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vis_artifacts.m
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vis_artifacts.m
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function [h_old,h_new] = vis_artifacts(new,old,varargin)
% vis_artifacts(NewEEG,OldEEG,Options...)
% Display the artifact rejections done by any of the artifact cleaning functions.
%
% Keyboard Shortcuts:
% [n] : display just the new time series
% [o] : display just the old time series
% [b] : display both time series super-imposed
% [d] : display the difference between both time series
% [+] : increase signal scale
% [-] : decrease signal scale
% [*] : expand time range
% [/] : reduce time range
% [h] : show/hide slider
% [e] : toggle events
% [l] : toggle event legend
%
% In:
% NewEEG : cleaned continuous EEG data set
% OldEEG : original continuous EEG data set
% Options... : name-value pairs specifying the options, with names:
% 'YRange' : y range of the figure that is occupied by the signal plot
% 'YScaling' : distance of the channel time series from each other in std. deviations
% 'WindowLength : window length to display
% 'NewColor' : color of the new (i.e., cleaned) data
% 'OldColor' : color of the old (i.e., uncleaned) data
% 'HighpassOldData' : whether to high-pass the old data if not already done
% 'ScaleBy' : the data set according to which the display should be scaled, can be
% 'old', 'new' or 'noscale' (default: 'new')
% 'ChannelSubset' : optionally a channel subset to display
% 'TimeSubet' : optionally a time subrange to display
% 'DisplayMode' : what should be displayed: 'both', 'new', 'old', 'diff'
% 'ShowSetname' : whether to display the dataset name in the title
% 'EqualizeChannelScaling' : optionally equalize the channel scaling
% See also code for more options.
%
% Notes:
% This function is primarily meant for testing purposes and is not a particularly high-quality
% implementation. If you are a MATLAB expert and have an interest in this function we would
% appreciate it if you chose to do a clean rewrite. :)
%
% Examples:
% vis_artifacts(clean,raw)
%
% % display only a subset of channels
% vis_artifacts(clean,raw,'ChannelSubset',1:4:raw.nbchan);
%
%
% Christian Kothe, Swartz Center for Computational Neuroscience, UCSD
% 2010-07-06
%
% relies on the findjobj() function by Yair M. Altman.
%
% 07/27/2018 Makoto. Addressed the issue of ResizeFcn behavior change started in R2014b. https://www.mathworks.com/help/matlab/graphics_transition/why-has-the-behavior-of-resizefcn-changed.html
% Copyright (C) Christian Kothe, SCCN, 2012, ckothe@ucsd.edu
%
% This program is free software; you can redistribute it and/or modify it under the terms of the GNU
% General Public License as published by the Free Software Foundation; either version 2 of the
% License, or (at your option) any later version.
%
% This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
% even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
% General Public License for more details.
%
% You should have received a copy of the GNU General Public License along with this program; if not,
% write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
% USA
have_signallegend = false;
have_eventlegend = false;
if nargin < 2
old = new;
elseif ischar(old)
varargin = [{old} varargin];
old = new;
end
% parse options
opts = hlp_varargin2struct(varargin, ...
{'yrange','YRange'}, [0.05 0.95], ... % y range of the figure occupied by the signal plot
{'yscaling','YScaling'}, 3.5, ... % distance of the channel time series from each other in std. deviations
{'wndlen','WindowLength'}, 10, ... % window length to display
{'newcol','NewColor'}, [0 0 0.5], ... % color of the new (i.e., cleaned) data
{'oldcol','OldColor'}, [1 0 0], ... % color of the old (i.e., uncleaned) data
{'highpass_old','HighpassOldData'},true, ...% whether to high-pass the old data if not already done
{'show_removed_portions','ShowRemovedPortions'},true, ...% whether to show removed data portions (if only one set is passed in)
{'show_events','ShowEvents'},true, ... % whether to show events
{'show_eventlegend','ShowEventLegend'},false, ... % whether to show a legend for the currently visible events
{'scale_by','ScaleBy'},'allnew',... % the data set according to which the display should be scaled (can be allold, allnew, wndold, wndnew or noscale)
{'channel_subset','ChannelSubset'},[], ... % optionally a channel subset to display
{'time_subset','TimeSubset'},[],... % optionally a time subrange to display
{'display_mode','DisplayMode'},'both',... % what should be displayed: 'both', 'new', 'old', 'diff'
{'show_setname','ShowSetname'},true,... % whether to display the dataset name in the title
{'line_spec','LineSpec'},'-',... % line style for plotting
{'line_width','LineWidth'},0.5,... % line width
{'add_legend','AddLegend'},false,... % add a signal legend
{'equalize_channel_scaling','EqualizeChannelScaling'},false); % optionally equalize the channel scaling
% ensure that the data are not epoched and expand the rejections with NaN's (now both should have the same size)
if opts.show_removed_portions
new = expand_rejections(to_continuous(new));
old = expand_rejections(to_continuous(old));
end
new.chanlocs = old.chanlocs;
% correct for filter delay
if isfield(new.etc,'filter_delay')
new.data = new.data(:,[1+round(new.etc.filter_delay*new.srate):end end:-1:(end+1-round(new.etc.filter_delay*new.srate))]); end
if isfield(old.etc,'filter_delay')
old.data = old.data(:,[1+round(old.etc.filter_delay*old.srate):end end:-1:(end+1-round(old.etc.filter_delay*old.srate))]); end
% make sure that the old data is high-passed the same way as the new data
if opts.highpass_old && isfield(new.etc,'clean_drifts_kernel') && ~isfield(old.etc,'clean_drifts_kernel')
old.data = old.data';
for c=1:old.nbchan
old.data(:,c) = filtfilt_fast(new.etc.clean_drifts_kernel,1,old.data(:,c)); end
old.data = old.data';
end
if isscalar(opts.line_width)
opts.line_width = [opts.line_width opts.line_width]; end
% optionally pick a subrange to work on
if ~isempty(opts.channel_subset)
old = pop_select(old,'channel',opts.channel_subset);
new = pop_select(new,'channel',opts.channel_subset);
end
if ~isempty(opts.time_subset)
old = pop_select(old,'time',opts.time_subset);
new = pop_select(new,'time',opts.time_subset);
end
if opts.equalize_channel_scaling
rescale = 1./mad(old.data,[],2);
new.data = bsxfun(@times,new.data,rescale);
old.data = bsxfun(@times,old.data,rescale);
end
% generate event colormap - fixed for numeric event type by Makoto 5/15/14
if ~isempty(old.event)
if isnumeric(old.event(1).type)
for tmpIdx = 1:length(old.event)
old.event(tmpIdx).type = num2str(old.event(tmpIdx).type);
end
end
opts.event_colormap = gen_colormap(old.event,'jet');
end
% calculate whole-data scale
old_iqr = 2*mad(quantile(old.data',1000),1)';
old_iqr(isnan(old_iqr)) = deal(mean(old_iqr(~isnan(old_iqr))));
new_iqr = 2*mad(quantile(new.data',1000),1)';
new_iqr(isnan(new_iqr)) = deal(mean(new_iqr(~isnan(new_iqr))));
% create figure & slider
lastPos = 0;
%hFig = figure('ResizeFcn',@on_window_resized,'KeyPressFcn',@(varargin)on_key(varargin{2}.Key)); hold; axis();
hFig = figure('KeyPressFcn',@(varargin)on_key(varargin{2}.Key)); hold; axis();
hAxis = gca;
hSlider = uicontrol('style','slider','KeyPressFcn',@(varargin)on_key(varargin{2}.Key)); on_resize();
set(hSlider, 'callback', @on_update);
% jSlider = findjobj(hSlider);
% jSlider.AdjustmentValueChangedCallback = @on_update;
annotation(hFig,'textbox', [0 0.07 1 0],...
'String', {'Keyboard shortcuts: [n] new data, [o] old data, [b] both data, [d] difference, [+] increase amp scale, [-] decrease amp scale, [*] shrink time scale, [/] expand time scale, [h] show/hide slider.'},...
'HorizontalAlignment','center', 'FontSize',14, 'FitBoxToText','off', 'LineStyle','none');
% Implementing a delay fuse (07/27/2017 Makoto).
set(hFig, 'ResizeFcn', @on_window_resized);
% do the initial update
%on_update();
function repaint(relPos,moved)
% repaint the current data
% if this happens, we are maxing out MATLAB's graphics pipeline: let it catch up
if relPos == lastPos && moved
return; end
% axes
cla(hAxis);
gca = hAxis;
% compute pixel range from axis properties
xl = get(hAxis,'XLim');
yl = get(hAxis,'YLim');
fp = get(hFig,'Position');
ap = get(hAxis,'Position');
pixels = (fp(3))*(ap(3)-ap(1));
ylr = yl(1) + opts.yrange*(yl(2)-yl(1));
channel_y = (ylr(2):(ylr(1)-ylr(2))/(size(new.data,1)-1):ylr(1))';
% Add channel labels to y axis
if isfield(old.chanlocs,'labels')
set(hAxis,'ytick',flipud(channel_y));
set(hAxis,'yticklabel',fliplr({old.chanlocs.labels}));
end
% compute sample range
wndsamples = opts.wndlen * new.srate;
pos = floor((size(new.data,2)-wndsamples)*relPos);
wndindices = 1 + floor(0:wndsamples/pixels:(wndsamples-1));
wndrange = pos+wndindices;
wndrange(wndrange > length(old.data)) = length(old.data);
oldwnd = old.data(:,wndrange);
newwnd = new.data(:,wndrange);
switch opts.scale_by
case 'allnew'
iqrange = new_iqr;
case 'allold'
iqrange = old_iqr;
case {'wndnew','new'}
iqrange = mad(newwnd',1)';
iqrange(isnan(iqrange)) = mad(oldwnd(isnan(iqrange),:)',1)';
case {'wndold','old'}
iqrange = mad(oldwnd',1)';
case 'noscale'
iqrange = ones(size(new.data,1),1);
otherwise
error('Unsupported scale_by option.');
end
scale = ((ylr(2)-ylr(1))/size(new.data,1)) ./ (opts.yscaling*iqrange); scale(~isfinite(scale)) = 0;
scale(scale>median(scale)*3) = median(scale);
scale = scale(:);
scale = repmat(scale,1,length(wndindices));
% draw
if opts.show_setname
tit = sprintf('%s - ',[old.filepath filesep old.filename]);
else
tit = '';
end
if ~isempty(wndrange)
tit = [tit sprintf('[%.1f - %.1f]',new.xmin + (wndrange(1)-1)/new.srate, new.xmin + (wndrange(end)-1)/new.srate)];
end
xrange = xl(1):(xl(2)-xl(1))/(length(wndindices)-1):xl(2);
yoffset = repmat(channel_y,1,length(wndindices));
switch opts.display_mode
case 'both'
title([tit '; superposition'],'Interpreter','none');
h_old = plot(xrange, (yoffset + scale.*oldwnd)','Color',opts.oldcol,'LineWidth',opts.line_width(1));
h_new = plot(xrange, (yoffset + scale.*newwnd)','Color',opts.newcol,'LineWidth',opts.line_width(2));
case 'new'
title([tit '; cleaned'],'Interpreter','none');
plot(xrange, (yoffset + scale.*newwnd)','Color',opts.newcol,'LineWidth',opts.line_width(2));
case 'old'
title([tit '; original'],'Interpreter','none');
plot(xrange, (yoffset + scale.*oldwnd)','Color',opts.oldcol,'LineWidth',opts.line_width(1));
case 'diff'
title([tit '; difference'],'Interpreter','none');
plot(xrange, (yoffset + scale.*(oldwnd-newwnd))','Color',opts.newcol,'LineWidth',opts.line_width(1));
end
% also plot events
if opts.show_events && ~isempty(old.event)
evtlats = [old.event.latency];
evtindices = find(evtlats>wndrange(1) & evtlats<wndrange(end));
if ~isempty(evtindices)
evtpos = xl(1) + (evtlats(evtindices)-wndrange(1))/wndsamples*(xl(2)-xl(1));
evttypes = {old.event(evtindices).type};
% for each visible type
visible_types = unique(evttypes);
handles = [];
labels = {};
for ty = visible_types(:)'
% plot line instances in the right color
% curtype = ty{1}; % same fix as above
if isnumeric(ty)
curtype = ty(1);
else
curtype = ty{1};
end
curcolor = opts.event_colormap.values(strcmp(opts.event_colormap.keys,curtype),:);
matchpos = strcmp(evttypes,curtype);
h = line([evtpos(matchpos);evtpos(matchpos)],repmat([0;1],1,nnz(matchpos)),'Color',curcolor);
handles(end+1) = h(1);
labels{end+1} = curtype;
end
if opts.show_eventlegend
legend(handles,labels,'Location','NorthWest');
have_eventlegend = true;
elseif have_eventlegend
legend off;
have_eventlegend = false;
end
end
end
axis([0 1 0 1]);
if opts.add_legend && ~have_signallegend
legend([h_old(1);h_new(1)],'Original','Corrected');
have_signallegend = 1;
end
drawnow;
lastPos = relPos;
end
function on_update(varargin)
% slider moved
repaint(get(hSlider,'Value'),~isempty(varargin));
end
function on_resize(varargin)
% adapt/set the slider's size
wPos = get(hFig,'Position');
if ~isempty(hSlider)
try
set(hSlider,'Position',[20,20,wPos(3)-40,20]);
catch,end
on_update;
end
end
function on_window_resized(varargin)
% window resized
on_resize();
end
function EEG = to_continuous(EEG)
% convert an EEG set to continuous if currently epoched
if ndims(EEG.data) == 3
EEG.data = EEG.data(:,:);
[EEG.nbchan,EEG.pnts,EEG.trials] = size(EEG.data);
end
end
function EEG = expand_rejections(EEG)
% reformat the new data so that it can be super-imposed with the old data
[EEG.nbchan,EEG.pnts] = size(EEG.data);
if ~isfield(EEG.etc,'clean_channel_mask')
EEG.etc.clean_channel_mask = true(1,EEG.nbchan); end
if ~isfield(EEG.etc,'clean_sample_mask')
EEG.etc.clean_sample_mask = true(1,EEG.pnts); end
tmpdata = nan(length(EEG.etc.clean_channel_mask),length(EEG.etc.clean_sample_mask));
tmpdata(EEG.etc.clean_channel_mask,EEG.etc.clean_sample_mask) = EEG.data;
EEG.data = tmpdata;
[EEG.nbchan,EEG.pnts] = size(EEG.data);
end
function on_key(key)
switch lower(key)
case {'add','+'}
% decrease datascale
opts.yscaling = opts.yscaling*0.9;
case {'subtract','-'}
% increase datascale
opts.yscaling = opts.yscaling*1.1;
case {'multiply','*'}
% increase timerange
opts.wndlen = opts.wndlen*1.1;
case {'divide','/'}
% decrease timerange
opts.wndlen = opts.wndlen*0.9;
case 'pagedown'
% shift display page offset down
opts.pageoffset = opts.pageoffset+1;
case 'pageup'
% shift display page offset up
opts.pageoffset = opts.pageoffset-1;
case 'n'
opts.display_mode = 'new';
case 'o'
opts.display_mode = 'old';
case 'b'
opts.display_mode = 'both';
case 'd'
opts.display_mode = 'diff';
case 'l'
opts.show_eventlegend = ~opts.show_eventlegend;
case 'e'
opts.show_events = ~opts.show_events;
case 'h'
if strcmp(get(hSlider,'Visible'),'on')
set(hSlider,'Visible','off')
else
set(hSlider,'Visible','on')
end
end
on_update();
end
end
% create a mapping from event types onto colors
function map = gen_colormap(eventstruct,mapname)
map.keys = unique({eventstruct.type});
if isscalar(map.keys)
tmp = colormap(mapname);
map.values = tmp(round(end/2),:);
elseif ~isempty(map.keys)
tmp = colormap(mapname);
map.values = tmp(1+floor((0:length(map.keys)-1)/(length(map.keys)-1)*(length(tmp)-1)),:);
else
map.values = [];
end
end