pascalnide
pascalnide copied to clipboard
tranleduy2000
switching
| 15 | #now switch to the install directory (we might be running from a link in /usr/bin/local) |
|---|---|
| 16 | |
| 17 | cd "$DIR" |
| 18 | |
| 19 | echo "in dir:" $PWD |
| 20 | |
| 21 | echo Starting pipereader |
| … | |
| 27 | # this does not seem to work. |
| 28 | pkill -P $$ |
| 29 | # remove the pipe for next time |
| 30 | rm -f $pipe |
| 31 | } |
| 32 | |
| 33 | trap cleanup EXIT |
% detecteyemovements() - detect saccades & fixations in eye tracking data. % Saccade detection is based on the algorithm by % Engbert & Mergenthaler (2006). Saccades are defined as % (monocular or binocular) outliers in 2D velocity space. % Velocity thresholds for saccade detection are determined % adaptively as a multiple of the (median-based) SD of all % data samples in the epoch. Fixations are defined as % intervals in-between saccades. Eye movements can be added % as new events to EEGLAB's event structure. For various % other options, see below. % % Usage: % >> EEG = detecteyemovements(EEG,left_eye_xy,right_eye_xy,vfac,mindur,... % degperpixel,smooth,globalthresh,clusterdist,clustermode, % plotfig,writesac,writefix) % % Required inputs: % EEG - [string] EEG struct, also containing synchronized eye % tracking data (see pop_importeyetracker) % left_eye_xy - [vector of two channel indices], % specifying channel indices of X- (first value) and % Y-component (second value) of left eye. Leave empty [] % if the left eye was not recorded. % right_eye_xy - [vector of two channel indices], % specifying channel indices of X- (first value) and % Y-component (second value) of right eye. Leave empty [] % if the right eye was not recorded. % vfac - [double] velocity factor ("lambda") to determine % the velocity threshold for saccade detection % (cf. Engbert & Mergenthaler, 2006) % mindur - [integer] minimum saccade duration (in samples) % (cf. Engbert & Mergenthaler, 2006) % degperpixel - [double] visual angle of one screen pixel % if this value is left empty [], saccade characteristics % are reported in the original data metric (pixel?) % instead of in degrees of visual angle % smooth - [0/1] if set to 1, the raw data is smoothed over a % 5-sample window to suppress noise % noise. Recommended for high native ET sampling rates. % globalthresh - [0/1]. Use the same thresholds for all epochs? % 0: Adaptive velocity thresholds are computed % individually for each data epoch. % 1: Adaptive velocity thresholds are first computed for % each epoch, but then the mean thresholds are applied to % each epochs (i.e. same detection parameters are used for % all epochs). Setting is irrelevant if the input data is % still continuous (= only one data epoch). % clusterdist - [integer] value in sampling points that defines the % minimum allowed fixation duration between two saccades. % If the off- and onsets of two temp. adjacent sacc. are % closer together than 'clusterdist' samples, these % saccades are regarded as a "cluster" and treated % according to the 'clustermode' setting (see below). % clusterdist is irrelevant if clustermode == 1. % clustermode - [1,2,3,4]. Integer between 1 and 4. % 1: keep all saccades, do nothing % 2: keep only first saccade of each cluster % 3: keep only largest sacc. of each cluster % 4: combine all movements into one (longer) saccade % this new saccade is defined as the movement that % occurs between the onset of the 1st saccade in the % cluster and the offset of the last sacc. in cluster % WARNING: CLUSTERMODE 4 is experimental and untested! % plotfig - [0/1] Show a figure with eye movement properties? % 0: do not plot a figure. % 1: plot a figure displaying properties of detected % saccades & fixations % writesac - [0/1]: Add saccades to EEG.event? % 0: detect saccades, but do not store them in EEG.event. % 1: add detected saccades as new events to EEG.event. % writefix - [0/1]: Add fixations to EEG.event? % 0: detect fixations, but do not add them to EEG.event. % 1: add detected fixations as new events to EEG.event. % Note: It is recommended to first test the parameters of % saccade/fixation detection without adding events. % For this, set writesac and writefix to 0. % % Outputs: % EEG - EEG structure. If writesac or writefix were set to 1, % the EEG structure (EEG.event/EEG.urevent/EEG.epoch) % will contain additional "saccade" and "fixation" events % with their respective properties % % See also: vecvel, velthresh, microsacc_plugin, binsacc, saccpar, % mergesacc, addevents % % % MAJOR UPDATE 03/2018: bad ET intervals, as marked by "bad_ET" events % in EEG.event (see function pop_rej_contin.m) are excluded from eye movement % detection. Bad intervals are now ignored when estimating velocity thresholds % and saccade/fixation events overlapping with these intervals are removed % % An example call of the function might look like this: % >> EEG = detecteyemovements(EEG,[],[33 34],6,4,0.037,1,0,25,4,1,1,0) % % In this example, the eye position data for the right eye is stored in % channels 33 (horiz.) and 34 (vertical). The left eye was not recorded. % The velocity threshold is set to 6 times the (median-based) % SD of all velocity samples in the epoch. The minimum duration of % saccades to be detected is 4 samples. In the experiment, one screen % pixel corresponded to 0.037 degrees of visual angle. % The raw data is smoothed prior to saccade detection (smooth: 1). % Adaptive velocity thresholds (X and Y-threshold for each eye) are % determined individually for each data epoch (globalthresh: 0). For saccades % separated by fixations of less than 25 samples, only the first saccade % is kept (clusterdist: 25, clustermode: 2). A figure with the % results is plotted. Detected saccades are stored as new events in % EEG.event, but fixations are not stored. % % The eye movement detection is based on: % % Engbert, R., & Kliegl, R. (2003). Microsaccades uncover the orientation % of covert attention. Vision Research, Vol. 43, 1035-1045 % % ...as well as... % % Engbert, R., & Mergenthaler, K. (2006). Microsaccades are triggered by % low retinal image slip, PNAS, Vol. 103 (18), 7192-7197 % % Author: od % Copyright (C) 2009-2018 Olaf Dimigen, HU Berlin % [email protected]
% 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 3 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, 51 Franklin Street, Boston, MA 02110-1301, USA
function EEG = detecteyemovements(EEG,left_eye_xy,right_eye_xy,vfac,mindur,degperpixel,smooth,globalthresh,clusterdist,clustermode,plotfig,writesac,writefix)
allsac = []; allfix = [];
% data of which eye is available? ldata = false; if length(left_eye_xy) == 2, ldata = true; end rdata = false; if length(right_eye_xy) == 2, rdata = true; end
if length(left_eye_xy) == 1 || length(right_eye_xy) == 1 error('%s(): For each recorded eye, horizontal (X) and vertical (Y) gaze channel must be specified.',mfilename); end
nsample = size(EEG.data,2); nepochs = size(EEG.data,3); badepochs = zeros(nepochs,1); nbadsmp = 0;
% preallocate storage for sacc. detection thresholds l_msdx = NaN(nepochs,1); l_msdy = NaN(nepochs,1); r_msdx = NaN(nepochs,1); r_msdy = NaN(nepochs,1);
% warn message if back-to-back saccades are detected clusterwarning = false;
% critical bugfix 2013-10-01, by OD % due to bug in third-party function "smoothdata": % function smoothdata() was removed from the toolbox % function vecvel() was updated to incorporate different levels of smoothing: % options: % - smoothlevel 0: no smoothing, simple diff() % - smoothlevel 1: 3-point window % - smoothlevel 2: 5-point window if smooth smoothlevel = 2; % 5-point smoothing else smoothlevel = 0; % no smoothing end
%% screen feedback
% detection feedback if ~ldata && ~rdata error('%s(): Please correctly specify channels containing eye tracking data.\nFor each recorded eye, both horiz. (X) and vert. (Y) channel must be specified.',mfilename) else % summary of detection parameters fprintf('\n--------------------------------------------------------------------') fprintf('\nDetecting saccades after Engbert & Mergenthaler (2006)\n') fprintf('\nVelocity threshold factor (vfac): %.2f SD',vfac); fprintf('\nMinimum saccade duration (mindur): %.2f samples (%.2f ms)',mindur,mindur1000/EEG.srate); if ~isempty(degperpixel) | isnan(degperpixel) % bugfix 2016-11-12 by OD: added case if degperpixel = NaN (from GUI input) fprintf('\nVisual angle per screen pixel: %f°',degperpixel); metric = 'deg'; else fprintf('\nWARNING: No input provided for degperpixel!\nSpatial saccade properties are given in original metric (pixel?)'); degperpixel = 1; metric = 'pix'; end if nepochs < 2, fprintf('\n-- Using continuous data.'), else fprintf('\n-- Using epoched data.'); end if ldata && rdata, fprintf('\n-- Using binocular data:'); else fprintf('\n-- Using monocular data:'); end if ldata fprintf('\n\tLeft horiz.: "%s"',EEG.chanlocs(left_eye_xy(1)).labels); fprintf('\n\tLeft verti.: "%s"',EEG.chanlocs(left_eye_xy(2)).labels); end if rdata fprintf('\n\tRight horiz.: "%s"',EEG.chanlocs(right_eye_xy(1)).labels); fprintf('\n\tRight verti.: "%s"',EEG.chanlocs(right_eye_xy(2)).labels); end if smooth, fprintf('\n-- Raw data is smoothed in 5-sample window.'); else fprintf('\n-- Raw data is not smoothed.'); end if nepochs > 1 if globalthresh fprintf('\n-- Velocity thresholds computed globally across all %i epochs',nepochs); else fprintf('\n-- Velocity thresholds computed individually for each epoch'); end end fprintf('\n-- Treatment of saccade clusters:'); switch clustermode case 1 fprintf('\n\tAll saccades are kept'); case 2 fprintf('\n\tSaccades separated by fixations < %i ms are clustered.',clusterdist(1000/EEG.srate)); fprintf('\n\tFirst sacc. of each cluster is kept.'); case 3 fprintf('\n\tSaccades separated by fixations < %i ms are clustered.',clusterdist*(1000/EEG.srate)); fprintf('\n\tLargest sacc. of each cluster is kept.'); case 4 fprintf('\n\tSaccades separated by fixations < %i ms are clustered.',clusterdist*(1000/EEG.srate)); fprintf('\n\tClusters are combined into one saccade.'); otherwise error('%s(): Unknown input for clustermode, should be: 1,2,3,4.',mfilename) end if plotfig, fprintf('\n-- A figure with eye movement properties is plotted.'); end if writesac && writefix, fprintf('\n-- Saccades and fixations will be added to EEG.event.'); elseif writesac, fprintf('\n-- Saccades will be added to EEG.event.'); elseif writefix, fprintf('\n-- Fixations will be added to EEG.event.'); else, fprintf('\n-- Saccades and fixations are detected, but NOT stored anywhere.'); end end
%% get "bad_ET" intervals from EEG.event structure badvector = zeros(1,size(EEG.data,2)*size(EEG.data,3)); ix_badETevent = find(ismember({EEG.event.type},'bad_ET')); if ~isempty(ix_badETevent)
fprintf('\n--------------------------------------------------------------------') fprintf('\nFound "bad_ET" events in EEG.event.\nThese intervals will be ignored for saccade detection!') fprintf('\n--------------------------------------------------------------------') bad_lat = [EEG.event(ix_badETevent).latency]; bad_dur = [EEG.event(ix_badETevent).duration]; bad_ET = [bad_lat; bad_dur]'; bad_ET(:,3) = bad_ET(:,1)+bad_ET(:,2)-1;
% create long vector (as long as EEG) indicating bad samples for j = 1:size(bad_ET,1) badvector(bad_ET(j,1):bad_ET(j,3)) = 1; end end % reshape "badvector" to 3D if data is already epoched badvector = reshape(badvector,1,size(EEG.data,2),size(EEG.data,3));
%% pre-compute saccade velocity thresholds for all epochs % this enables the option to use the same threshold for all epochs, not % provided by the original Engbert & Mergenthaler implementation % new 03/2018: exclude bad ET intervals from velocity estimation fprintf('\nComputing adaptive velocity thresholds...') % if any(badvector(:)) % fprintf('\n-- Found %i "bad_ET" events marking bad eye-tracking intervals in EEG.event.',length(ix_badETevent)) % fprintf('\n-- These intervals (%.2f%% of data) will be ignored when computing velocity thresholds.',(sum(badvector)/length(badvector(:))*100)) % end for e=1:nepochs ix_goodET = ~badvector(:,:,e); % get index of non-bad ET samples if ldata l = EEG.data([left_eye_xy(1) left_eye_xy(2)],ix_goodET,e)'; vl = vecvel(l,EEG.srate,smoothlevel); [l_msdx(e), l_msdy(e)] = velthresh(vl); end if rdata r = EEG.data([right_eye_xy(1) right_eye_xy(2)],ix_goodET,e)'; vr = vecvel(r,EEG.srate,smoothlevel); [r_msdx(e), r_msdy(e)] = velthresh(vr); end end
%% detect saccades & fixations for e=1:nepochs
%% saccades of left eye
sac = [];
if ldata
l = EEG.data([left_eye_xy(1) left_eye_xy(2)],:,e)'; % don't exclude bad_ET samples here (otherwise discont. high-velocity jumps in time series)
% bad/missing samples in eye track?
badsmp = sum(sum(l<=0)); if badsmp > 0, badepochs(e) = 1; nbadsmp = nbadsmp + badsmp; end
vl = vecvel(l,EEG.srate,smoothlevel); % get eye velocities
% detect monocular saccades
if globalthresh % use precomputed velocity thresholds (mean of all epochs)
sacL = microsacc_plugin(l,vl,vfac,mindur,mean(l_msdx),mean(l_msdy));
else % compute velocity thresholds from this epoch only
sacL = microsacc_plugin(l,vl,vfac,mindur,l_msdx(e),l_msdy(e));
end
end
%% saccades of right eye
if rdata
r = EEG.data([right_eye_xy(1) right_eye_xy(2)],:,e)';
% bad/missing samples in eye track?
badsmp = sum(sum(r<=0)); if badsmp > 0, badepochs(e) = 1; nbadsmp = nbadsmp + badsmp; end
vr = vecvel(r,EEG.srate,smoothlevel); % get eye velocities
% detect monocular saccades
if globalthresh % use precomputed velocity thresholds (mean of all epochs)
sacR = microsacc_plugin(r,vr,vfac,mindur,mean(r_msdx),mean(r_msdy));
else
% compute velocity thresholds from this epoch only
sacR = microsacc_plugin(r,vr,vfac,mindur,r_msdx(e),r_msdy(e));
end
end
%% binocular saccades
if ldata && rdata
[sac, tmp, tmp] = binsacc(sacL,sacR); clear tmp
sac = saccpar(sac); % average saccade characteristics of both eyes
sac = mergesacc(sac,(l+r)./2,clusterdist,clustermode); % merge nearby saccades (e.g. glissades)
elseif ldata
sac = sacL; clear sacL;
sac = saccpar([sac sac]);
sac = mergesacc(sac,l,clusterdist,clustermode);
elseif rdata
sac = sacR; clear sacR;
sac = saccpar([sac sac]);
sac = mergesacc(sac,r,clusterdist,clustermode);
end
%% update various saccade metrics if ~isempty(sac)
% define saccade duration as difference between saccade offset and
% saccade onset sample. In saccpar(), monocular saccade durations
% of both eyes are averaged, leading to uneven values (e.g.: 10.5
% samples) different from the difference between onset and offset
% values (which are the monocular extremes).
% Instead: use difference between offset and onset
sac(:,3) = sac(:,2)-sac(:,1)+1;
% report saccade velocity/distance/amplitude as visual angles
sac(:,[5 6 8]) = sac(:,[5 6 8]) .* degperpixel;
% report saccade angles in degree rather than radians
sac(:,[7 9]) = sac(:,[7 9]) * 180/pi;
% add index of corresp. data epoch
sac(:,10) = e;
% store screen location for start and end of saccade
if ldata && rdata
gazexy = (l+r)./2; % binoc. recordings: average across eyes
elseif ldata
gazexy = l;
elseif rdata
gazexy = r;
end
% get position immediatly before sacc. onset and after sacc. offset
startsmp = sac(:,1)-1; endsmp = sac(:,2)+1;
if startsmp(1) < 1, startsmp(1,1) = 1; end
if endsmp(end) > size(gazexy,2), endsmp(end) = size(gazexy,2); end
sac(:,11) = gazexy(startsmp,1);
sac(:,12) = gazexy(startsmp,2);
sac(:,13) = gazexy(endsmp ,1);
sac(:,14) = gazexy(endsmp ,2);
end
% columns of [sac]: % 1: saccade onset (sample) % 2: saccade offset (sample) % 3: duration (samples) % 4: delay between eyes (samples) % 5: vpeak (peak velocity) % 6: saccade "distance" (eucly. dist. between start and endpoint) % 7: saccade angle (based on saccade "distance") % 8: saccade "amplitude" (eucly. dist. of min/max in full saccade trajectory) % 9: saccade angle (based on saccade "amplitude") %10: index of corresponding data epoch (1 in case of contin. data) %11: horizontal (x) gaze position before start of saccade (pixel) %12: vertial (y) gaze position before start of saccade (pixel) %13: horizontal (x) gaze position after end of saccade (pixel) %14: vertical (y) gaze position after end of saccade (pixel)
%% remove saccades that occured during "bad_ET" intervals [] % Delete all saccades whos onset or offset occurs during a % bad_ET interval (signal jumps to blinks are otherwise detected as % saccades) % % [] Note: It is not clear/trivial how to treat blinks in the context % of EM detection, since we do not know what really happened during a % blink or loss of the signal. For example, is a long fixation with a % blink in the middle really two fixations? Or should it be treated as % one long fixation? Or should all fixations that are ended or started % by blinks or are interrupted by them be completely removed? % The solution here is preliminary: I first remove all saccades that % started or ended during a blink interval. The remaining saccades are % used to define fixatinos. Finally, all fixations are removed that % overlap with "bad_ET" intervals in EEG.event. So this approach % removes any eye movement event that overlaps with a bad_ET interval. % OD, 2018-03-08
%% delete saccades starting/ending during "bad_ET" intervals badETsmp = find(badvector(:,:,e)); if ~isempty(sac) ix_fakesac = find(ismember(sac(:,1),badETsmp) | ismember(sac(:,2),badETsmp)); sac(ix_fakesac,:) = []; % if ~isempty(ix_fakesac) % fprintf('\n\n-- Removed %i saccades that occured during "bad_ET" intervals',length(ix_fakesac)) % end end
%% get fixations nsac = size(sac,1); fix = []; if ~isempty(sac) if nsac > 1 for f = 1:nsac-1
fix(f,1) = sac(f,2)+1;
fix(f,2) = sac(f+1,1)-1;
% catch special case: if Engbert algorithms are applied
% without any saccade clustering [>> mergesacc()] there can
% be back-to-back saccades with intervening "fixations" of
% zero sample duration. Catch this by setting the duration
% of these fixations to one sample
if fix(f,1) > fix(f,2)
fix(f,1) = fix(f,2); % 1-sample fixation
clusterwarning = true;
end
end
end
% if epoch does not begin with saccade, add first fixation
if sac(1,1) > 1
fix = [[1 sac(1,1)-1]; fix];
end
% if epoch does not end with saccade, add last fixation
if sac(end,2) < nsample
fix = [fix;[sac(end,2)+1 nsample]];
end
% add more fixation properties
for f = 1:size(fix,1)
% fixation duration (in samples!)
fix(f,3) = fix(f,2)-fix(f,1)+1;
% mean fix. position: left eye
if ldata
fix(f,4) = mean( l(fix(f,1):fix(f,2),1) );
fix(f,5) = mean( l(fix(f,1):fix(f,2),2) );
else
fix(f,4) = NaN;
fix(f,5) = NaN;
end
% mean fix. position: right eye
if rdata
fix(f,6) = mean( r(fix(f,1):fix(f,2),1) );
fix(f,7) = mean( r(fix(f,1):fix(f,2),2) );
else
fix(f,6) = NaN;
fix(f,7) = NaN;
end
% binocular fixation position
fix(f,8) = nanmean(fix(f,[4 6]));
fix(f,9) = nanmean(fix(f,[5 7]));
end
fix(:,10) = e; % add index of corresp. data epoch
% recompute latencies of eye movement events
% (only necessary for epoched data)
offset = (e-1)*nsample;
sac(:,[1 2]) = sac(:,[1 2])+offset;
% special case: single sacc. lasts entire epoch
if ~isempty(fix)
fix(:,[1 2]) = fix(:,[1 2])+offset;
end
end
% columns of [fix]: % 1: fixation onset (sample) % 2: fixation offset (sample) % 3: duration (samples) % 4: mean fix position (L eye X) % 5: mean fix position (L eye Y) % 6: mean fix position (R eye X) % 7: mean fix position (R eye Y) % 8: mean fix position (L/R average X) % 9: mean fix position (L/R average Y) %10: index of corresponding data epoch (1 in case of contin. data)
%% remove fixations overlapping with "bad_ET" intervals % this includes fixations interrupted by a blink (!) % development note: loop is slow, need to implement more efficiently if ~isempty(fix) & any(badvector(:)) badfix = false(size(fix,1),1); % preallocate logical
% go tru fixations, check whether "bad"
for p = 1:size(fix,1)
fixsmp = fix(p,1):fix(p,2);
if any(ismember(fixsmp,badETsmp))
badfix(p) = true;
end
end
% remove fixations overlapping with "bad_ET" intervals
fix(badfix,:) = [];
% if any(badfix)
% fprintf('\n-- Removed %i fixations that overlapped with "bad_ET" intervals',sum(badfix))
% end
end
% slow, but simple: allsac = [allsac;sac]; allfix = [allfix;fix]; end % epoch loop
%% remove artificial eye movements caused by boundaries (data breaks) % Remove all EMs whose onset is detected in temporal proximity to boundary. % Otherwise, data breaks will likely result in additional fake sacc./fix. % Applies only if eye movements are detected in the continuous data if nepochs == 1
ix_bnd = find(cellfun(@(x) strcmp(x,'boundary'),{EEG.event.type})); % bug fix: now robust against numeric types
% any data breaks? % (due to manual rejections or function pop_rej_eyecontin) if ~isempty(ix_bnd)
% minimum distance from data break in milliseconds (hard-coded)
BOUNDDIST_MS = 50;
BOUNDDIST = round(BOUNDDIST_MS / (1000/EEG.srate));
% data break latencies
bound_lats = round([EEG.event(ix_bnd).latency]);
% mark all samples close to data break
boundvector = zeros(1,EEG.pnts);
for b = 1:length(bound_lats)
lowr = bound_lats(b)-BOUNDDIST;
uppr = bound_lats(b)+BOUNDDIST;
if lowr <= 0, lowr = 1; end
if uppr > EEG.pnts, uppr = EEG.pnts; end
boundvector(lowr:uppr) = 1;
end
nearboundsmp = find(boundvector);
% option 1: event onset is close to boundary
% fakesac = find(ismember(allsac(:,1),nearboundsmp));
% fakefix = find(ismember(allfix(:,1),nearboundsmp));
% option 2: event on- or offset is close to boundary
fakesac = find(ismember(allsac(:,1),nearboundsmp) | ismember(allsac(:,2),nearboundsmp));
fakefix = find(ismember(allfix(:,1),nearboundsmp) | ismember(allfix(:,2),nearboundsmp));
allsac(fakesac,:) = [];
allfix(fakefix,:) = [];
fprintf('\n--------------------------------------------------------------------');
fprintf('\nFound %i data breaks (boundary events) in the continuous data',length(ix_bnd));
fprintf('\nRemoving eye movements that might be artifacts of data breaks:');
fprintf('\nRemoved %i saccades < %i ms away from a boundary',length(fakesac),BOUNDDIST_MS);
fprintf('\nRemoved %i fixations < %i ms away from a boundary',length(fakefix),BOUNDDIST_MS);
end end
if clusterwarning && clustermode == 1 fprintf('\n--------------------------------------------------------------------'); fprintf('\n\n*** WARNING! ***\nDetected pairs of saccades that ended/started on adjacent data samples.'); fprintf('\nYou should probably use saccade clustering! (clustermode: 2,3,4)'); end
%% user feedback if bad eye-tracking data (values <= 0) but no bad_ET events if sum(badepochs)>0 & isempty(ix_badETevent) warning('\nI found bad or missing data (i.e. values <= 0) in the ET channels but no "bad_ET" events!'); fprintf('\nDetails:'); fprintf('\n-- %i of %i epochs contained gaze position values <= 0',sum(badepochs),nepochs); fprintf('\n-- Total number of bad samples <= 0: %i',nbadsmp); fprintf('\n-- Did you detect or reject bad intervals with out-of-range values (''Reject data based on eyetrack'')?'); fprintf('\n-- Did you subtract a baseline from eye channels (explaining negative values)?'); fprintf('\n-- Note that unmarked blinks or missing data in the eye-track will...:') fprintf('\n ...distort the velocity threshold for saccade detection!'); fprintf('\n ...be erroneously detected as additional saccades/fixations!'); end
%% user feedback: saccade & fixation detection fprintf('\n--------------------------------------------------------------------'); fprintf('\nVelocity thresholds used:'); if nepochs > 1, fprintf(' (mean across epochs):'); end; if ldata, fprintf('\n\tLeft eye. Horiz.: %.2f %s/s. Vert.: %.2f %s/s',mean(l_msdx(e)vfacdegperpixel),metric,mean(l_msdyvfacdegperpixel),metric); end if rdata, fprintf('\n\tRight eye. Horiz.: %.2f %s/s. Vert.: %.2f %s/s',mean(r_msdx(e)vfacdegperpixel),metric,mean(r_msdyvfacdegperpixel),metric); end fprintf('\n--------------------------------------------------------------------') if ~isempty(allsac) fprintf('\n%i saccades detected:',size(allsac,1)); fprintf('\n\tMedian amplitude: %.2f %s',median(allsac(:,6)),metric); fprintf('\n\tMedian duration: %.2f ms',median(allsac(:,3))*1000/EEG.srate); fprintf('\n\tMedian peak veloc. %.2f %s/s',median(allsac(:,5)),metric); end if ~isempty(allfix) fprintf('\n%i fixations detected:',size(allfix,1)); fprintf('\n\tMedian duration: %.2f ms',median(allfix(:,3))*1000/EEG.srate); if ldata,fprintf('\n\tMedian fix. pos. left eye: Horiz.: %.2f px. Vert.: %.2f px',median(allfix(:,4)),median(allfix(:,5))); end if rdata,fprintf('\n\tMedian fix. pos. right eye: Horiz.: %.2f px. Vert.: %.2f px',median(allfix(:,6)),median(allfix(:,7))); end end
%% plot figure with eye movements properties if plotfig if ldata && rdata fprintf('\n--------------------------------------------------------------------') fprintf('\nPlotting eye movement properties...\nFor plotting, fixation locations are averaged across eyes.') ploteyemovements(allsac(:,6),allsac(:,5),allsac(:,7),allfix(:,3)*1000/EEG.srate,mean(allfix(:,[4 6]),2),mean(allfix(:,[5 7]),2),metric); elseif ldata fprintf('\n--------------------------------------------------------------------') fprintf('\nPlotting eye movement properties of left eye...') ploteyemovements(allsac(:,6),allsac(:,5),allsac(:,7),allfix(:,3)*1000/EEG.srate,allfix(:,4),allfix(:,5),metric); else fprintf('\n--------------------------------------------------------------------') fprintf('\nPlotting eye movement properties of right eye...') ploteyemovements(allsac(:,6),allsac(:,5),allsac(:,7),allfix(:,3)*1000/EEG.srate,allfix(:,6),allfix(:,7),metric); end end
%% write eye movements to EEG.event & EEG.urevent if writefix || writesac
% check: are there already eye movements events in EEG.event? em_types = {'saccade','fixation','L_saccade','R_saccade','L_fixation','R_fixation'}; if any(cellfun(@(x) any(strcmp(x,em_types)),{EEG.event.type})) % updated [v.0.337] fprintf('\n--------------------------------------------------------------------') fprintf('\n*** WARNING! ***:\nFound existing eye movement events in EEG.event!'); fprintf('\nNew events will be added to the already existing events!'); end
% write saccades if writesac fprintf('\n--------------------------------------------------------------------') fprintf('\nAdding %i saccades to EEG.event...\n',size(allsac,1)) EEG = addevents(EEG,allsac(:,[1 3 5 6 7 10 11:14]),{'latency','duration','sac_vmax','sac_amplitude','sac_angle','epoch','sac_startpos_x','sac_startpos_y','sac_endpos_x','sac_endpos_y'},'saccade'); end
% write fixations
if writefix
fprintf('\n--------------------------------------------------------------------')
fprintf('\nAdding %i fixations to EEG.event...\n',size(allfix,1))
if ldata && rdata % binocular
EEG = addevents(EEG,allfix(:,[1 3 8 9 10]),{'latency','duration','fix_avgpos_x','fix_avgpos_y','epoch'},'fixation');
%EEG = addevents(EEG,allfix(:,[1 3 4 5 6 7 10]),{'latency','duration','fixposition_ly','fixposition_lx','fixposition_ry','fixposition_rx','epoch'},'fixation');
elseif ldata % left eye recorded
EEG = addevents(EEG,allfix(:,[1 3 4 5 10]),{'latency','duration','fix_avgpos_x','fix_avgpos_y','epoch'},'fixation');
elseif rdata % right eye recorded
EEG = addevents(EEG,allfix(:,[1 3 6 7 10]),{'latency','duration','fix_avgpos_x','fix_avgpos_y','epoch'},'fixation');
end
end
fprintf('--------------------------------------------------------------------')
end
fprintf('\nDone.\n\n')
end