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Slepi/Code/Utilities/spm12/spm_eeg_artefact_zscore.m
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spm_eeg_artefact_zscore.m
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function res = spm_eeg_artefact_zscore(S)
% Plugin for spm_eeg_artefact doing z-score thresholding
% S - input structure
% fields of S:
% S.D - M/EEG object
% S.chanind - vector of indices of channels that this plugin will look at.
%
% Additional parameters can be defined specific for each plugin
% Output:
% res -
% If no input is provided the plugin returns a cfg branch for itself
%
% If input is provided the plugin returns a matrix of size D.nchannels x D.ntrials
% with zeros for clean channel/trials and ones for artefacts.
%______________________________________________________________________________________
% Copyright (C) 2013 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_eeg_artefact_zscore.m 6060 2014-06-19 13:31:19Z vladimir $
%-This part if for creating a config branch that plugs into spm_cfg_eeg_artefact
% Any parameters can be specified and they are then passed to the plugin
% when it's called.
%--------------------------------------------------------------------------
if nargin == 0
threshold = cfg_entry;
threshold.tag = 'threshold';
threshold.name = 'Threshold';
threshold.strtype = 'r';
threshold.num = [1 1];
threshold.val = {3};
threshold.help = {'Threshold value (in stdev)'};
excwin = cfg_entry;
excwin.tag = 'excwin';
excwin.name = 'Excision window';
excwin.strtype = 'r';
excwin.num = [1 1];
excwin.val = {100};
excwin.help = {'Window (in ms) to mark as bad around each event (for mark mode only), 0 - do not mark data as bad'};
zscore = cfg_branch;
zscore.tag = 'zscore';
zscore.name = 'Threshold z-scored data';
zscore.val = {threshold, excwin};
res = zscore;
return
end
SVNrev = '$Rev: 6060 $';
%-Startup
%--------------------------------------------------------------------------
spm('sFnBanner', mfilename, SVNrev);
spm('FigName','M/EEG zscore thresholding');
D = spm_eeg_load(S.D);
chanind = S.chanind;
threshold = S.threshold;
if isequal(S.mode, 'reject') && isequal(D.type, 'continuous')
error('Rejection mode not for continuous data');
end
res = zeros(D.nchannels, D.ntrials);
%-Artefact detection
%--------------------------------------------------------------------------
spm_progress_bar('Init', length(chanind), 'Channels checked');
if length(chanind) > 100, Ibar = floor(linspace(1, length(chanind),100));
else Ibar = [1:length(chanind)]; end
for j = 1:length(chanind)
dat = squeeze(D(chanind(j), :, :));
if size(dat, 1) == 1
dat = dat';
end
dat = detrend(dat);
zsdat = dat./repmat(std(dat), size(dat, 1), 1);
if isequal(S.mode, 'reject')
res(chanind(j), :) = any(abs(zsdat)>threshold);
elseif isequal(S.mode, 'mark')
bad = abs(zsdat)>threshold;
if ~any(bad(:))
if isequal(D.type, 'continuous')
if ismember(j, Ibar), spm_progress_bar('Set', j); end
end
continue;
end
if S.excwin>0
excwin = ones(round(1e-3*S.excwin*D.fsample), 1);
bad = ~~conv2(excwin, 1, double(bad), 'same');
end
for i = 1:D.ntrials
res = [];
onsets = find(bad(:, i));
offsets = find(~bad(:, i));
onsets(find(diff(onsets)<2)+1) = [];
if bad(end)
offsets(end+1) = length(bad)+1;
end
for k = 1:length(onsets)
res(end+1).type = 'artefact_zscore';
res(end).value = char(D.chanlabels(chanind(j)));
res(end).time = D.time(onsets(k)+1) - D.time(1) + D.trialonset(i);
res(end).duration = (min(offsets(offsets>onsets(k)))-onsets(k))./D.fsample;
end
if ~isempty(res)
ev = D.events(i);
if iscell(ev)
ev = ev{1};
end
if ~S.append
sel1 = strmatch('artefact_zscore', {ev.type});
sel2 = strmatch(char(D.chanlabels(chanind(j))), {ev(sel1).value});
ev(sel1(sel2)) = [];
end
D = events(D, i, spm_cat_struct(ev, res));
end
end
end
if ismember(j, Ibar), spm_progress_bar('Set', j); end
end
spm_progress_bar('Clear');
if isequal(S.mode, 'mark')
res = D;
end
spm('FigName','M/EEG zscore thresholding: done');
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