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Slepi/Code/Utilities/spm12/spm_eeg_artefact_nans.m
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spm_eeg_artefact_nans.m
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function res = spm_eeg_artefact_nans(S)
% Plugin for spm_eeg_artefact doing NaN detection.
% 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) 2011-2013 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_eeg_artefact_nans.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
nans = cfg_branch;
nans.tag = 'nans';
nans.name = 'Detect NaNs';
nans.val = {};
res = nans;
return
end
SVNrev = '$Rev: 6060 $';
%-Startup
%--------------------------------------------------------------------------
spm('sFnBanner', mfilename, SVNrev);
spm('FigName','M/EEG NaN detection');
D = spm_eeg_load(S.D);
chanind = S.chanind;
res = zeros(D.nchannels, D.ntrials);
if isequal(S.mode, 'reject')
res = zeros(D.nchannels, D.ntrials);
%-Artefact detection
%--------------------------------------------------------------------------
spm_progress_bar('Init', D.ntrials, 'Trials checked');
if D.ntrials > 100, Ibar = floor(linspace(1, D.ntrials,100));
else Ibar = [1:D.ntrials]; end
for i = 1:D.ntrials
for j = 1:length(chanind)
if any(isnan(squeeze(D(chanind(j), :, i))))
res(chanind(j), i) = 1;
end
end
if ismember(i, Ibar), spm_progress_bar('Set', i); end
end
spm_progress_bar('Clear');
elseif isequal(S.mode, 'mark')
if isequal(D.type, 'continuous')
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
else
spm_progress_bar('Init', D.ntrials, 'Trials checked');
if D.ntrials > 100, Ibar = floor(linspace(1, D.ntrials,100));
else Ibar = [1:D.ntrials]; end
end
for i = 1:D.ntrials
res = [];
for j = 1:length(chanind)
dat = ~isnan(squeeze(D(chanind(j), :, i)));
if sum(dat)/length(dat)<(1-S.badchanthresh)
res(end+1).type = 'artefact_nan';
res(end).value = char(D.chanlabels(chanind(j)));
res(end).time = D.trialonset(i);
res(end).duration = D.time(end) - D.time(1) + 1;
else
tmp = find(dat);
diffs = diff([0 tmp D.nsamples]);
onsets = find(diffs>1);
onsetsamples = [];
if any(onsets == 1);
onsetsamples = 1;
onsets(1) = [];
onsetsamples = [onsetsamples tmp(onsets-1)+1];
onsets = [1 onsets];
else
onsetsamples = [onsetsamples tmp(onsets-1)+1];
end
k = 1;
m = 1;
while k<=length(onsets)
if m <= length(onsets)
ind1 = onsetsamples(k);
ind2 = onsetsamples(m) + diffs(onsets(m))-2;
if ind2 > length(dat)
ind2 = length(dat);
end
if (sum(dat(ind1:ind2))/(ind2-ind1+1))<0.5
m = m+1;
else
if m>k
m = m-1;
end
res(end+1).type = 'artefact_nan';
res(end).value = char(D.chanlabels(chanind(j)));
res(end).time = D.time(onsetsamples(k)+1) - D.time(1) + D.trialonset(i);
res(end).duration = (onsetsamples(m) + diffs(onsets(m))-onsetsamples(k)-1)/D.fsample;
k = m+1;
m = k;
end
else
ind1 = onsetsamples(k);
ind2 = length(dat);
if (sum(dat(ind1:ind2))/(ind2-ind1+1))<0.5
res(end+1).type = 'artefact_nan';
res(end).value = char(D.chanlabels(chanind(j)));
res(end).time = D.time(onsetsamples(k)+1) - D.time(1) + D.trialonset(i);
res(end).duration = (length(dat)-onsetsamples(k)+2)/D.fsample;
else
m = m-1;
res(end+1).type = 'artefact_nan';
res(end).value = char(D.chanlabels(chanind(j)));
res(end).time = D.time(onsetsamples(k)+1) - D.time(1) + D.trialonset(i);
res(end).duration = (onsetsamples(m) + diffs(onsets(m))-onsetsamples(k)-1)/D.fsample;
end
break;
end
end
end
if isequal(D.type, 'continuous')
if ismember(j, Ibar), spm_progress_bar('Set', j); end
end
end
if ~isempty(res)
ev = D.events(i);
if iscell(ev)
ev = ev{1};
end
if ~S.append
ev(strmatch('artefact_nan', {ev.type})) = [];
end
D = events(D, i, spm_cat_struct(ev, res));
end
if ~isequal(D.type, 'continuous')
if ismember(i, Ibar), spm_progress_bar('Set', i); end
end
end
spm_progress_bar('Clear');
res = D;
end
spm('FigName','M/EEG NaN detection: done');
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