEG, ''savedata'');'];
end
end
end
% numerical format
% ----------------
if isnumeric(EEG.data)
v = version;
EEG.icawinv = double(EEG.icawinv); % required for dipole fitting, otherwise it crashes
EEG.icaweights = double(EEG.icaweights);
EEG.icasphere = double(EEG.icasphere);
if ~isempty(findstr(v, 'R11')) || ~isempty(findstr(v, 'R12')) || ~isempty(findstr(v, 'R13'))
EEG.data = double(EEG.data);
EEG.icaact = double(EEG.icaact);
else
try,
if isa(EEG.data, 'double') && option_single
EEG.data = single(EEG.data);
EEG.icaact = single(EEG.icaact);
end
catch,
disp('WARNING: EEGLAB ran out of memory while converting dataset to single precision.');
disp(' Save dataset (preferably saving data to a separate file; see File > Memory options).');
disp(' Then reload it.');
end
end
end
% verify the type of the variables
% --------------------------------
% data dimensions -------------------------
if isnumeric(EEG.data) && ~isempty(EEG.data)
if ~isequal(size(EEG.data,1), EEG.nbchan)
disp( [ 'eeg_checkset warning: number of rows in data (' int2str(size(EEG.data,1)) ...
') does not match the number of channels (' int2str(EEG.nbchan) '): corrected' ]);
res = com;
EEG.nbchan = size(EEG.data,1);
end
if (ndims(EEG.data)) < 3 && (EEG.pnts > 1)
if mod(size(EEG.data,2), EEG.pnts) ~= 0
fprintf(2, 'eeg_checkset error: binary data file likely truncated, importing anyway...\n');
if EEG.trials > 1
EEG.trials = floor(size(EEG.data,2)/EEG.pnts);
EEG.data(:,EEG.trials*EEG.pnts+1:end) = [];
res = com;
else
EEG.pnts = size(EEG.data,2);
res = com;
end
else
if EEG.trials > 1
disp( 'eeg_checkset note: data array made 3-D');
res = com;
end
if size(EEG.data,2) ~= EEG.pnts
EEG.data = reshape(EEG.data, EEG.nbchan, EEG.pnts, size(EEG.data,2)/EEG.pnts);
end
end
end
% size of data -----------
if size(EEG.data,3) ~= EEG.trials
disp( ['eeg_checkset warning: 3rd dimension size of data (' int2str(size(EEG.data,3)) ...
') does not match the number of epochs (' int2str(EEG.trials) '), corrected' ]);
res = com;
EEG.trials = size(EEG.data,3);
end
if size(EEG.data,2) ~= EEG.pnts
disp( [ 'eeg_checkset warning: number of columns in data (' int2str(size(EEG.data,2)) ...
') does not match the number of points (' int2str(EEG.pnts) '): corrected' ]);
res = com;
EEG.pnts = size(EEG.data,2);
end
end
% xmin must be 0 for continuous data or
% pop_select does not behave correctly when
% removing data points
% --------------------
if EEG.trials == 1 && EEG.xmin ~= 0
EEG.xmin = 0;
fprintf( 'eeg_checkset note: xmin set to 0 for continuous data\n');
res = com;
end
% parameters consistency
% -------------------------
if round(EEG.srate*(EEG.xmax-EEG.xmin)+1) ~= EEG.pnts
fprintf( 'eeg_checkset note: upper time limit (xmax) adjusted so (xmax-xmin)*srate+1 = number of frames\n');
if EEG.srate == 0
EEG.srate = 1;
end
EEG.xmax = (EEG.pnts-1)/EEG.srate+EEG.xmin;
res = com;
end
% deal with event arrays
% ----------------------
if ~isfield(EEG, 'event'), EEG.event = []; res = com; end
if ~isempty(EEG.event)
if EEG.trials > 1 && ~isfield(EEG.event, 'epoch')
if popask( [ 'eeg_checkset error: the event info structure does not contain an ''epoch'' field.' ...
'Should EEGLAB attempt to abort operation ?' 10 '(press Cancel to fix the problem from the commandline)'])
error('eeg_checkset error(): user abort');
%res = com;
%EEG.event = [];
%EEG = eeg_checkset(EEG);
%return;
else
res = com;
return;
%error('eeg_checkset error: no epoch field in event structure');
end
end
else
EEG.event = [];
end
if isfield(EEG, 'urevent') && ~isempty(EEG.urevent) && ~isfield(EEG.event, 'urevent') && ~isempty(EEG.event)
warning('Inconsistency between urevent (backup) and event structures, removing urevent structure');
EEG.urevent = [];
end
if isempty(EEG.event)
EEG.eventdescription = {};
end
if ~isfield(EEG, 'eventdescription') || ~iscell(EEG.eventdescription)
EEG.eventdescription = cell(1, length(fieldnames(EEG.event)));
res = com;
else
if ~isempty(EEG.event)
if length(EEG.eventdescription) > length( fieldnames(EEG.event))
EEG.eventdescription = EEG.eventdescription(1:length( fieldnames(EEG.event)));
elseif length(EEG.eventdescription) < length( fieldnames(EEG.event))
EEG.eventdescription(end+1:length( fieldnames(EEG.event))) = {''};
end
end
end
% create urevent if continuous data
% ---------------------------------
%if ~isempty(EEG.event) && ~isfield(EEG, 'urevent')
% EEG.urevent = EEG.event;
% disp('eeg_checkset note: creating the original event table (EEG.urevent)');
% for index = 1:length(EEG.event)
% EEG.event(index).urevent = index;
% end
%end
if isfield(EEG, 'urevent') && isfield(EEG.urevent, 'urevent')
EEG.urevent = rmfield(EEG.urevent, 'urevent');
end
% deal with epoch arrays
% ----------------------
if ~isfield(EEG, 'epoch'), EEG.epoch = []; res = com; end
% check if only one epoch
% -----------------------
if EEG.trials == 1
if isfield(EEG.event, 'epoch')
EEG.event = rmfield(EEG.event, 'epoch'); res = com;
end
if ~isempty(EEG.epoch)
EEG.epoch = []; res = com;
end
end
if ~isfield(EEG, 'epochdescription'), EEG.epochdescription = {}; res = com; end
if ~isempty(EEG.epoch)
if isstruct(EEG.epoch), l = length( EEG.epoch);
else l = size( EEG.epoch, 2);
end
if l ~= EEG.trials
if popask( [ 'eeg_checkset error: the number of epoch indices in the epoch array/struct (' ...
int2str(l) ') is different from the number of epochs in the data (' int2str(EEG.trials) ').' 10 ...
'Should EEGLAB attempt to abort operation ?' 10 '(press Cancel to fix the problem from the commandline)'])
error('eeg_checkset error: user abort');
%res = com;
%EEG.epoch = [];
%EEG = eeg_checkset(EEG);
%return;
else
res = com;
return;
%error('eeg_checkset error: epoch structure size invalid');
end
end
else
EEG.epoch = [];
end
% check ica
% ---------
if ~isfield(EEG, 'icachansind')
if isempty(EEG.icaweights)
EEG.icachansind = []; res = com;
else
EEG.icachansind = [1:EEG.nbchan]; res = com;
end
elseif isempty(EEG.icachansind)
if ~isempty(EEG.icaweights)
EEG.icachansind = [1:EEG.nbchan]; res = com;
end
end
if ~isempty(EEG.icasphere)
if ~isempty(EEG.icaweights)
if size(EEG.icaweights,2) ~= size(EEG.icasphere,1)
if popask( [ 'eeg_checkset error: number of columns in weights array (' int2str(size(EEG.icaweights,2)) ')' 10 ...
'does not match the number of rows in the sphere array (' int2str(size(EEG.icasphere,1)) ')' 10 ...
'Should EEGLAB remove ICA information ?' 10 '(press Cancel to fix the problem from the commandline)'])
res = com;
EEG.icasphere = [];
EEG.icaweights = [];
EEG = eeg_checkset(EEG);
return;
else
error('eeg_checkset error: user abort');
res = com;
return;
%error('eeg_checkset error: invalid weight and sphere array sizes');
end
end
if isnumeric(EEG.data)
if length(EEG.icachansind) ~= size(EEG.icasphere,2)
if popask( [ 'eeg_checkset error: number of elements in ''icachansind'' (' int2str(length(EEG.icachansind)) ')' 10 ...
'does not match the number of columns in the sphere array (' int2str(size(EEG.icasphere,2)) ')' 10 ...
'Should EEGLAB remove ICA information ?' 10 '(press Cancel to fix the problem from the commandline)'])
res = com;
EEG.icasphere = [];
EEG.icaweights = [];
EEG = eeg_checkset(EEG);
return;
else
error('eeg_checkset error: user abort');
res = com;
return;
%error('eeg_checkset error: invalid weight and sphere array sizes');
end
end
if isempty(EEG.icaact) || (size(EEG.icaact,1) ~= size(EEG.icaweights,1)) || (size(EEG.icaact,2) ~= size(EEG.data,2))
EEG.icaweights = double(EEG.icaweights);
EEG.icawinv = double(EEG.icawinv);
% scale ICA components to RMS microvolt
if option_scaleicarms
if ~isempty(EEG.icawinv)
if mean(mean(abs(pinv(EEG.icaweights * EEG.icasphere)-EEG.icawinv))) < 0.0001
disp('Scaling components to RMS microvolt');
scaling = repmat(sqrt(mean(EEG(1).icawinv(:,:).^2))', [1 size(EEG.icaweights,2)]);
EEG.etc.icaweights_beforerms = EEG.icaweights;
EEG.etc.icasphere_beforerms = EEG.icasphere;
EEG.icaweights = EEG.icaweights .* scaling;
EEG.icawinv = pinv(EEG.icaweights * EEG.icasphere);
end
end
end
if ~isempty(EEG.data) && option_computeica
fprintf('eeg_checkset: recomputing the ICA activation matrix ...\n');
res = com;
% Make compatible with Matlab 7
if any(isnan(EEG.data(:)))
tmpdata = EEG.data(EEG.icachansind,:);
fprintf('eeg_checkset: recomputing ICA ignoring NaN indices ...\n');
tmpindices = find(~sum(isnan(tmpdata))); % was: tmpindices = find(~isnan(EEG.data(1,:)));
EEG.icaact = zeros(size(EEG.icaweights,1), size(tmpdata,2)); EEG.icaact(:) = NaN;
EEG.icaact(:,tmpindices) = (EEG.icaweights*EEG.icasphere)*tmpdata(:,tmpindices);
else
EEG.icaact = (EEG.icaweights*EEG.icasphere)*EEG.data(EEG.icachansind,:); % automatically does single or double
end
EEG.icaact = reshape( EEG.icaact, size(EEG.icaact,1), EEG.pnts, EEG.trials);
end
end
if ~isempty(EEG.icaact)
if EEG.trials*EEG.pnts < 1000
tmpact = (EEG.icaweights * EEG.icasphere) * EEG.data(EEG.icachansind,:);
else
% special MATLAB calculation kicks in above 1000
% (otherwise difference on the order of 0.0000001%)
tmpact = (EEG.icaweights * EEG.icasphere) * EEG.data(EEG.icachansind,1:1000);
end
if mean(abs(tmpact(:,1) - EEG.icaact(:,1))) > 0.000001
backtraceState = warning('backtrace');
warning('backtrace', 'off')
warning('ICA activities and weights mismatch, click on the link below for more information', 'verbose')
fprintf('<a href="https://eeglab.org/others/TIPS_and_FAQ.html#ica-activity-warning">https://eeglab.org/others/TIPS_and_FAQ.html#ica-activity-warning</a>\n')
if isstruct(backtraceState)
warning('backtrace', backtraceState.state);
end
end
end
end
if isempty(EEG.icawinv)
EEG.icawinv = pinv(EEG.icaweights*EEG.icasphere); % a priori same result as inv
res = com;
end
else
disp( [ 'eeg_checkset warning: weights matrix cannot be empty if sphere matrix is not, correcting ...' ]);
res = com;
EEG.icasphere = [];
end
if option_computeica
if ~isempty(EEG.icaact) && ndims(EEG.icaact) < 3 && (EEG.trials > 1)
disp( [ 'eeg_checkset note: independent component made 3-D' ]);
res = com;
EEG.icaact = reshape(EEG.icaact, size(EEG.icaact,1), EEG.pnts, EEG.trials);
end
else
if ~isempty(EEG.icaact)
fprintf('eeg_checkset: removing ICA activation matrix (as per edit options) ...\n');
end
EEG.icaact = [];
end
else
if ~isfield(EEG, 'icaweights') || ~isempty( EEG.icaweights ), EEG.icaweights = []; res = com; end
if ~isfield(EEG, 'icawinv') || ~isempty( EEG.icawinv ), EEG.icawinv = []; res = com; end
if ~isfield(EEG, 'icaact') || ~isempty( EEG.icaact ), EEG.icaact = []; res = com; end
end
if isempty(EEG.icaact)
EEG.icaact = [];
end
% -------------
% check chanlocs
% -------------
if ~isfield(EEG, 'chaninfo')
EEG.chaninfo = [];
end
if ~isempty( EEG.chanlocs )
% reference (use EEG structure)
% ---------
if ~isfield(EEG, 'ref')
EEG.ref = '';
end
if isequal(EEG.ref, 'averef')
EEG.ref = 'average';
res = com;
end
if isequal(EEG.ref, 'average')
ref = 'average';
else ref = '';
end
if ~isfield( EEG.chanlocs, 'ref')
EEG.chanlocs(1).ref = ref;
end
if isfield(EEG.chanlocs, 'ref') && ~isempty(EEG.chanlocs(1).ref)
if ~isequal(EEG.chanlocs(1).ref, EEG.ref)
EEG.ref = EEG.chanlocs(1).ref;
res = com;
end
end
charrefs = cellfun('isclass',{EEG.chanlocs.ref},'char');
if any(charrefs), ref = ''; end
for tmpind = find(~charrefs)
EEG.chanlocs(tmpind).ref = ref;
end
if ~isstruct( EEG.chanlocs)
if exist( EEG.chanlocs ) ~= 2
disp( [ 'eeg_checkset warning: channel file does not exist or is not in Matlab path: filename removed from EEG struct' ]);
EEG.chanlocs = [];
res = com;
else
res = com;
try, EEG.chanlocs = readlocs( EEG.chanlocs );
disp( [ 'eeg_checkset: channel file read' ]);
catch, EEG.chanlocs = []; end
end
else
if ~isfield(EEG.chanlocs,'labels')
disp('eeg_checkset warning: no field label in channel location structure, removing it');
EEG.chanlocs = [];
res = com;
end
end
if isstruct( EEG.chanlocs)
if length( EEG.chanlocs) ~= EEG.nbchan && length( EEG.chanlocs) ~= EEG.nbchan+1 && ~isempty(EEG.data)
disp( [ 'eeg_checkset warning: number of channels different in data and channel file/struct: channel file/struct removed' ]);
EEG.chanlocs = [];
res = com;
end
end
% general checking of channels
% ----------------------------
EEG = eeg_checkchanlocs(EEG);
if EEG.nbchan ~= length(EEG.chanlocs)
EEG.chanlocs = [];
EEG.chaninfo = [];
disp('Warning: the size of the channel location structure does not match with');
disp(' number of channels. Channel information have been removed.');
end
end
EEG.chaninfo.icachansind = EEG.icachansind; % just a copy for programming convenience
%if ~isfield(EEG, 'urchanlocs')
% EEG.urchanlocs = EEG.chanlocs;
% for index = 1:length(EEG.chanlocs)
% EEG.chanlocs(index).urchan = index;
% end
% disp('eeg_checkset note: creating backup chanlocs structure (urchanlocs)');
%end
% Field 'datachan in 'urchanlocs' is removed, if exist
if isfield(EEG, 'urchanlocs') && ~isempty(EEG.urchanlocs) && isfield(EEG.urchanlocs, 'datachan')
EEG.urchanlocs = rmfield(EEG.urchanlocs, 'datachan');
end
% check reference
% ---------------
if ~isfield(EEG, 'ref')
EEG.ref = 'common';
end
if ischar(EEG.ref) && strcmpi(EEG.ref, 'common')
if length(EEG.chanlocs) > EEG.nbchan
disp('Extra common reference electrode location detected');
EEG.ref = EEG.nbchan+1;
end
end
% DIPFIT structure
% ----------------
if isfield(EEG,'dipfit')
try
% check if dipfitdefs is present
dipfitdefs;
if isfield(EEG.dipfit, 'vol') && ~isfield(EEG.dipfit, 'hdmfile')
if exist('pop_dipfit_settings')
disp('Old DIPFIT structure detected: converting to DIPFIT 2 format');
EEG.dipfit.hdmfile = template_models(1).hdmfile;
EEG.dipfit.coordformat = template_models(1).coordformat;
EEG.dipfit.mrifile = template_models(1).mrifile;
EEG.dipfit.chanfile = template_models(1).chanfile;
EEG.dipfit.coord_transform = [];
EEG.saved = 'no';
res = com;
end
end
if isfield(EEG.dipfit, 'hdmfile')
if length(EEG.dipfit.hdmfile) > 8
if strcmpi(EEG.dipfit.hdmfile(end-8), template_models(1).hdmfile(end-8)), EEG.dipfit.hdmfile = template_models(1).hdmfile; end
if strcmpi(EEG.dipfit.hdmfile(end-8), template_models(2).hdmfile(end-8)), EEG.dipfit.hdmfile = template_models(2).hdmfile; end
end
if length(EEG.dipfit.mrifile) > 8
if strcmpi(EEG.dipfit.mrifile(end-8), template_models(1).mrifile(end-8)), EEG.dipfit.mrifile = template_models(1).mrifile; end
if strcmpi(EEG.dipfit.mrifile(end-8), template_models(2).mrifile(end-8)), EEG.dipfit.mrifile = template_models(2).mrifile; end
end
if length(EEG.dipfit.chanfile) > 8
if strcmpi(EEG.dipfit.chanfile(end-8), template_models(1).chanfile(end-8)), EEG.dipfit.chanfile = template_models(1).chanfile; end
if strcmpi(EEG.dipfit.chanfile(end-8), template_models(2).chanfile(end-8)), EEG.dipfit.chanfile = template_models(2).chanfile; end
end
end
if isfield(EEG.dipfit, 'coord_transform')
if isempty(EEG.dipfit.coord_transform)
EEG.dipfit.coord_transform = [0 0 0 0 0 0 1 1 1];
end
elseif ~isempty(EEG.dipfit)
EEG.dipfit.coord_transform = [0 0 0 0 0 0 1 1 1];
end
catch
e = lasterror;
if ~strcmp(e.identifier,'MATLAB:UndefinedFunction')
% if we got some error aside from dipfitdefs not being present, rethrow it
rethrow(e);
end
end
else
EEG.dipfit = []; res = com;
end
% check events (fast)
% ------------
if isfield(EEG.event, 'type')
tmpevent = EEG.event(1:min(length(EEG.event), 100));
if ~all(cellfun(@ischar, { tmpevent.type })) && ~all(cellfun(@isnumeric, { tmpevent.type }))
disp('Warning: converting all event types to strings');
for ind = 1:length(EEG.event)
EEG.event(ind).type = num2str(EEG.event(ind).type);
end
EEG = eeg_checkset(EEG, 'eventconsistency');
end
end
% EEG.times (only for epoched datasets)
% ---------
if ~isfield(EEG, 'times') || isempty(EEG.times) || length(EEG.times) ~= EEG.pnts
EEG.times = linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts);
EEG.saved = 'no';
end
if ~isfield(EEG, 'history'), EEG.history = ''; res = com; end
if ~isfield(EEG, 'splinefile'), EEG.splinefile = ''; res = com; end
if ~isfield(EEG, 'icasplinefile'), EEG.icasplinefile = ''; res = com; end
if ~isfield(EEG, 'saved'), EEG.saved = 'no'; res = com; end
if ~isfield(EEG, 'subject'), EEG.subject = ''; res = com; end
if ~isfield(EEG, 'condition'), EEG.condition = ''; res = com; end
if ~isfield(EEG, 'group'), EEG.group = ''; res = com; end
if ~isfield(EEG, 'run'), EEG.run = []; res = com; end
if ~isfield(EEG, 'session'), EEG.session = []; res = com; end
if ~isfield(EEG, 'urchanlocs'), EEG.urchanlocs = []; res = com; end
if ~isfield(EEG, 'specdata'), EEG.specdata = []; res = com; end
if ~isfield(EEG, 'specicaact'), EEG.specicaact = []; res = com; end
if ~isfield(EEG, 'comments'), EEG.comments = ''; res = com; end
if ~isfield(EEG, 'etc' ), EEG.etc = []; res = com; end
if ~isfield(EEG, 'urevent' ), EEG.urevent = []; res = com; end
if ~isfield(EEG, 'datfile' ), EEG.datfile = ''; res = com; end
if ~isfield(EEG, 'roi' ), EEG.roi = []; res = com; end
if ~isfield(EEG, 'ref') || isempty(EEG.ref), EEG.ref = 'common'; res = com; end
% create fields if absent
% -----------------------
if ~isfield(EEG, 'reject'), EEG.reject.rejjp = []; res = com; end
listf = { 'rejjp' 'rejkurt' 'rejmanual' 'rejthresh' 'rejconst', 'rejfreq' ...
'icarejjp' 'icarejkurt' 'icarejmanual' 'icarejthresh' 'icarejconst', 'icarejfreq'};
for index = 1:length(listf)
name = listf{index};
elecfield = [name 'E'];
if ~isfield(EEG.reject, elecfield), EEG.reject.(elecfield) = []; res = com; end
if ~isfield(EEG.reject, name)
EEG.reject.(name) = [];
res = com;
elseif ~isempty(EEG.reject.(name)) && isempty(EEG.reject.(elecfield))
% check if electrode array is empty with rejection array is not
nbchan = fastif(strcmp(name, 'ica'), size(EEG.icaweights,1), EEG.nbchan);
EEG.reject = setfield(EEG.reject, elecfield, zeros(nbchan, length(getfield(EEG.reject, name)))); res = com;
end
end
if ~isfield(EEG.reject, 'rejglobal'), EEG.reject.rejglobal = []; res = com; end
if ~isfield(EEG.reject, 'rejglobalE'), EEG.reject.rejglobalE = []; res = com; end
% track version of EEGLAB
% -----------------------
tmpvers = eeg_getversion;
if ~isfield(EEG.etc, 'eeglabvers') || ~isequal(EEG.etc.eeglabvers, tmpvers)
EEG.etc.eeglabvers = tmpvers;
EEG = eeg_hist( EEG, ['EEG.etc.eeglabvers = ''' tmpvers '''; % this tracks which version of EEGLAB is being used, you may ignore it'] );
res = com;
end
% default colors for rejection
% ----------------------------
if ~isfield(EEG.reject, 'rejmanualcol'), EEG.reject.rejmanualcol = [1.0000 1 0.783]; res = com; end
if ~isfield(EEG.reject, 'rejthreshcol'), EEG.reject.rejthreshcol = [0.8487 1.0000 0.5008]; res = com; end
if ~isfield(EEG.reject, 'rejconstcol'), EEG.reject.rejconstcol = [0.6940 1.0000 0.7008]; res = com; end
if ~isfield(EEG.reject, 'rejjpcol'), EEG.reject.rejjpcol = [1.0000 0.6991 0.7537]; res = com; end
if ~isfield(EEG.reject, 'rejkurtcol'), EEG.reject.rejkurtcol = [0.6880 0.7042 1.0000]; res = com; end
if ~isfield(EEG.reject, 'rejfreqcol'), EEG.reject.rejfreqcol = [0.9596 0.7193 1.0000]; res = com; end
if ~isfield(EEG.reject, 'disprej'), EEG.reject.disprej = { }; end
if ~isfield(EEG, 'stats'), EEG.stats.jp = []; res = com; end
if ~isfield(EEG.stats, 'jp'), EEG.stats.jp = []; res = com; end
if ~isfield(EEG.stats, 'jpE'), EEG.stats.jpE = []; res = com; end
if ~isfield(EEG.stats, 'icajp'), EEG.stats.icajp = []; res = com; end
if ~isfield(EEG.stats, 'icajpE'), EEG.stats.icajpE = []; res = com; end
if ~isfield(EEG.stats, 'kurt'), EEG.stats.kurt = []; res = com; end
if ~isfield(EEG.stats, 'kurtE'), EEG.stats.kurtE = []; res = com; end
if ~isfield(EEG.stats, 'icakurt'), EEG.stats.icakurt = []; res = com; end
if ~isfield(EEG.stats, 'icakurtE'), EEG.stats.icakurtE = []; res = com; end
% component rejection
% -------------------
if ~isfield(EEG.stats, 'compenta'), EEG.stats.compenta = []; res = com; end
if ~isfield(EEG.stats, 'compentr'), EEG.stats.compentr = []; res = com; end
if ~isfield(EEG.stats, 'compkurta'), EEG.stats.compkurta = []; res = com; end
if ~isfield(EEG.stats, 'compkurtr'), EEG.stats.compkurtr = []; res = com; end
if ~isfield(EEG.stats, 'compkurtdist'), EEG.stats.compkurtdist = []; res = com; end
if ~isfield(EEG.reject, 'threshold'), EEG.reject.threshold = [0.8 0.8 0.8]; res = com; end
if ~isfield(EEG.reject, 'threshentropy'), EEG.reject.threshentropy = 600; res = com; end
if ~isfield(EEG.reject, 'threshkurtact'), EEG.reject.threshkurtact = 600; res = com; end
if ~isfield(EEG.reject, 'threshkurtdist'), EEG.reject.threshkurtdist = 600; res = com; end
if ~isfield(EEG.reject, 'gcompreject'), EEG.reject.gcompreject = []; res = com; end
if length(EEG.reject.gcompreject) ~= size(EEG.icaweights,1)
EEG.reject.gcompreject = zeros(1, size(EEG.icaweights,1));
end
% remove old fields
% -----------------
if isfield(EEG, 'averef'), EEG = rmfield(EEG, 'averef'); end
if isfield(EEG, 'rt' ), EEG = rmfield(EEG, 'rt'); end
% store in new structure
% ----------------------
if isstruct(EEG)
if ~exist('ALLEEGNEW','var')
ALLEEGNEW = EEG;
else
ALLEEGNEW(inddataset) = EEG;
end
end
end
% recorder fields
% ---------------
fieldorder = { 'setname' ...
'filename' ...
'filepath' ...
'subject' ...
'group' ...
'condition' ...
'session' ...
'comments' ...
'nbchan' ...
'trials' ...
'pnts' ...
'srate' ...
'xmin' ...
'xmax' ...
'times' ...
'data' ...
'icaact' ...
'icawinv' ...
'icasphere' ...
'icaweights' ...
'icachansind' ...
'chanlocs' ...
'urchanlocs' ...
'chaninfo' ...
'ref' ...
'event' ...
'urevent' ...
'eventdescription' ...
'epoch' ...
'epochdescription' ...
'reject' ...
'stats' ...
'specdata' ...
'specicaact' ...
'splinefile' ...
'icasplinefile' ...
'dipfit' ...
'history' ...
'saved' ...
'etc' };
for fcell = fieldnames(EEG)'
fname = fcell{1};
if ~any(strcmp(fieldorder,fname))
fieldorder{end+1} = fname;
end
end
try
ALLEEGNEW = orderfields(ALLEEGNEW, fieldorder);
catch
disp('Couldn''t order data set fields properly.');
end
if exist('ALLEEGNEW','var')
EEGFINAL = ALLEEGNEW;
end
if ~isa(EEG, 'eegobj') && option_eegobject
EEGFINAL = eegobj(EEG);
end
return;
function num = popask( text )
ButtonName=questdlg2( text, ...
'Confirmation', 'Cancel', 'Yes','Yes');
switch lower(ButtonName),
case 'cancel', num = 0;
case 'yes', num = 1;
end
function res = mycellfun(com, vals, classtype);
res = zeros(1, length(vals));
switch com
case 'isempty',
for index = 1:length(vals), res(index) = isempty(vals{index}); end
case 'isclass'
if strcmp(classtype, 'double')
for index = 1:length(vals), res(index) = isnumeric(vals{index}); end
else
error('unknown cellfun command');
end
otherwise error('unknown cellfun command');
end
Datasets
Models
