% POP_SELECT - given an input EEG dataset structure, output a new EEG data structure
% retaining and/or excluding specified time/latency, data point, channel,
% and/or epoch range(s).
% Usage:
% >> OUTEEG = pop_select(INEEG, 'key1', value1, 'key2', value2 ...);
%
% Graphic interface:
% "Time range" - [edit box] RETAIN only the indicated epoch latency or continuous data
% time range: [low high] in ms, inclusive. For continuous data, several
% time ranges may be specified, separated by semicolons.
% Example: "5 10; 12 EEG.xmax" will retain the indicated
% stretches of continuous data, and remove data portions outside
% the indicated ranges, e.g. from 0 s to 5 s and from 10 s to 12 s.
% Command line equivalent: 'time' (or 'notime' - see below)
% "Time range" - [checkbox] EXCLUDE the indicated latency range(s) from the data.
% For epoched data, it is not possible to remove a range of latencies
% from the middle of the epoch, so either the low and/or the high values
% in the specified latency range (see above) must be at an epoch boundary
% (EEG.xmin, EEGxmax). Command line equivalent: [if checked] 'notime'
% "Point range" - [edit box] RETAIN the indicated data point range(s).
% Same options as for the "Time range" features (above).
% Command line equivalent: 'point' (or 'nopoint' - see below).
% "Point range" - [checkbox] EXCLUDE the indicated point range(s).
% Command line equivalent: [if checked] 'nopoint'
% "Epoch range" - [edit box] RETAIN the indicated data epoch indices in the dataset.
% This checkbox is only visible for epoched datasets.
% Command line equivalent: 'trial' (or 'notrial' - see below)
% "Epoch range" - [checkbox] EXCLUDE the specified data epochs.
% Command line equivalent: [if checked] 'notrial'
% "Channel range" - [edit box] RETAIN the indicated vector of data channels
% Command line equivalent: 'channel' (or 'nochannel' - see below)
% "Channel range" - [checkbox] EXCLUDE the indicated channels.
% Command line equivalent: [if checked] 'nochannel'
% "..." - [button] select channels by name.
% "Scroll dataset" - [button] call the EEGPLOT function to scroll the
% channel activities in a new window for visual inspection.
% Commandline equivalent: EEGPLOT - see its help for details.
% Inputs:
% INEEG - input EEG dataset structure
%
% Optional inputs
% 'time' - [min max] in seconds. Epoch latency or continuous data time range
% to retain in the new dataset, (Note: not ms, as in the GUI text entry
% above). For continuous data (only), several time ranges can be specified,
% separated by semicolons. Example: "5 10; 12 EEG.xmax" will retain
% the indicated times ranges, removing data outside the indicated ranges
% e.g. here from 0 to 5 s and from 10 s to 12 s. (See also, 'notime')
% 'rmtime' - [min max] in seconds. Epoch latency or continuous dataset time range
% to exclude from the new dataset. For continuous data, may be
% [min1 max1; min2 max2; ...] to exclude several time ranges. For epoched
% data, the latency range must include an epoch boundary, as latency
% ranges in the middle of epochs cannot be removed from epoched data.
% 'point' - [min max] epoch or continuous data point range to retain in the new
% dataset. For continuous datasets, this may be [min1 max1; min2 max2; ...]
% to retain several point ranges. (Notes: If both 'point'/'nopoint' and
% 'time' | 'notime' are specified, the 'point' limit values take precedence.
% The 'point' argument was originally a point vector, now deprecated).
% 'rmpoint' - [min max] epoch or continuous data point range to exclude in the new dataset.
% For epoched data, the point range must include either the first (0)
% or the last point (EEG.pnts), as a central point range cannot be removed.
% 'trial' - [integer array] array of trial indices to retain in the new dataset
% 'rmtrial' - [integer array] array of trial indices to exclude from the new dataset
% 'sorttrial' - ['on'|'off'] sort trial indices before extracting them (default: 'on').
% 'checkchans' - ['on'|'off'] check that channels are present before
% rejecting them (default: 'on')
% 'channel' - vector of channel indices to retain in the new
% dataset. Can also be a cell array of channel names.
% 'rmchannel' - vector of channel indices to exclude from the new
% dataset. Can also be a cell array of channel names.
% 'chantype' - [string|cell] list of channel types to keep
% 'rmchantype' - [string|cell] list of channel types to remove
%
% Outputs:
% OUTEEG - new EEG dataset structure
%
% Note: This function performs a conjunction (AND) of all its optional inputs.
% Using negative counterparts of all options, any logical combination is
% possible. Legacy input 'notrial', 'notime', 'nochannel', 'nopoint'
% are still supported.
%
% Author: Arnaud Delorme, CNL/Salk Institute, 2001; SCCN/INC/UCSD, 2002-
%
% 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 changed the format for events and trial conditions -ad
% 02-04-02 changed display format and allow for negation of inputs -ad
% 02-17-02 removed the event removal -ad
% 03-17-02 added channel info subsets selection -ad
% 03-21-02 added event latency recalculation -ad
function [EEG, com] = pop_select( EEG, varargin)
com = '';
if nargin < 1
help pop_select;
return;
end
if isempty(EEG(1).data)
disp('Pop_select error: cannot process empty dataset'); return;
end
if nargin < 2
geometry = { [1 1 1] [1 1 0.25 0.23 0.51] [1 1 0.25 0.23 0.51] [1 1 0.25 0.23 0.51] [1 1 0.25 0.23 0.51] ...
[1 1 0.25 0.23 0.51] [1] [1 1 1]};
enabletype = ~isempty(EEG(1).chanlocs) && isfield(EEG(1).chanlocs, 'type') && ~isempty(EEG(1).chanlocs(1).type);
if isequal(inputname(1), 'EEG')
enableScroll = 'on';
else
enableScroll = 'off';
end
uilist = { ...
{ 'Style', 'text', 'string', 'Select data in:', 'fontweight', 'bold' }, ...
{ 'Style', 'text', 'string', 'Input desired range', 'fontweight', 'bold' }, ...
{ 'Style', 'text', 'string', 'on->remove these', 'fontweight', 'bold' }, ...
{ 'Style', 'text', 'string', 'Time range [min max] (s)', 'fontangle', fastif(length(EEG)>1, 'italic', 'normal') }, ...
{ 'Style', 'edit', 'string', '', 'enable', 'on' }, ...
{ }, { 'Style', 'checkbox', 'string', ' ', 'enable', 'on' },{ }, ...
...
{ 'Style', 'text', 'string', 'Point range (ex: [1 10])', 'fontangle', fastif(length(EEG)>1, 'italic', 'normal') }, ...
{ 'Style', 'edit', 'string', '', 'enable', 'on' }, ...
{ }, { 'Style', 'checkbox', 'string', ' ', 'enable', 'on' },{ }, ...
...
{ 'Style', 'text', 'string', 'Epoch range (ex: 3:2:10)', 'fontangle', fastif(length(EEG)>1, 'italic', 'normal') }, ...
{ 'Style', 'edit', 'string', '', 'enable', 'on' }, ...
{ }, { 'Style', 'checkbox', 'string', ' ', 'enable', 'on' },{ }, ...
...
{ 'Style', 'text', 'string', 'Channel(s)' }, ...
{ 'Style', 'edit', 'string', '', 'tag', 'chans' }, ...
{ }, { 'Style', 'checkbox', 'string', ' ' }, ...
{ 'style' 'pushbutton' 'string' '...', 'enable' fastif(isempty(EEG(1).chanlocs), 'off', 'on') ...
'callback' 'pop_chansel(get(gcbf, ''userdata''), ''field'', ''labels'', ''handle'', findobj(''parent'', gcbf, ''tag'', ''chans''));' }, ...
...
{ 'Style', 'text', 'string', 'Channel type(s)' }, ...
{ 'Style', 'edit', 'string', '', 'tag', 'chantype' }, ...
{ }, { 'Style', 'checkbox', 'string', ' ' }, ...
{ 'style' 'pushbutton' 'string' '...', 'enable' fastif(enabletype, 'on', 'off') ...
'callback' 'pop_chansel(get(gcbf, ''userdata''), ''field'', ''type'', ''handle'', findobj(''parent'', gcbf, ''tag'', ''chantype''));' }, ...
...
{ }, { }, { 'Style', 'pushbutton', 'string', 'Scroll dataset', 'enable', fastif(length(EEG)>1, 'off', 'on'), 'enable', enableScroll, 'callback', ...
'eegplot(EEG.data, ''srate'', EEG.srate, ''winlength'', 5, ''limits'', [EEG.xmin EEG.xmax]*1000, ''position'', [100 300 800 500], ''xgrid'', ''off'', ''eloc_file'', EEG.chanlocs);' } {}};
% 'callback' 'tmplabels = get(gcbf, ''userdata''); [~, tmpvalchan] = pop_chansel(tmplabels, ''withindex'', ''on''); set(findobj(gcbf, ''tag'', ''chans''), ''string'',tmpvalchan); clear tmplabels tmpvalchan' }, ...
chanlocs = eeg_mergelocs(EEG.chanlocs);
results = inputgui( 'geometry', geometry, 'uilist', uilist, 'helpcom', 'pophelp(''pop_select'');', 'title', 'Select data -- pop_select()', 'userdata', chanlocs );
if isempty(results), return; end
% decode inputs
% -------------
args = {};
if ~isempty( results{1} )
if ~results{2}, args = { args{:}, 'time', eval( [ '[' results{1} ']' ] ) };
else args = { args{:}, 'rmtime', eval( [ '[' results{1} ']' ] ) }; end
end
if ~isempty( results{3} )
if ~results{4}, args = { args{:}, 'point', eval( [ '[' results{3} ']' ] ) };
else args = { args{:}, 'rmpoint', eval( [ '[' results{3} ']' ] ) }; end
end
if ~isempty( results{5} )
if ~results{6}, args = { args{:}, 'trial', eval( [ '[' results{5} ']' ] ) };
else args = { args{:}, 'rmtrial', eval( [ '[' results{5} ']' ] ) }; end
end
if ~isempty( results{7} )
[ chaninds, chanlist ] = eeg_decodechan(chanlocs, results{7});
if isempty(chanlist)
if length(EEG) > 1 && length(unique([EEG.nbchan])) > 1
error([ 'Cannot use channel indices when processing multiple datasets' 10 ...
'with some channels already removed' ])
end
chanlist = chaninds;
end
if ~results{8}, args = { args{:}, 'channel' , chanlist };
else args = { args{:}, 'rmchannel', chanlist }; end
end
if ~isempty( results{9} )
[ ~, chantypes ] = eeg_decodechan(chanlocs, results{9}, 'type');
if ~results{10}, args = { args{:}, 'chantype' , unique(chantypes) };
else args = { args{:}, 'rmchantype', unique(chantypes) }; end
end
else
args = varargin;
end
% process multiple datasets
% -------------------------
if length(EEG) > 1
if nargin < 2
[ EEG, com ] = eeg_eval( 'pop_select', EEG, 'warning', 'on', 'params', args);
else
[ EEG, com ] = eeg_eval( 'pop_select', EEG, 'warning', 'off', 'params',args);
end
return;
end
%----------------------------AMICA---------------------------------
if isfield(EEG.etc,'amica') && isfield(EEG.etc.amica,'prob_added')
for index = 1:2:length(args)
if strcmpi(args{index}, 'channel')
args{index+1} = [ args{index+1} EEG.nbchan-(0:2*EEG.etc.amica.num_models-1)];
end
end
end
%--------------------------------------------------------------------
g = finputcheck(args, { 'time' 'real' [] []; ...
'notime' 'real' [] []; ...
'rmtime' 'real' [] []; ...
'trial' 'integer' [] [1:EEG.trials]; ...
'notrial' 'integer' [] []; ...
'rmtrial' 'integer' [] []; ...
'point' 'integer' [] []; ...
'nopoint' 'integer' [] []; ...
'rmpoint' 'integer' [] []; ...
'channel' { 'integer','cell' } [] [];
'nochannel' { 'integer','cell' } [] [];
'rmchannel' { 'integer','cell' } [] [];
'chantype' { 'string','cell' } [] {};
'rmchantype' { 'string','cell' } [] {};
'trialcond' 'integer' [] []; ...
'notrialcond' 'integer' [] []; ...
'sort' 'integer' [] []; ...
'sorttrial' 'string' { 'on','off' } 'on' }, 'pop_select');
if ischar(g)
error(g);
end
if ~isempty(g.rmtrial)
g.notrial = g.rmtrial;
end
if ~isempty(g.rmtime)
g.notime = g.rmtime;
end
if ~isempty(g.rmpoint)
g.nopoint = g.rmpoint;
end
if ~isempty(g.rmchannel)
g.nochannel = g.rmchannel;
end
if ~isempty(g.sort)
if g.sort, g.sorttrial = 'on';
else g.sorttrial = 'off';
end
end
if strcmpi(g.sorttrial, 'on')
g.trial = sort(setdiff( g.trial, g.notrial ));
if isempty(g.trial), error('Error: dataset %s is empty',EEG.filename); end
else
g.trial(ismember(g.trial,g.notrial)) = [];
% still warn about & remove duplicate trials (may be removed in the future)
[p,q] = unique_bc(g.trial);
if length(p) ~= length(g.trial)
disp('Warning: trial selection contained duplicated elements, which were removed.');
end
g.trial = g.trial(sort(q));
end
% decode channels
% ---------------
if ~isempty(g.channel) || ~isempty(g.nochannel)
% find channels by name
if ~isempty(g.channel)
if ~isempty(g.chantype) || ~isempty(g.rmchantype)
error('You can select channels by name or by type but not both');
end
inds = eeg_decodechan(EEG, g.channel, 'labels', true);
chanFlag = zeros(1, EEG.nbchan);
chanFlag(inds) = 1;
else
chanFlag = ones(1, EEG.nbchan);
end
if ~isempty(g.nochannel)
if ~isempty(g.chantype) || ~isempty(g.rmchantype)
error('You can select channels by name or by type but not both');
end
inds = eeg_decodechan(EEG, g.nochannel, 'labels', true);
chanFlag(inds) = 0;
end
else
% find channels by type
if ~isempty(g.chantype)
inds = eeg_decodechan(EEG, g.chantype, 'type', true);
chanFlag = zeros(1, EEG.nbchan);
chanFlag(inds) = 1;
else
chanFlag = ones(1, EEG.nbchan);
end
if ~isempty(g.rmchantype)
inds = eeg_decodechan(EEG, g.rmchantype, 'type', true);
chanFlag(inds) = 0;
end
end
g.channel = find(chanFlag);
% time selection
% --------------
if ~isempty(g.time) && (g.time(1) < EEG.xmin*1000) && (g.time(2) > EEG.xmax*1000)
error('Wrong time range');
end
if min(g.trial) < 1 || max( g.trial ) > EEG.trials
error('Wrong trial range');
end
if size(g.point,2) > 2
g.point = [g.point(1) g.point(end)];
disp('Warning: vector format for point range is deprecated');
end
if size(g.nopoint,2) > 2
g.nopoint = [g.nopoint(1) g.nopoint(end)];
disp('Warning: vector format for point range is deprecated');
end
if ~isempty( g.point )
g.time = zeros(size(g.point));
for index = 1:length(g.point(:))
g.time(index) = eeg_point2lat(g.point(index), 1, EEG.srate, [EEG.xmin EEG.xmax]);
end
g.notime = [];
end
if ~isempty( g.nopoint )
g.notime = zeros(size(g.nopoint));
for index = 1:length(g.nopoint(:))
g.notime(index) = eeg_point2lat(g.nopoint(index), 1, EEG.srate, [EEG.xmin EEG.xmax]);
end
g.time = [];
end
if ~isempty( g.notime )
if size(g.notime,2) ~= 2
error('Time/point range must contain 2 columns exactly');
end
if g.notime(2) == EEG.xmax
g.time = [EEG.xmin g.notime(1)];
else
if g.notime(1) == EEG.xmin
g.time = [g.notime(2) EEG.xmax];
elseif EEG.trials > 1
error('Wrong notime range. Remember that it is not possible to remove a slice of time for data epochs.');
end
end
if g.notime(end) > EEG.xmax, g.notime(end) = EEG.xmax; end
if g.notime(1) < EEG.xmin, g.notime(1) = EEG.xmin; end
if floor(max(g.notime(:))) > EEG.xmax
error('Time/point range exceed upper data limits');
end
if min(g.notime(:)) < EEG.xmin
error('Time/point range exceed lower data limits');
end
end
if ~isempty(g.time)
if size(g.time,2) ~= 2
error('Time/point range must contain 2 columns exactly');
end
for index = 1:length(g.time)
if g.time(index) > EEG.xmax
g.time(index) = EEG.xmax;
disp('Upper time limits exceed data, corrected');
elseif g.time(index) < EEG.xmin
g.time(index) = EEG.xmin;
disp('Lower time limits exceed data, corrected');
end
end
end
% select trial values
%--------------------
if ~isempty(g.trialcond)
try
tt = struct( g.trialcond{:} ); catch
error('Trial conditions format error');
end
ttfields = fieldnames (tt);
for index = 1:length(ttfields)
if ~isfield( EEG.epoch, ttfields{index} )
error([ ttfields{index} 'is not a field of EEG.epoch' ]);
end
tmpepoch = EEG.epoch;
eval( [ 'Itriallow = find( [ tmpepoch(:).' ttfields{index} ' ] >= tt.' ttfields{index} '(1) );' ] );
eval( [ 'Itrialhigh = find( [ tmpepoch(:).' ttfields{index} ' ] <= tt.' ttfields{index} '(end) );' ] );
Itrialtmp = intersect_bc(Itriallow, Itrialhigh);
g.trial = intersect_bc( g.trial(:)', Itrialtmp(:)');
end
end
if isempty(g.trial)
error('Empty dataset, no trial');
end
if length(g.trial) ~= EEG.trials
fprintf('Removing %d trial(s)...\n', EEG.trials - length(g.trial));
end
if length(g.channel) ~= EEG.nbchan
fprintf('Removing %d channel(s)...\n', EEG.nbchan - length(g.channel));
end
try
% For AMICA probabilities...
%-----------------------------------------------------
if isfield(EEG.etc, 'amica') && ~isempty(EEG.etc.amica) && isfield(EEG.etc.amica, 'v_smooth') && ~isempty(EEG.etc.amica.v_smooth) && ~isfield(EEG.etc.amica,'prob_added')
if isfield(EEG.etc.amica, 'num_models') && ~isempty(EEG.etc.amica.num_models)
if size(EEG.data,2) == size(EEG.etc.amica.v_smooth,2) && size(EEG.data,3) == size(EEG.etc.amica.v_smooth,3) && size(EEG.etc.amica.v_smooth,1) == EEG.etc.amica.num_models
EEG = eeg_formatamica(EEG);
%-------------------------------------------
[EEG, com] = pop_select(EEG,args{:});
%-------------------------------------------
EEG = eeg_reformatamica(EEG);
EEG = eeg_checkamica(EEG);
return;
else
disp('AMICA probabilities not compatible with size of data, probabilities cannot be rejected')
disp('Resuming rejection...')
end
end
end
% ------------------------------------------------------
catch
warnmsg = strcat('your dataset contains amica information, but the amica plugin is not installed. Continuing and ignoring amica information.');
warning(warnmsg)
end
% recompute latency and epoch number for events
% ---------------------------------------------
if length(g.trial) ~= EEG.trials && ~isempty(EEG.event)
if ~isfield(EEG.event, 'epoch')
disp('Pop_epoch warning: bad event format with epoch dataset, removing events');
EEG.event = [];
else
if isfield(EEG.event, 'epoch')
keepevent = [];
for indexevent = 1:length(EEG.event)
newindex = find( EEG.event(indexevent).epoch == g.trial );% For AMICA probabilities...
%-----------------------------------------------------
try
if isfield(EEG.etc, 'amica') && ~isempty(EEG.etc.amica) && isfield(EEG.etc.amica, 'v_smooth') && ~isempty(EEG.etc.amica.v_smooth) && ~isfield(EEG.etc.amica,'prob_added')
if isfield(EEG.etc.amica, 'num_models') && ~isempty(EEG.etc.amica.num_models)
if size(EEG.data,2) == size(EEG.etc.amica.v_smooth,2) && size(EEG.data,3) == size(EEG.etc.amica.v_smooth,3) && size(EEG.etc.amica.v_smooth,1) == EEG.etc.amica.num_models
EEG = eeg_formatamica(EEG);
%-------------------------------------------
[EEG, com] = pop_select(EEG,args{:});
%-------------------------------------------
EEG = eeg_reformatamica(EEG);
EEG = eeg_checkamica(EEG);
return;
else
disp('AMICA probabilities not compatible with size of data, probabilities cannot be rejected')
disp('Resuming rejection...')
end
end
end
catch
warnmsg = strcat('your dataset contains amica information, but the amica plugin is not installed. Continuing and ignoring amica information.');
warning(warnmsg)
end;
% ------------------------------------------------------
if ~isempty(newindex)
keepevent = [keepevent indexevent];
if isfield(EEG.event, 'latency')
EEG.event(indexevent).latency = EEG.event(indexevent).latency - (EEG.event(indexevent).epoch-1)*EEG.pnts + (newindex-1)*EEG.pnts;
end
EEG.event(indexevent).epoch = newindex;
end
end
diffevent = setdiff_bc([1:length(EEG.event)], keepevent);
if ~isempty(diffevent)
disp(['Pop_select: removing ' int2str(length(diffevent)) ' unreferenced events']);
EEG.event(diffevent) = [];
end
end
end
end
% performing removal
% ------------------
if ~isempty(g.time) || ~isempty(g.notime)
if EEG.trials > 1
% select new time window
% ----------------------
try, tmpevent = EEG.event;
tmpeventlatency = [ tmpevent.latency ];
catch, tmpeventlatency = [];
end
alllatencies = 1-(EEG.xmin*EEG.srate); % time 0 point
alllatencies = linspace( alllatencies, EEG.pnts*(EEG.trials-1)+alllatencies, EEG.trials);
[EEG.data, tmptime, indices, epochevent]= epoch(EEG.data, alllatencies, ...
[g.time(1) g.time(2)]*EEG.srate, 'allevents', tmpeventlatency);
tmptime = tmptime/EEG.srate;
if g.time(1) ~= tmptime(1) && g.time(2)-1/EEG.srate ~= tmptime(2)
fprintf('pop_select(): time limits have been adjusted to [%3.3f %3.3f] to fit data points limits\n', tmptime(1), tmptime(2)+1/EEG.srate);
end
EEG.xmin = tmptime(1);
EEG.xmax = tmptime(2);
EEG.pnts = size(EEG.data,2);
alllatencies = alllatencies(indices);
% modify the event structure accordingly (latencies and add epoch field)
% ----------------------------------------------------------------------
allevents = [];
newevent = [];
count = 1;
if ~isempty(epochevent)
newevent = EEG.event(1);
for index=1:EEG.trials
for indexevent = epochevent{index}
newevent(count) = EEG.event(indexevent);
newevent(count).epoch = index;
newevent(count).latency = newevent(count).latency - alllatencies(index) - tmptime(1)*EEG.srate + 1 + EEG.pnts*(index-1);
count = count + 1;
end
end
end
EEG.event = newevent;
% erase event-related fields from the epochs
% ------------------------------------------
if ~isempty(EEG.epoch)
fn = fieldnames(EEG.epoch);
EEG.epoch = rmfield(EEG.epoch,{fn{strmatch('event',fn)}});
end
else
if isempty(g.notime)
if length(g.time) == 2 && EEG.xmin < 0
disp('Warning: negative minimum time; unchanged to ensure correct latency of initial boundary event');
end
g.notime = g.time';
g.notime = g.notime(:);
if g.notime(1) ~= 0, g.notime = [EEG.xmin g.notime(:)'];
else g.notime = [g.notime(2:end)'];
end
if g.time(end) == EEG.xmax, g.notime(end) = [];
else g.notime(end+1) = EEG.xmax;
end
for index = 1:length(g.notime)
if g.notime(index) ~= 0 && g.notime(index) ~= EEG.xmax
if mod(index,2), g.notime(index) = g.notime(index) + 1/EEG.srate;
else g.notime(index) = g.notime(index) - 1/EEG.srate;
end
end
end
g.notime = reshape(g.notime, 2, length(g.notime)/2)';
end
nbtimes = length(g.notime(:));
[points,flag] = eeg_lat2point(g.notime(:)', ones(1,nbtimes), EEG.srate, [EEG.xmin EEG.xmax]);
points = reshape(points, size(g.notime));
% fixing if last region is the same
if flag
if ~isempty(find((points(end,1)-points(end,2))== 0)), points(end,:) = []; end
end
EEG = eeg_eegrej(EEG, points);
end
end
% performing removal
% ------------------
if ~isequal(g.channel,1:size(EEG.data,1)) || ~isequal(g.trial,1:size(EEG.data,3))
eeglab_options;
if ~isequal(g.channel,1:size(EEG.data,1))
if ~isempty(EEG.dipfit)
warning('erasing dipole information since channels have being removed')
EEG.dipfit = [];
EEG.roi = [];
end
end
if option_memmapdata
% this code below is preferred for memory mapped files
diff1 = setdiff_bc([1:size(EEG.data,1)], g.channel);
diff2 = setdiff_bc([1:size(EEG.data,3)], g.trial);
if ~isempty(diff1)
EEG.data(diff1, :, :) = [];
end
if ~isempty(diff2)
EEG.data(:, :, diff2) = [];
end
else
EEG.data = EEG.data(g.channel, :, g.trial);
end
end
if ~isempty(EEG.icaact), EEG.icaact = EEG.icaact(:,:,g.trial); end
if ~isempty(EEG.chanlocs)
if ~isfield(EEG.chaninfo, 'removedchans')
EEG.chaninfo.removedchans = [];
end
try
diff1 = setdiff_bc([1:EEG.nbchan], g.channel);
fields = fieldnames(EEG.chanlocs);
for iChan = diff1(:)'
EEG.chaninfo.removedchans(end+1).(fields{1}) = EEG.chanlocs(iChan).(fields{1});
for iField = 1:length(fields)
EEG.chaninfo.removedchans(end).(fields{iField}) = EEG.chanlocs(iChan).(fields{iField});
end
end
catch
disp('There was an issue storing removed channels in pop_select');
end
EEG.chanlocs = EEG.chanlocs(g.channel);
end
EEG.trials = length(g.trial);
EEG.pnts = size(EEG.data,2);
EEG.nbchan = length(g.channel);
if ~isempty(EEG.epoch)
EEG.epoch = EEG.epoch( g.trial );
end
if ~isempty(EEG.specdata)
if length(g.point) == EEG.pnts
EEG.specdata = EEG.specdata(g.channel, :, g.trial);
else
EEG.specdata = [];
fprintf('Warning: spectral data were removed because of the change in the number of points\n');
end
end
% ica specific
% ------------
if ~isempty(EEG.icachansind)
rmchans = setdiff_bc( EEG.icachansind, g.channel ); % channels to remove
% channel sub-indices
% -------------------
icachans = 1:length(EEG.icachansind);
for index = length(rmchans):-1:1
chanind = find(EEG.icachansind == rmchans(index));
icachans(chanind) = [];
end
% new channels indices
% --------------------
count = 1;
newinds = [];
for index = 1:length(g.channel)
if any(EEG.icachansind == g.channel(index))
newinds(count) = index;
count = count+1;
end
end
EEG.icachansind = newinds;
else
icachans = 1:size(EEG.icasphere,2);
end
if ~isempty(EEG.icawinv)
flag_rmchan = (length(icachans) ~= size(EEG.icawinv,1));
if isempty(EEG.icaweights) || flag_rmchan
EEG.icawinv = EEG.icawinv(icachans,:);
EEG.icaweights = pinv(EEG.icawinv);
EEG.icasphere = eye(size(EEG.icaweights,2));
end
end
if ~isempty(EEG.specicaact)
if length(g.point) == EEG.pnts
EEG.specicaact = EEG.specicaact(icachans, :, g.trial);
else
EEG.specicaact = [];
fprintf('Warning: spectral ICA data were removed because of the change in the number of points\n');
end
end
% check if only one epoch
% -----------------------
if EEG.trials == 1
if isfield(EEG.event, 'epoch')
EEG.event = rmfield(EEG.event, 'epoch');
end
EEG.epoch = [];
end
if isfield(EEG.reject, 'gcompreject') && isequal(g.channel,1:size(EEG.data,1))
tmpgcompreject = EEG.reject.gcompreject;
EEG.reject = [];
EEG.reject.gcompreject = tmpgcompreject;
else
EEG.reject = [];
end
EEG.stats = [];
EEG.reject.rejmanual = [];
% for stats, can adapt remove the selected trials and electrodes
% in the future to gain time -----------------------------------
EEG.stats.jp = [];
EEG = eeg_checkset(EEG, 'eventconsistency');
% generate command
% ----------------
if nargout > 1
com = sprintf('EEG = pop_select( EEG, %s);', vararg2str(args));
end
return;
% ********* OLD, do not remove any event any more
% ********* in the future maybe do a pack event to remove events not in the time range of any epoch
if ~isempty(EEG.event)
% go to array format if necessary
if isstruct(EEG.event), format = 'struct';
else format = 'array';
end
switch format, case 'struct', EEG = eventsformat(EEG, 'array'); end
% keep only events related to the selected trials
Indexes = [];
Ievent = [];
for index = 1:length( g.trial )
currentevents = find( EEG.event(:,2) == g.trial(index));
Indexes = [ Indexes ones(1, length(currentevents))*index ];
Ievent = union_bc( Ievent, currentevents );
end
EEG.event = EEG.event( Ievent,: );
EEG.event(:,2) = Indexes(:);
switch format, case 'struct', EEG = eventsformat(EEG, 'struct'); end
end
Datasets
Models
