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Adapted rej_eyecontin.m to work on epoched data #29

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Adapted rej_eyecontin.m to work on epoched data #29

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146 changes: 106 additions & 40 deletions rej_eyecontin.m
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,25 @@
% pop_detecteyemovements(), so that the bad ET data will not
% distort the detection of saccades and fixations
%
%
% Update November 2024: The function can now receive as
% input continuous or epoched data. If run on epoched data
% with rejectionmethod == 2, it will add events to the EEG
% structure with an additional column 'epoch'. This allows
% to mask bad ET samples when using detecteyemovements.m on
% epoched data, as epoching does not update event duration,
% and thus 'bad_ET' derived from continous data creates
% wrong bad_ET masks on epoched data.
% Note that the epoch added to EEG.event is the epoch an
% event started at; if an event lasts several epochs, this
% will not appear on the 'epoch' column, but will still
% work correctly in detecteyemovements.m. The new optional
% third output, seqs_bad, is a struct with information
% regrading bad ET sequences per epoch.
% This implementation currently does not allow to use
% rejectionmethod == 1 with epoched data.
% Rotem Krispil, [email protected].
%
% Usage:
% >> EEG = rej_eyecontin(EEG,chns,minvals,maxvals,windowsize,rejectionmethod)
%
Expand Down Expand Up @@ -79,7 +98,7 @@
%
% See also: pop_rej_eyecontin, pop_rej_eyeepoch, pop_select
%
% Author: od
% Author: od, rk (2024 update)
% Copyright (C) 2009-2021 Olaf Dimigen & Ulrich Reinacher, HU Berlin
% [email protected]
% This program is free software; you can redistribute it and/or modify
Expand All @@ -96,7 +115,7 @@
% along with this program; if not, write to the Free Software
% Foundation, 51 Franklin Street, Boston, MA 02110-1301, USA

function [EEG,seq_bad] = rej_eyecontin(EEG,chns,minvals,maxvals,windowsize,rejectionmethod)
function [EEG,seq_bad_overall, seqs_bad] = rej_eyecontin(EEG,chns,minvals,maxvals,windowsize,rejectionmethod)


%% input checks
Expand All @@ -123,23 +142,30 @@

%% collect indices of out-of-range samples across all channels
fprintf('\n%s(): Rejecting intervals with out-of-range eye track',mfilename)
ix_bad = [];
for c = 1:length(chns)
[chnid chntxt] = eeg_decodechan(EEG.chanlocs,chns(c)); % get channel names

if checkminima && checkmaxima
fprintf('\nChannel %i \"%s\": rejecting values < %i or > %i',chnid,chntxt{:},minvals(c),maxvals(c));
ix = find(EEG.data(chns(c),:) < minvals(c) | EEG.data(chns(c),:) > maxvals(c));
elseif checkminima
fprintf('\nChannel %i \"%s\": rejecting values < %i',chnid,chntxt{:},minvals(c));
ix = find(EEG.data(chns(c),:) > maxvals(c));
else
fprintf('\nChannel %i \"%s\": rejecting values > %i',chnid,chntxt{:},maxvals(c));
ix = find(EEG.data(chns(c),:) < minvals(c) );
ix_bad = {};
nEpochs = size(EEG.data, 3);
nSamples = size(EEG.data, 2);
for e = 1:nEpochs
ix_bad_e = [];
for c = 1:length(chns)
[chnid chntxt] = eeg_decodechan(EEG.chanlocs,chns(c)); % get channel names

if checkminima && checkmaxima
fprintf('\nChannel %i \"%s\": rejecting values < %i or > %i',chnid,chntxt{:},minvals(c),maxvals(c));
ix = find(EEG.data(chns(c),:, e) < minvals(c) | EEG.data(chns(c),:, e) > maxvals(c));
elseif checkminima
fprintf('\nChannel %i \"%s\": rejecting values < %i',chnid,chntxt{:},minvals(c));
ix = find(EEG.data(chns(c),:, e) > maxvals(c));
else
fprintf('\nChannel %i \"%s\": rejecting values > %i',chnid,chntxt{:},maxvals(c));
ix = find(EEG.data(chns(c),:, e) < minvals(c) );
end
ix_bad_e = [ix_bad_e ix];

end
ix_bad = [ix_bad ix];
ix_bad{e} = unique(ix_bad_e);
end
ix_bad = unique(ix_bad);
%ix_bad = unique(ix_bad);
if windowsize > 0
fprintf('\nRejecting an extra plusminus %i samples (%.0f ms) around each out-of-range interval.',windowsize,windowsize*1000/EEG.srate)
end
Expand All @@ -148,45 +174,85 @@
if isempty(ix_bad)
fprintf('\n\nNo out-of-range samples found.\n')
else
% identify start and end indices of contiguous blocks of bad data
seq_bad = findsequence2(ix_bad');

% add extra rejection zone around bad data
% e.g., to remove sub-threshold bad samples at beginning/end of blinks
seq_bad(:,1) = seq_bad(:,1) - windowsize;
seq_bad(:,2) = seq_bad(:,2) + windowsize;

% remove indices outside data boundaries
seq_bad(seq_bad(:,1) < 1,1) = 1;
seq_bad(seq_bad(:,2) > size(EEG.data,2),2) = size(EEG.data,2);

seqs_bad = {};
for e = 1:nEpochs
% Check if epoch has bad data
if ~isempty(ix_bad{e})

% identify start and end indices of contiguous blocks of bad data
seq_bad = findsequence2(ix_bad{e}');

% add extra rejection zone around bad data
% e.g., to remove sub-threshold bad samples at beginning/end of blinks
seq_bad(:,1) = seq_bad(:,1) - windowsize;
seq_bad(:,2) = seq_bad(:,2) + windowsize;

% remove indices outside data boundaries
seq_bad(seq_bad(:,1) < 1,1) = 1;
seq_bad(seq_bad(:,2) > nSamples,2) = nSamples;

seqs_bad{e} = seq_bad;
end

end
% to handle overlapping blocks of bad data
% translate again into vector of bad samples (0 = good, 1 = bad)
badvector = zeros(size(EEG.data,2),1);
for n = 1:size(seq_bad,1);
badvector(seq_bad(n,1):seq_bad(n,2)) = 1;
badvectoroverall = zeros(nSamples * nEpochs,1);
for e = 1:nEpochs
if ~isempty(seqs_bad{e})
seq_bad = seqs_bad{e};
badvector = zeros(nSamples, 1);
for n = 1:size(seq_bad,1)
badvectoroverall((seq_bad(n,1) + nSamples*(e-1)) : (seq_bad(n,2) + nSamples*(e-1))) = 1;
badvector(seq_bad(n,1):seq_bad(n,2)) = 1;
end

% get start/end of bad blocks again
seqs_bad{e} = findsequence2(find(badvector));
end
end

% get start/end of bad blocks again
seq_bad = findsequence2(find(badvector));
seq_bad_overall = findsequence2(find(badvectoroverall));

fprintf('\n\nFound %i out-of-range samples in %i continuous intervals of bad data',length(ix_bad),size(seq_bad,1));
if nEpochs > 1
% Add epoch column
epochs = floor(seq_bad_overall(:,1)/nSamples) + 1;
seq_bad_overall(:,4) = epochs;
else
% If single epoch, extract from struct. seq_bad_overall is a double
% already if single epoch - maintaining compatability with previous
% version.
seqs_bad = seqs_bad{1};
end

fprintf('\n\nFound %i out-of-range samples in %i continuous intervals of bad data',sum(badvectoroverall),size(seq_bad_overall,1));

% reject bad data
switch rejectionmethod

case 1 % remove using pop_select
fprintf('\nRemoving bad intervals from continuous data...\n\n')
EEG = pop_select(EEG,'nopoint',seq_bad(:,1:2));

if nEpochs == 1
fprintf('\nRemoving bad intervals from continuous data...\n\n')
EEG = pop_select(EEG,'nopoint',seq_bad_overall(:,1:2));
else
error('%s(): Attempting to remove bad intervals from epoched data', mfilename)
end
case 2 % add "bad_ET" marker for each bad interval

% Update march 2018 (OD)
% Do not remove data, but introduce new bad_ET events in EEG.event
fprintf('\nAdding %i bad_ET markers to the EEG.event structure...\nNot removing any data.\n\n',size(seq_bad,1))
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seq_bad needs to be changed to seq_bad_overall. Sorry for missing that.

% add duration of bad interval (in samples)
seq_bad(:,3) = seq_bad(:,2)-seq_bad(:,1) + 1;
% Are there already bad_ET events (e.g., from runnning the
% function previously on continuous data)? If so, remove them.
old_badET_events = contains({EEG.event.type}, {'bad_ET'});
EEG = pop_editeventvals(EEG,'delete', find(old_badET_events));
% add bad ET markers
EEG = addevents(EEG,[seq_bad(:,1) seq_bad(:,3)],{'latency','duration'},'bad_ET');
if nEpochs == 1
EEG = addevents(EEG,[seq_bad_overall(:,1) seq_bad_overall(:,3)],{'latency','duration'},'bad_ET');
else
EEG = addevents(EEG,[seq_bad_overall(:,1) seq_bad_overall(:,3:4)],{'latency','duration', 'epoch'},'bad_ET');
end

otherwise
error('%s(): rejection method input not recognized',mfilename)
Expand Down