% POP_INTERP - interpolate data channels
%
% Usage: EEGOUT = pop_interp(EEG, badchans, method, t_range);
%
% Inputs:
% EEG - EEGLAB dataset
% badchans - [integer array] indices of channels to interpolate.
% For instance, these channels might be bad.
% [chanlocs structure] channel location structure containing
% either locations of channels to interpolate or a full
% channel structure (missing channels in the current
% dataset are interpolated).
% method - [string] method used for interpolation (default is 'spherical').
% 'invdist'/'v4' uses inverse distance on the scalp
% 'spherical' uses spherical interpolation.
% 'sphericalKang' uses Kang et al. 2015 spherical interpolation.
% (see EEG_INTERP)
% 'spacetime' uses griddata3 to interpolate both in space
% and time (very slow and cannot be interrupted).
% t_range - [integer array with just two elements] time interval of the
% badchans which should be interpolated. First element is
% the start time and the second element is the end time.
% Output:
% EEGOUT - data set with bad electrode data replaced by
% interpolated data
%
% Author: Arnaud Delorme, CERCO, CNRS, 2009-
%
% See also: EEG_INTERP
% Copyright (C) Arnaud Delorme, CERCO, 2009, arno@salk.edu
%
% This file is part of EEGLAB, see http://www.eeglab.org
% for the documentation and details.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% 1. Redistributions of source code must retain the above copyright notice,
% this list of conditions and the following disclaimer.
%
% 2. Redistributions in binary form must reproduce the above copyright notice,
% this list of conditions and the following disclaimer in the documentation
% and/or other materials provided with the distribution.
%
% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
% ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
% LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
% CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
% SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
% INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
% CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
% ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
% THE POSSIBILITY OF SUCH DAMAGE.
function [EEG com] = pop_interp(EEG, bad_elec, method, t_range)
com = '';
t_range = '';
if nargin < 1
help pop_interp;
return;
end
if nargin > 1 && nargin < 3
method = 'spherical';
end
if nargin < 2
disp('Warning: interpolation can be done on the fly in studies');
disp(' this function will actually create channels in the dataset');
disp('Warning: do not interpolate channels before running ICA');
disp('You may define channel location to interpolate in the channel');
disp('editor and declare such channels as non-data channels');
enablenondat = 'off';
if isfield(EEG.chaninfo, 'removedchans')
if ~isempty(EEG.chaninfo.removedchans)
enablenondat = 'on';
end
end
if isempty(EEG.epoch)
uilist = { { 'Style' 'text' 'string' 'What channel(s) do you want to interpolate' 'fontweight' 'bold' } ...
{ 'style' 'text' 'string' 'none selected' 'tag' 'chanlist' } ...
{ 'style' 'pushbutton' 'string' 'Select from removed channels' 'callback' 'pop_interp(''nondatchan'',gcbf);' 'enable' enablenondat } ...
{ 'style' 'pushbutton' 'string' 'Select from data channels' 'callback' 'pop_interp(''datchan'',gcbf);' } ...
{ 'style' 'pushbutton' 'string' 'Use specific channels of other dataset' 'callback' 'pop_interp(''selectchan'',gcbf);'} ...
{ 'style' 'pushbutton' 'string' 'Use all channels from other dataset' 'callback' 'pop_interp(''uselist'',gcbf);'} ...
{ } ...
{ 'style' 'text' 'string' 'Interpolation method'} ...
{ 'style' 'popupmenu' 'string' 'Spherical|Planar (slow)' 'tag' 'method' } ...
{ } ...
{ 'Style', 'text', 'string', 'Time range [min max] (s)', 'fontangle', fastif(length(EEG)>1, 'italic', 'normal') } ...
{ 'Style', 'edit', 'string', '', 'enable', fastif(length(EEG)>1, 'off', 'on') } ...
{} { 'Style' 'text' 'string' 'Note: for group level analysis, interpolate in STUDY' } ...
};
geom = { 1 1 1 1 1 1 1 [1.1 1] 1 [1.1 1] 1 1 };
geomvert = [ 1 1 1 1 1 1 1 1 0.5 1 0.5 1 ];
else
uilist = { { 'Style' 'text' 'string' 'What channel(s) do you want to interpolate' 'fontweight' 'bold' } ...
{ 'style' 'text' 'string' 'none selected' 'tag' 'chanlist' } ...
{ 'style' 'pushbutton' 'string' 'Select from removed channels' 'callback' 'pop_interp(''removedchans'',gcbf);' 'enable' enablenondat } ...
{ 'style' 'pushbutton' 'string' 'Select from data channels' 'callback' 'pop_interp(''datchan'',gcbf);' } ...
{ 'style' 'pushbutton' 'string' 'Use specific channels of other dataset' 'callback' 'pop_interp(''selectchan'',gcbf);'} ...
{ 'style' 'pushbutton' 'string' 'Use all channels from other dataset' 'callback' 'pop_interp(''uselist'',gcbf);'} ...
{ } ...
{ 'style' 'text' 'string' 'Interpolation method'} ...
{ 'style' 'popupmenu' 'string' 'Spherical|spherical(Kang et al.)|Planar (slow)' 'tag' 'method' } ...
{} { 'Style' 'text' 'string' 'Note: for group level analysis, interpolate in STUDY' } ...
};
geom = { 1 1 1 1 1 1 1 [1.1 1] 1 1 };
geomvert = [ 1 1 1 1 1 1 1 1 0.5 1 ];
end
[res, userdata, ~, restag ] = inputgui( 'uilist', uilist, 'title', 'Interpolate channel(s) -- pop_interp()', 'geometry', geom, 'geomvert', geomvert, 'helpcom', 'pophelp(''pop_interp'')');
if isempty(res) || isempty(userdata), return; end
if restag.method == 1
method = 'spherical';
elseif restag.method == 2
method = 'sphericalKang';
else
method = 'invdist';
end
bad_elec = userdata.chans;
if nargin < 4
if numel(res) > 1
t_range = res{2};
else
t_range = '';
end
end
if isempty(t_range)
t_range = [EEG.xmin EEG.xmax];
else
t_range = eval( [ '[' t_range ']' ] );
end
if size(t_range,2) ~= 2
error('Time/point range must contain 2 columns exactly');
end
if floor(max(t_range)) > EEG.xmax
error('Time/point range exceed upper data limits');
end
if min(t_range) < EEG.xmin
error('Time/point range exceed lower data limits');
end
com = sprintf('EEG = pop_interp(EEG, %s, ''%s'');', userdata.chanstr, method);
if ~isempty(findstr('removedchans', userdata.chanstr))
eval( [ userdata.chanstr '=[];' ] );
end
elseif ischar(EEG)
command = EEG;
clear EEG;
fig = bad_elec;
userdata = get(fig, 'userdata');
if strcmpi(command, 'removedchans')
global EEG;
tmpchaninfo = EEG.chaninfo;
[chanlisttmp, chanliststr] = pop_chansel( { tmpchaninfo.removedchans.labels } );
if ~isempty(chanlisttmp)
userdata.chans = EEG.chaninfo.removedchans(chanlisttmp);
userdata.chanstr = [ 'EEG.chaninfo.removedchans([' num2str(chanlisttmp) '])' ];
set(fig, 'userdata', userdata);
set(findobj(fig, 'tag', 'chanlist'), 'string', chanliststr);
end
elseif strcmpi(command, 'datchan')
global EEG;
tmpchaninfo = EEG.chanlocs;
[chanlisttmp, chanliststr] = pop_chansel( { tmpchaninfo.labels } );
if ~isempty(chanlisttmp)
userdata.chans = chanlisttmp;
userdata.chanstr = [ '[' num2str(chanlisttmp) ']' ];
set(fig, 'userdata', userdata);
set(findobj(fig, 'tag', 'chanlist'), 'string', chanliststr);
end
else
global ALLEEG EEG;
tmpanswer = inputdlg2({ 'Dataset index' }, 'Choose dataset', 1, { '' });
if ~isempty(tmpanswer)
tmpanswernum = round(str2num(tmpanswer{1}));
if ~isempty(tmpanswernum)
if tmpanswernum > 0 && tmpanswernum <= length(ALLEEG)
TMPEEG = ALLEEG(tmpanswernum);
tmpchans1 = TMPEEG.chanlocs;
if strcmpi(command, 'selectchan')
chanlist = pop_chansel( { tmpchans1.labels } );
else
chanlist = 1:length(TMPEEG.chanlocs); % use all channels
end
% look at what new channels are selected
tmpchans2 = EEG.chanlocs;
[tmpchanlist, chaninds] = setdiff_bc( { tmpchans1(chanlist).labels }, { tmpchans2.labels } );
if ~isempty(tmpchanlist)
if length(chanlist) == length(TMPEEG.chanlocs)
userdata.chans = TMPEEG.chanlocs;
userdata.chanstr = [ 'ALLEEG(' tmpanswer{1} ').chanlocs' ];
else
userdata.chans = TMPEEG.chanlocs(chanlist(sort(chaninds)));
userdata.chanstr = [ 'ALLEEG(' tmpanswer{1} ').chanlocs([' num2str(chanlist(sort(chaninds))) '])' ];
end
set(fig, 'userdata', userdata);
tmpchanlist(2,:) = { ' ' };
set(findobj(gcbf, 'tag', 'chanlist'), 'string', [ tmpchanlist{:} ]);
else
warndlg2('No new channels selected');
end
else
warndlg2('Wrong index');
end
end
end
end
return;
end
% remove from removedchans if interpolated
if isfield(EEG.chaninfo, 'removedchans') && ~isempty(EEG.chaninfo.removedchans) && isstruct(bad_elec)
for iChan = 1:length(bad_elec)
ind = strmatch(lower(bad_elec(iChan).labels), lower({EEG.chaninfo.removedchans.labels}), 'exact');
if ~isempty(ind)
EEG.chaninfo.removedchans(ind) = [];
end
end
end
EEG = eeg_interp(EEG, bad_elec, method, t_range);
Datasets
Models
