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EyeTrackerAnalysisRecord.m
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EyeTrackerAnalysisRecord.m
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classdef EyeTrackerAnalysisRecord < handle
%% class EyeTrackerAnalysisRecord
% Constitutes an .ETA file.
% Saves:
% * eye tracking raw data.
% * previous segmentizations results.
% * blinks logical vector (a vector with length equal to the number of samples, where
% vector(i) = 1 iff sample i is within a blink and vector(i) = 0 otherwise.
% * eyeballing data.
properties (Access= public, Constant)
CONDS_NAMES_PREFIX= 'C';
ENUM_DETECTION_BINOCULAR = 0;
ENUM_DETECTION_MONOCULAR_L = 1;
ENUM_DETECTION_MONOCULAR_R = 2;
end
properties (Access= private, Constant)
% relative path of readEDF.mexw64 file with which .edf files are read into
% a MATLAB struct
READ_EDF_PATH= fullfile('readEDF');
% parameters for the pupils based blinks detection algorithm
PUPILS_BASED_BLINKS_DETECTION_STD = 2.5;
PUPILS_BASED_BLINKS_DETECTION_CONSECUTIVE_SAMPLES = 3;
PUPILS_BASED_BLINKS_DETECTION_TOLERANCE = 3;
PUPILS_BASED_BLINKS_DETECTION_MAX_SEG_TIME = 10;
end
properties (Access= private)
% a string given in the constructor. can be used to identify this analysis record.
analysis_tag;
% raw eye tracking data
eye_tracker_data_structs;
% boolean indicating eeg data is present
is_eeg_involved;
% cell array of structs holding the parameters for the corresponding segmentizations
% data stored in segmentization_vecs. serves as a lookup index to restore previously
% performed segmentizations stored in segmentization_vecs.
segmentization_vecs_index= {};
% cell array of structs holding event timings. An event is either a blink or a message
% sent during the eye racking recording session.
segmentization_vecs= {};
% cell array of structs holding eyeballing data for the corresponding segmentizations
% data stored in segmentization_vecs.
saccades_extractors_data= {};
% the index in the above cell arrays of the chosen segmentization.
chosen_segmentization_i= 0;
% degrees per pixels of the eye tracking recording.
dpp = [];
% sampling rate of the eye tracking recording.
sampling_rate = [];
end
methods (Access= public)
function obj= EyeTrackerAnalysisRecord(progress_screen, progress_contribution, analysis_tag, eye_tracker_files, dpp)
% EyeTrackerAnalysisRecord ctor.
% Input:
% * progress_screen -> handle to a DualBarProgressScreen object.
% * progress_contribution -> factor to scale progress with.
% * analysis_tag -> A string that can be used to identify this analysis record.
% * eye_tracker_files -> cell array of the full paths for the eye tracking data files.
% * dpp -> degrees per pixels of the eye tracking recording.
obj.analysis_tag= analysis_tag;
obj.dpp = dpp;
if ~iscell(eye_tracker_files)
eye_tracker_files= {eye_tracker_files};
end
eye_tracker_files_nr= numel(eye_tracker_files);
obj.is_eeg_involved= false;
obj.eye_tracker_data_structs= {};
for eye_tracker_file_i= 1:eye_tracker_files_nr
curr_eye_tracker_full_file_name= eye_tracker_files{eye_tracker_file_i};
[~, eye_tracker_file_name, eye_tracker_file_ext]= fileparts(curr_eye_tracker_full_file_name);
if strcmpi(eye_tracker_file_ext, '.edf')
% file is an eyelink eye tracking data
copyfile(curr_eye_tracker_full_file_name, EyeTrackerAnalysisRecord.READ_EDF_PATH);
progress_screen.displayMessage(['converting session #', num2str(eye_tracker_file_i), ' edf file']);
addpath(EyeTrackerAnalysisRecord.READ_EDF_PATH);
% read the eye tracking data with readEDF.mexw64
extracted_struct = readEDF(fullfile(EyeTrackerAnalysisRecord.READ_EDF_PATH, [eye_tracker_file_name, '.edf']));
extracted_struct = rmfield(extracted_struct, 'fixations');
extracted_struct = rmfield(extracted_struct, 'saccades');
extracted_struct.gazeLeft = rmfield(extracted_struct.gazeLeft, 'pix2degX');
extracted_struct.gazeLeft = rmfield(extracted_struct.gazeLeft, 'pix2degY');
extracted_struct.gazeLeft = rmfield(extracted_struct.gazeLeft, 'velocityX');
extracted_struct.gazeLeft = rmfield(extracted_struct.gazeLeft, 'velocityY');
extracted_struct.gazeLeft = rmfield(extracted_struct.gazeLeft, 'whichEye');
extracted_struct.gazeRight = rmfield(extracted_struct.gazeRight, 'pix2degX');
extracted_struct.gazeRight = rmfield(extracted_struct.gazeRight, 'pix2degY');
extracted_struct.gazeRight = rmfield(extracted_struct.gazeRight, 'velocityX');
extracted_struct.gazeRight = rmfield(extracted_struct.gazeRight, 'velocityY');
extracted_struct.gazeRight = rmfield(extracted_struct.gazeRight, 'whichEye');
extracted_structs = {extracted_struct};
delete(fullfile(EyeTrackerAnalysisRecord.READ_EDF_PATH, [eye_tracker_file_name, '.edf']));
rmpath(EyeTrackerAnalysisRecord.READ_EDF_PATH);
elseif strcmp(eye_tracker_file_ext, '.mat')
% file is a previously generated eye tracking data struct
progress_screen.displayMessage(['loading session #', num2str(eye_tracker_file_i), ' mat file']);
loaded_mat= load(curr_eye_tracker_full_file_name);
extracted_structs = EyeTrackerAnalysisRecord.extractEyeTrackerStructsFromLoadedMatStructs(loaded_mat);
if isempty(extracted_structs)
error('EyeTrackerAnalysisRecord:InvalidMat', [eye_tracker_file_name, '.mat does not contain an eyelink data struct.']);
end
elseif strcmp(eye_tracker_file_ext, '.set')
% file is an EEGLAB data
extracted_structs = {EyeTrackerAnalysisRecord.addEtaFieldsToEegStruct(pop_loadset(curr_eye_tracker_full_file_name))};
obj.is_eeg_involved= true;
end
if eye_tracker_file_i == 1
obj.sampling_rate = EyeTrackerAnalysisRecord.compSamplingRate(extracted_structs{1}.gazeRight.time);
else
for session_i = 1:numel(extracted_structs)
curr_session_sampling_rate = EyeTrackerAnalysisRecord.compSamplingRate(extracted_structs{session_i}.gazeRight.time);
if curr_session_sampling_rate ~= obj.sampling_rate
error('EyeTrackerAnalysisRecord:InvalidMat', [eye_tracker_file_name, 'contains data with different sampling rate (', num2str(curr_session_sampling_rate), ' Hz) than does the first session file (', num2str(obj.sampling_rate), ' Hz) - sessions with different sampling rates are not supported.']);
end
end
end
progress_screen.addProgress(progress_contribution/eye_tracker_files_nr);
obj.eye_tracker_data_structs= [obj.eye_tracker_data_structs, extracted_structs];
end
end
function was_previous_segmentization_loaded = segmentizeData(obj, progress_screen, progress_contribution, trial_onset_triggers, trial_offset_triggers, trial_rejection_triggers, baseline, post_offset_triggers_segment, trial_dur, blinks_delta, blink_detection_algos_flags)
segmentizations_nr= numel(obj.segmentization_vecs);
for segmentization_i= 1:segmentizations_nr
if isempty( setxor(obj.segmentization_vecs_index{segmentization_i, 1}, trial_onset_triggers) ) && ...
obj.segmentization_vecs_index{segmentization_i, 2} == trial_dur && ...
obj.segmentization_vecs_index{segmentization_i, 3} == baseline && ...
obj.segmentization_vecs_index{segmentization_i, 4} == blinks_delta && ...
isempty( setxor(obj.segmentization_vecs_index{segmentization_i, 5}, trial_offset_triggers) ) && ...
( (isempty(obj.segmentization_vecs_index{segmentization_i, 6}) && isempty(post_offset_triggers_segment) ) || ...
(~isempty(obj.segmentization_vecs_index{segmentization_i, 6}) && ~isempty(post_offset_triggers_segment)) && obj.segmentization_vecs_index{segmentization_i, 6} == post_offset_triggers_segment ) && ...
isempty( setxor(obj.segmentization_vecs_index{segmentization_i, 7}, trial_rejection_triggers) )
obj.chosen_segmentization_i= segmentization_i;
was_previous_segmentization_loaded= true;
progress_screen.addProgress(progress_contribution);
return;
end
end
was_previous_segmentization_loaded = false;
sessions_nr = numel(obj.eye_tracker_data_structs);
for session_i= 1:sessions_nr
curr_session_eye_tracker_data_struct= obj.eye_tracker_data_structs{session_i};
progress_screen.displayMessage(['session #', num2str(session_i), ': indexing blinks']);
if blink_detection_algos_flags(1)
eyelink_based_blinks_vec = EyeTrackerAnalysisRecord.eyelinkBased_blinkdetection(curr_session_eye_tracker_data_struct, blinks_delta, progress_screen, 0.4*progress_contribution/sessions_nr);
else
eyelink_based_blinks_vec = zeros(1, length(curr_session_eye_tracker_data_struct.gazeRight.time));
progress_screen.addProgress(0.4*progress_contribution/sessions_nr);
end
if blink_detection_algos_flags(2)
pupils_based_blinks_vec = EyeTrackerAnalysisRecord.pupilBased_blinkdetection_twoEyes(curr_session_eye_tracker_data_struct.gazeRight.pupil, curr_session_eye_tracker_data_struct.gazeLeft.pupil, obj.sampling_rate, obj.PUPILS_BASED_BLINKS_DETECTION_STD, obj.PUPILS_BASED_BLINKS_DETECTION_CONSECUTIVE_SAMPLES, obj.PUPILS_BASED_BLINKS_DETECTION_TOLERANCE, blinks_delta, obj.PUPILS_BASED_BLINKS_DETECTION_MAX_SEG_TIME, progress_screen, 0.4*progress_contribution/sessions_nr);
else
pupils_based_blinks_vec = zeros(1, length(curr_session_eye_tracker_data_struct.gazeRight.time));
progress_screen.addProgress(0.4*progress_contribution/sessions_nr);
end
obj.segmentization_vecs{segmentizations_nr+1}(session_i).blinks= eyelink_based_blinks_vec | pupils_based_blinks_vec;
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_start_times = [];
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_end_times = [];
triggers_nr= numel(trial_onset_triggers);
for trigger_i= 1:triggers_nr
progress_screen.displayMessage(['session #', num2str(session_i), ': segmentizing data by condition ', trial_onset_triggers{trigger_i}]);
% First looking for triggers within messages
[start_times, end_times, ~] = extractSegmentsTimesFromMessages(curr_session_eye_tracker_data_struct, trial_onset_triggers{trigger_i});
% If we did not find the triggers within messages, trying instead to look for them in inputs
if isempty(start_times)
[start_times, end_times, ~] = extractSegmentsTimesFromInputs(curr_session_eye_tracker_data_struct, trial_onset_triggers{trigger_i});
if isempty(start_times)
%obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_start_times.(curr_cond_field_name)= [];
%obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_end_times.(curr_cond_field_name)= [];
progress_screen.displayMessage(['session #', num2str(session_i), ':Didn''t find trigger ', '''', trial_onset_triggers{trigger_i}, '''']);
progress_screen.addProgress(0.2*progress_contribution/(sessions_nr*triggers_nr));
continue;
end
end
%assign trials timings
trials_nr= numel(start_times);
curr_cond_field_name = convertMsgToValidFieldName([EyeTrackerAnalysisRecord.CONDS_NAMES_PREFIX, sprintf('%02d', trigger_i), '_', trial_onset_triggers{trigger_i}]);
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_start_times.(curr_cond_field_name)= NaN(trials_nr, 1);
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_end_times.(curr_cond_field_name)= NaN(trials_nr, 1);
session_samples_nr = numel(curr_session_eye_tracker_data_struct.gazeLeft.time);
% Going over each trial (segment) found from triggers for the current condition (trigger)
for trial_i=1:trials_nr
% Getting start of gaze data for segment
indStart= find(ismember(curr_session_eye_tracker_data_struct.gazeLeft.time, start_times(trial_i) + (0 : (1000/obj.sampling_rate - 1))), 1);
if isempty(indStart)
% No matching sample found. This could be the case when recording started after the baseline requested. Warning user
progress_screen.displayMessage(['<<WARNING (on session #', num2str(session_i), '): no corresponding starting sample found for trial ', num2str(trial_i), ', consider checking if requested baseline is correct>>']);
continue;
end
% Getting end of gaze data for segment
indEnd = find(ismember(curr_session_eye_tracker_data_struct.gazeLeft.time, end_times(trial_i) + (0 : (1000/obj.sampling_rate - 1))), 1);
if isempty(indEnd)
% No matching sample found. This could be the case when recording started after the baseline requested. Warning user
progress_screen.displayMessage(['<<WARNING (on session #', num2str(session_i), '): no corresponding end sample found for trial ', num2str(trial_i), ', consider checking if requested post-trigger segment duration is correct>>']);
continue;
end
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_start_times.(curr_cond_field_name)(trial_i) = indStart;
obj.segmentization_vecs{segmentizations_nr+1}(session_i).trials_end_times.(curr_cond_field_name)(trial_i) = indEnd;
end
progress_screen.addProgress(0.2*progress_contribution/(sessions_nr*triggers_nr));
end
end
obj.segmentization_vecs_index{segmentizations_nr+1, 1}= trial_onset_triggers;
obj.segmentization_vecs_index{segmentizations_nr+1, 2}= trial_dur;
obj.segmentization_vecs_index{segmentizations_nr+1, 3}= baseline;
obj.segmentization_vecs_index{segmentizations_nr+1, 4}= blinks_delta;
obj.segmentization_vecs_index{segmentizations_nr+1, 5}= trial_offset_triggers;
obj.segmentization_vecs_index{segmentizations_nr+1, 6}= post_offset_triggers_segment;
obj.segmentization_vecs_index{segmentizations_nr+1, 7}= trial_rejection_triggers;
obj.saccades_extractors_data{segmentizations_nr+1}= [];
obj.chosen_segmentization_i= numel(obj.segmentization_vecs);
function msg = convertMsgToValidFieldName(msg)
msg(ismember(msg,' `!@#$%^&*()-+=[]{}''";:,.<>/?\|')) = '_';
if isstrprop(msg(1),'digit')
msg = [EyeTrackerAnalysisRecord.CONDS_NAMES_PREFIX, msg];
end
% 63 -> matlab's maximum allowed variable name length
if numel(msg) > 63
msg = msg(1:63);
end
end
function [start_times, end_times, curr_cond_field_name] = extractSegmentsTimesFromMessages(eye, trial_onset_trigger)
% search phases:
% 1 - trial onset
% 2 - trial offset
are_offset_triggers_included = ~isempty(trial_offset_triggers);
start_times= [];
end_times = [];
curr_cond_field_name = '';
field_i = 1;
search_phase = 1;
% Going over each message at a time
while field_i <= numel(eye.messages)
msg = eye.messages(field_i).message; % Getting message content
if isempty(msg) % Skipping empty messages
field_i = field_i + 1;
continue;
end
msg_time = eye.messages(field_i).time; % Getting message timestamp
if search_phase == 1 % If looking for trial onset message
% Checking if current message matches trigger list
if EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(msg, trial_onset_trigger)
% Recording trial start time and message content
potential_trial_start_time = msg_time;
potential_trial_start_msg = msg;
search_phase = 2; % Starting to look for trial offset
end
elseif (are_offset_triggers_included && msg_time - potential_trial_start_time > trial_dur - baseline) || ... % If using trigger offsets and more time elapsed from onset trigger than permitted
(any(cellfun(@(str) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(msg, str), trial_rejection_triggers))) % Or if found a rejection trigger before finding an offset trigger
search_phase = 1; % Offseting flag to search for new trials
elseif (are_offset_triggers_included && any(cellfun(@(str) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(msg, str), trial_offset_triggers))) || ... % If using offset triggers and we found one
(~are_offset_triggers_included && (any(cellfun(@(str) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(msg, str), trial_onset_triggers)) || ... % Or we don't use offset trigger and we found a new onset trigger
msg_time - potential_trial_start_time > trial_dur - baseline)) % Or we don't use offset trigger and enough time elapsed to create a new segment
% Declaring this as a new segment
search_phase = 1; % Offseting flag to search for new trials next round
% Adding saved timestamp to segment start times
start_times= [start_times, potential_trial_start_time - baseline]; %#ok<AGROW>
% If this is the first time we encounter this trigger, adding to list of condition names
if isempty(regexp(curr_cond_field_name, ['(^|_)', potential_trial_start_msg, 'X'], 'ONCE'))
curr_cond_field_name = [curr_cond_field_name, potential_trial_start_msg, 'X']; %#ok<AGROW>
end
% Finding trigger end times
if are_offset_triggers_included
% If using trigger offset, using the msg time of the current found trigger + offset trigger segment duration
end_times = [end_times, msg_time + post_offset_triggers_segment]; %#ok<AGROW>
elseif msg_time - potential_trial_start_time < trial_dur - baseline
% If not using offset triggers but found another onset trigger, setting end time as the current timestamp
end_times = [end_times, msg_time]; %#ok<AGROW>
continue;
else
% If not using offset triggers and no consecutive onset triggers were found, using the previous onset + the trial duration minus baseline as offset
end_times = [end_times, potential_trial_start_time + trial_dur - baseline]; %#ok<AGROW>
continue;
end
end
field_i = field_i + 1;
end
% Handling case for when searching for an offset trigger and message list ended
if search_phase == 2
start_times= [start_times, potential_trial_start_time - baseline];
if are_offset_triggers_included
end_times = [end_times, min(potential_trial_start_time + trial_dur + post_offset_triggers_segment, eye.gazeLeft.time(end))];
else
end_times = [end_times, min(potential_trial_start_time + trial_dur, eye.gazeLeft.time(end))];
end
end
if ~isempty(curr_cond_field_name)
curr_cond_field_name(end) = '';
curr_cond_field_name = convertMsgToValidFieldName([EyeTrackerAnalysisRecord.CONDS_NAMES_PREFIX, curr_cond_field_name]);
end
end
function [start_times, end_times, curr_cond_field_name] = extractSegmentsTimesFromInputs(eye, trial_onset_trigger)
% search phases:
% 1 - trial onset
% 2 - trial offset
are_offset_triggers_included = ~isempty(trial_offset_triggers);
start_times= [];
end_times = [];
curr_cond_field_name = '';
field_i = 1;
search_phase = 1;
while field_i <= numel(eye.inputs)
input = num2str(eye.inputs(field_i).input);
if isempty(input)
field_i = field_i + 1;
continue;
end
input_time = eye.inputs(field_i).time;
if search_phase == 1
if EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(input, trial_onset_trigger)
potential_trial_start_time = input_time;
potential_trial_start_input = input;
search_phase = 2;
end
elseif (are_offset_triggers_included && any(cellfun(@(trigger) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(input, trigger), trial_offset_triggers))) || ...
(~are_offset_triggers_included && (any(cellfun(@(trigger) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(input, trigger), trial_onset_triggers)) || input_time - potential_trial_start_time > trial_dur - baseline))
search_phase = 1;
start_times= [start_times, potential_trial_start_time - baseline]; %#ok<AGROW>
if isempty(regexp(curr_cond_field_name, ['(^|_)', potential_trial_start_input, '_'], 'ONCE'))
curr_cond_field_name = [curr_cond_field_name, potential_trial_start_input, '_']; %#ok<AGROW>
end
if are_offset_triggers_included
end_times = [end_times, input_time + post_offset_triggers_segment]; %#ok<AGROW>
else
continue;
end
elseif (are_offset_triggers_included && input_time - potential_trial_start_time > trial_dur - baseline) || ...
any(cellfun(@(str) EyeTrackerAnalysisRecord.doesTriggerMatchRegexp(input, str), trial_rejection_triggers))
search_phase = 1;
end
field_i = field_i + 1;
end
if search_phase == 2
start_times= [start_times, potential_trial_start_time - baseline];
if are_offset_triggers_included
end_times = [end_times, potential_trial_start_time + trial_dur + post_offset_triggers_segment];
end
end
if ~isempty(curr_cond_field_name)
curr_cond_field_name(end) = '';
curr_cond_field_name = convertMsgToValidFieldName([EyeTrackerAnalysisRecord.CONDS_NAMES_PREFIX, curr_cond_field_name]);
end
end
end
function [segmentized_data, detection_done]= getSegmentizedData(obj, detection_requested, progress_screen, progress_contribution, filter_bandpass)
segmentized_data = [];
detection_done = [];
if obj.chosen_segmentization_i==0
error('EyeTrackerAnalysisRecord:noSegmentizationChosen', 'must call segmentizeData() prior to getSegmentizedData() so segmentized data would be chosen/created');
end
sessions_nr= numel(obj.segmentization_vecs{obj.chosen_segmentization_i});
segmentized_data_unmerged= cell(1,sessions_nr);
were_triggers_ever_found = false;
for session_i= 1:sessions_nr
if isempty(obj.segmentization_vecs{obj.chosen_segmentization_i}(session_i).trials_start_times)
continue;
else
were_triggers_ever_found = true;
end
curr_session_segmentization_vecs_struct= obj.segmentization_vecs{obj.chosen_segmentization_i}(session_i);
curr_session_eye_tracker_data_struct= EyeTrackerAnalysisRecord.filterEyeData(obj.eye_tracker_data_structs{session_i}, filter_bandpass, obj.sampling_rate);
% curr_session_eye_tracker_data_struct = obj.eye_tracker_data_structs{session_i};
conds_names= fieldnames(curr_session_segmentization_vecs_struct.trials_start_times);
for cond_name_i= 1:numel(conds_names)
curr_cond_name= conds_names{cond_name_i};
trials_nr= numel(curr_session_segmentization_vecs_struct.trials_start_times.(curr_cond_name));
if trials_nr==0
segmentized_data_unmerged{session_i}.(curr_cond_name)= [];
progress_screen.addProgress(progress_contribution / (numel(conds_names) * sessions_nr));
else
for trial_i= 1:trials_nr
indStart= curr_session_segmentization_vecs_struct.trials_start_times.(curr_cond_name)(trial_i);
if isnan(indStart)
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).onset_from_session_start= [];
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).samples_nr= [];
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).blinks= [];
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft= [];
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight= [];
continue;
end
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).onset_from_session_start= indStart;
indEnd= curr_session_segmentization_vecs_struct.trials_end_times.(curr_cond_name)(trial_i);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).samples_nr= indEnd - indStart + 1;
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).blinks= curr_session_segmentization_vecs_struct.blinks(indStart:indEnd);
was_left_eye_recorded = obj.wasLeftEyeRecorded(session_i);
was_right_eye_recorded = obj.wasRightEyeRecorded(session_i);
if detection_requested ~= EyeTrackerAnalysisRecord.ENUM_DETECTION_BINOCULAR || ~was_left_eye_recorded || ~was_right_eye_recorded
if ~was_left_eye_recorded || (detection_requested == EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_R && was_right_eye_recorded)
gaze= curr_session_eye_tracker_data_struct.gazeRight;
detection_done = EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_R;
if detection_requested ~= EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_R
if detection_requested == EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_L
detection_str = 'Monocular (L)';
else
detection_str = 'Binocular';
end
progress_screen.displayMessage(['<<WARNING (on session #', num2str(session_i), '): ', detection_str, ' detection was requested, but no data for left eye was found. proceeding with Monocular (R) detection>>']);
end
else
gaze= curr_session_eye_tracker_data_struct.gazeLeft;
detection_done = EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_L;
if detection_requested ~= EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_L
if detection_requested == EyeTrackerAnalysisRecord.ENUM_DETECTION_MONOCULAR_R
detection_str = 'Monocular (R)';
else
detection_str = 'Binocular';
end
progress_screen.displayMessage(['<<WARNING (on session #', num2str(session_i), '): ', detection_str, ' detection was requested, but no data for right eye was found. proceeding with Monocular (L) detection>>']);
end
end
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.x= gaze.x(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.y= gaze.y(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.pupil= gaze.pupil(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.x= segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.x;
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.y= segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.y;
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.pupil= segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.pupil;
else
detection_done = EyeTrackerAnalysisRecord.ENUM_DETECTION_BINOCULAR;
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.x= curr_session_eye_tracker_data_struct.gazeLeft.x(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.y= curr_session_eye_tracker_data_struct.gazeLeft.y(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeLeft.pupil= curr_session_eye_tracker_data_struct.gazeLeft.pupil(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.x= curr_session_eye_tracker_data_struct.gazeRight.x(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.y= curr_session_eye_tracker_data_struct.gazeRight.y(indStart:indEnd);
segmentized_data_unmerged{session_i}.(curr_cond_name)(trial_i).gazeRight.pupil= curr_session_eye_tracker_data_struct.gazeRight.pupil(indStart:indEnd);
end
if mod(trial_i, 50) == 0
progress_screen.addProgress(progress_contribution / (numel(conds_names) * sessions_nr * (trials_nr/50)));
end
end
progress_screen.addProgress(progress_contribution * mod(trials_nr, 50) / (numel(conds_names) * sessions_nr * trials_nr));
end
end
end
%merge sessions' structs
if were_triggers_ever_found
if sessions_nr > 1
segmentized_data = [];
for merged_session_i= 1:sessions_nr
if isempty(segmentized_data_unmerged{merged_session_i})
continue;
end
conds_names= fieldnames(obj.segmentization_vecs{obj.chosen_segmentization_i}(merged_session_i).trials_start_times);
for cond_i= 1:numel(conds_names)
curr_merged_cond_name= conds_names{cond_i};
if ~isfield(segmentized_data, curr_merged_cond_name)
segmentized_data.(curr_merged_cond_name)= [];
end
if ~isempty(segmentized_data_unmerged{merged_session_i}.(curr_merged_cond_name))
segmentized_data.(curr_merged_cond_name)= ...
[segmentized_data.(curr_merged_cond_name), segmentized_data_unmerged{merged_session_i}.(curr_merged_cond_name)];
end
end
end
else
segmentized_data= segmentized_data_unmerged{1};
end
else
progress_screen.addProgress(progress_contribution);
end
end
function registerSaccadesAnalysis(obj, saccades_analysis_struct)
if obj.chosen_segmentization_i==0
error('EyeTrackerAnalysisRecord:noSegmentizationChosen', 'data of an EyeTrackerAnalysisRecord object has to be segmentized prior to analysis');
end
obj.saccades_extractors_data{obj.chosen_segmentization_i}= saccades_analysis_struct;
end
function saccades_analysis_struct= loadSaccadesAnalysis(obj)
if obj.chosen_segmentization_i==0
error('EyeTrackerAnalysisRecord:noSegmentizationChosen', 'data of an EyeTrackerAnalysisRecord object has to be segmentized prior to analysis');
end
saccades_analysis_struct= obj.saccades_extractors_data{obj.chosen_segmentization_i};
end
function analysis_tag= getAnalysisTag(obj)
analysis_tag= obj.analysis_tag;
end
function eye_tracker_data_structs= getEyeTrackerDataStructs(obj)
eye_tracker_data_structs= obj.eye_tracker_data_structs;
end
function dpp = getDpp(obj)
dpp = obj.dpp;
end
function sampling_rate = getSamplingRate(obj)
sampling_rate = obj.sampling_rate;
end
function save(obj, full_file_path)
eta = obj; %#ok<NASGU>
tic; save(full_file_path, 'eta', '-mat'); toc;
end
function is_eeg_involved= isEegInvolved(obj)
is_eeg_involved= obj.is_eeg_involved;
end
end
methods (Access=private)
function res = wasLeftEyeRecorded(obj, session_i)
res = EyeTrackerAnalysisRecord.doesEyePosVecContainData(obj.eye_tracker_data_structs{session_i}.gazeLeft.x);
end
function res = wasRightEyeRecorded(obj, session_i)
res = EyeTrackerAnalysisRecord.doesEyePosVecContainData(obj.eye_tracker_data_structs{session_i}.gazeRight.x);
end
end
methods (Access= private, Static)
function eye_tracking_data_structs= extractEyeTrackerStructsFromLoadedMatStructs(loaded_struct)
eye_tracking_data_structs= {};
fields_names= fieldnames(loaded_struct);
for field_i= 1:numel(fields_names)
curr_tested_variable= loaded_struct.(fields_names{field_i});
if isstruct(curr_tested_variable)
%if isStructAnEyeDataStruct(curr_tested_variable)
eye_tracking_data_structs= [eye_tracking_data_structs, curr_tested_variable]; %#ok<AGROW>
%end
elseif iscell(curr_tested_variable)
for slot_i= 1:numel(curr_tested_variable)
%if isStructAnEyeDataStruct(curr_tested_variable{slot_i})
eye_tracking_data_structs= [eye_tracking_data_structs, curr_tested_variable{slot_i}]; %#ok<AGROW>
%end
end
end
end
function res= isStructAnEyeDataStruct(struct)
if numel(fieldnames(struct))~= 12 || ...
~isfield(struct, 'filename') || ...
~isfield(struct, 'numElements') || ...
~isfield(struct, 'numTrials') || ...
~isfield(struct, 'EDFAPI') || ...
~isfield(struct, 'preamble') || ...
~isfield(struct, 'gazeLeft') || ...
~isfield(struct, 'gazeRight') || ...
~isfield(struct, 'blinks') || ...
~isfield(struct, 'messages') || ...
~isfield(struct, 'gazeCoords') || ...
~isfield(struct, 'frameRate') || ...
~isfield(struct, 'inputs')
res= false;
return;
end
if ~isValidGazeStruct(struct.gazeLeft) || ~isValidGazeStruct(struct.gazeRight)
res= false;
return;
end
blinks_struct= struct.blinks;
if numel(fieldnames(blinks_struct))~= 2 || ...
~isfield(blinks_struct, 'startTime') || ...
~isfield(blinks_struct, 'endTime')
res= false;
return;
end
if isempty(struct.messages) || ...
numel(fieldnames(struct.messages))~=2 || ...
~isfield(struct.messages, 'message') || ...
~isfield(struct.messages, 'time')
res= false;
return;
end
if isempty(struct.inputs) || ...
numel(fieldnames(struct.inputs))~=2 || ...
~isfield(struct.inputs, 'input') || ...
~isfield(struct.inputs, 'time')
res= false;
return;
end
res= true;
% === TYPES CHECK NOT INCLUDED ===
% if ~ischar(struct.filename) || ...
% ~isnumeric(struct.numElements) || ...
% numel(struct.numElements)~=1 || ...
% ~isnumeric(struct.numTrials) || ...
% numel(struct.numTrials)~=1 || ...
% ~ischar(struct.EDFAPI) || ...
% ~ischar(struct.preamble) || ...
% ~isnumeric(struct.gazeCoords) || ...
% numel(struct.gazeCoords)~=4 || ...
% ~isnumeric(struct.frameRate) || ...
% numel(struct.frameRate)~=1
% res= false;
% return;
% end
function res= isValidGazeStruct(struct)
if numel(fieldnames(struct))~= 4 || ...
~isfield(struct, 'time') || ...
~isfield(struct, 'x') || ...
~isfield(struct, 'y') || ...
~isfield(struct, 'pupil')
res= false;
else
res= true;
end
end
end
end
function updated_eeg_struct= addEtaFieldsToEegStruct(eeg_struct)
updated_eeg_struct= eeg_struct;
updated_eeg_struct.gazeLeft.x= double(eeg_struct.data(74,:));
updated_eeg_struct.gazeLeft.y= double(eeg_struct.data(75,:));
updated_eeg_struct.gazeLeft.pupil= double(eeg_struct.data(76,:));
updated_eeg_struct.gazeLeft.time= 1:numel(updated_eeg_struct.gazeLeft.x);
updated_eeg_struct.gazeRight.x= double(eeg_struct.data(77,:));
updated_eeg_struct.gazeRight.y= double(eeg_struct.data(78,:));
updated_eeg_struct.gazeRight.pupil= double(eeg_struct.data(79,:));
updated_eeg_struct.gazeRight.time= 1:numel(updated_eeg_struct.gazeRight.x);
for trigger_i= 1:numel(eeg_struct.event)
updated_eeg_struct.messages(trigger_i).time= eeg_struct.event(trigger_i).latency;
updated_eeg_struct.messages(trigger_i).message= eeg_struct.event(trigger_i).type;
updated_eeg_struct.inputs(trigger_i).time= [];
updated_eeg_struct.inputs(trigger_i).input= [];
end
updated_eeg_struct.blinks.startTime= [];
updated_eeg_struct.blinks.endTime= [];
for event_i= 1:numel(eeg_struct.event)
if strcmp(eeg_struct.event(event_i).type,'R_blink') || strcmp(eeg_struct.event(event_i).type,'L_blink')
updated_eeg_struct.blinks.startTime= [updated_eeg_struct.blinks.startTime, eeg_struct.event(event_i).latency];
updated_eeg_struct.blinks.endTime= [updated_eeg_struct.blinks.endTime, eeg_struct.event(event_i).latency + eeg_struct.event(event_i).duration];
end
end
end
% expects sampling rate to be at least 100HZ
function sampling_rate = compSamplingRate(time_vec)
time_intervals = abs(diff(time_vec));
sampling_rate = round(1000 / mean(time_intervals(time_intervals <= 10)));
end
function eye_data_struct= filterEyeData(eye_data_struct, bandpass, rate)
eye_data_struct.gazeRight.x= EyeTrackerAnalysisRecord.naninterp(eye_data_struct.gazeRight.x);
eye_data_struct.gazeRight.y= EyeTrackerAnalysisRecord.naninterp(eye_data_struct.gazeRight.y);
eye_data_struct.gazeRight.x= EyeTrackerAnalysisRecord.lowPassFilter(bandpass,eye_data_struct.gazeRight.x,rate);
eye_data_struct.gazeRight.y= EyeTrackerAnalysisRecord.lowPassFilter(bandpass,eye_data_struct.gazeRight.y,rate);
eye_data_struct.gazeLeft.x= EyeTrackerAnalysisRecord.naninterp(eye_data_struct.gazeLeft.x);
eye_data_struct.gazeLeft.y= EyeTrackerAnalysisRecord.naninterp(eye_data_struct.gazeLeft.y);
eye_data_struct.gazeLeft.x= EyeTrackerAnalysisRecord.lowPassFilter(bandpass,eye_data_struct.gazeLeft.x,rate);
eye_data_struct.gazeLeft.y= EyeTrackerAnalysisRecord.lowPassFilter(bandpass,eye_data_struct.gazeLeft.y,rate);
end
function blinksbool= eyelinkBased_blinkdetection(eyelink, delta, progress_screen, progress_contribution)
if nargin==1
delta=130;
end
exp_time= length(eyelink.gazeRight.time);
blinksbool= zeros(1, exp_time);%initialize array matching the time points
blinksbool=logical(blinksbool);
blinks_nr= length(eyelink.blinks.startTime);
interval_blinks_nr= min(200,blinks_nr);
interval_progress_contribution= progress_contribution*interval_blinks_nr/blinks_nr;
for i= 1:blinks_nr
if mod(i,interval_blinks_nr)==0
progress_screen.addProgress(interval_progress_contribution);
end
curr_start_time_i= find(eyelink.gazeRight.time==eyelink.blinks.startTime(i), 1);
curr_end_time_i= find(eyelink.gazeRight.time==eyelink.blinks.endTime(i), 1);
if curr_start_time_i-delta<1
curr_start_time_i= 1;
else
curr_start_time_i= curr_start_time_i - delta;
end
if curr_end_time_i+delta>exp_time
curr_end_time_i= exp_time;
else
curr_end_time_i= curr_end_time_i + delta;
end
blinksbool(curr_start_time_i:curr_end_time_i)=1;
end
if mod(blinks_nr,interval_blinks_nr)~=0
progress_screen.addProgress(progress_contribution*mod(1,interval_blinks_nr/blinks_nr));
end
end
function new_blink_vec=pupilBased_blinkdetection_twoEyes(pupilr, pupill, Fs, std, consq_samples, tolerance, padding, maxsegtime, progress_screen, progress_contribution)
%% inputs
% pupilr - the pupildata vector of the right eye as retried by eyelink
% (arbitrary units).
% pupill - the pupildata vector of the left eye as retried by eyelink
% (arbitrary units).
% Fs - the eye tracking sampling rate (not using this at the moment
% 2.7.2018 (the hard coded numbers assumes a 1k refresh rate
% std - how many stds from the mean define an outlier
% consq_samples - how many consequtive outlier samples is the minimum to
% consider an offset/onset candidnate
% tolerance - how many non oulier samples will break the consequtive
% outlier sample counter
% old_blinkvec - a blink vector to plot and compare the new detection with.
% to_plot - boolean if the user wants the trial to be plotted.
% padding - how many samples to add before and after each detected blink.
% maxsegtime -(in seconds) define the maximum size of segments per detection - this is
% mainly important in non segmented data, as we use mean and std so
% splitting the data makes sense to not get heart much by breaks and other
% problems.
new_blink_vecr=[];
new_blink_vecl=[];
new_blink_vec=[];
segments_errors=[];
% if data is long (over 10k samples) split it and then analyze smaller parts
if (length(pupilr)/(maxsegtime*Fs))>1
lastseg_size=rem(length(pupilr),(maxsegtime*Fs));
if lastseg_size>(maxsegtime*Fs/2)
starttimes=1:(maxsegtime*Fs):length(pupilr);
else
starttimes=1:(maxsegtime*Fs):length(pupilr);
starttimes=starttimes(1:(end-1));
end
for i=1:length(starttimes)
if ~(i==length(starttimes)); %if it is not the last segment:
cur_pupilr=pupilr(starttimes(i):starttimes(i+1)-1);
cur_pupill=pupill(starttimes(i):starttimes(i+1)-1);
else
cur_pupilr=pupilr(starttimes(i):end);
cur_pupill=pupill(starttimes(i):end);
end
[blink_vecr,problemflagr]=EyeTrackerAnalysisRecord.pupilBased_blinkdetection(cur_pupilr,Fs,std,consq_samples,tolerance, progress_screen, 0.5*progress_contribution/length(starttimes));
[blink_vecl,problemflagl]=EyeTrackerAnalysisRecord.pupilBased_blinkdetection(cur_pupill,Fs,std,consq_samples,tolerance, progress_screen, 0.5*progress_contribution/length(starttimes));
new_blink_vecr=[new_blink_vecr,blink_vecr];
new_blink_vecl=[new_blink_vecl,blink_vecl];
segments_errors=[segments_errors,problemflagr | problemflagl];
progress_screen.addProgress(progress_contribution/length(starttimes));
end
else
[new_blink_vecr,problemflagr]=EyeTrackerAnalysisRecord.pupilBased_blinkdetection(pupilr,Fs,std,consq_samples,tolerance, progress_screen, 0.5*progress_contribution);
[new_blink_vecl,problemflagl]=EyeTrackerAnalysisRecord.pupilBased_blinkdetection(pupill,Fs,std,consq_samples,tolerance, progress_screen, 0.5*progress_contribution);
segments_errors=[problemflagr | problemflagl];
progress_screen.addProgress(progress_contribution);
end
%% count blinks only from both eyes:
new_blink_vec=new_blink_vecr & new_blink_vecl;
%% add the requested blink padding:
onsets=find(diff(new_blink_vec)==1);
offsets=find(diff(new_blink_vec)==-1);
temp_blink_vec=new_blink_vec;
for curroffset=offsets
if (curroffset+padding)<=length(temp_blink_vec);
temp_blink_vec(curroffset:(curroffset+padding))=1;
elseif curroffset<=length(temp_blink_vec);
temp_blink_vec(curroffset:(curroffset+(length(temp_blink_vec)-curroffset)))=1;
end
end
for curronset=onsets
if curronset-padding>0
temp_blink_vec(curronset-padding:curronset)=1;
else
temp_blink_vec(1:curronset)=1;
end
end
new_blink_vec=temp_blink_vec;
end
function [blink_vec,problemflag]=pupilBased_blinkdetection(pupildata,Fs,std,consq_samples,tolerance, progress_screen, progress_contribution)
% this functions uses the derivetive of pupilsize to find unplausible size
% changes and mark them as blink onsets and offsets. %it then compares the
% found blink with a prior blink vector.
% in our lab settings, room a: the values that works for me are:
% pupilBased_blinkdetection(pupildata,1000,2.5,4,5,old_blinkvec,1)
%logic:
%1. find all outlier samples in pupilsize slops (negative for onsets
%and position for offsets)
%2. search for consequtive outlier samples to define an offset or offset
%3. correct end and start estimation by:
%3.1 for onsets, go backwards on a filtered version of the pupilsize from
%each onset untill the first non negative slope sample
%3.2 for offsets, go forward from each offset and find the first non
%positive slope sample
%% inputs
% pupildata - the pupildata vector of one eye as retried by eyelink
% (arbitrary units).
% Fs - the eye tracking sampling rate (not using this at the moment
% 2.7.2018 (the hard coded numbers assumes a 1k refresh rate
% std - how many stds from the mean define an outlier
% consq_samples - how many consequtive outlier samples is the minimum to
% consider an offset/onset candidnate
% tolerance - how many non oulier samples will break the consequtive
% outlier sample counter
% old_blinkvec - a blink vector to plot and compare the new detection with.
% to_plot - boolean if the user wants the trial to be plotted.
%% code: onests:
%create a pupil size slope vector:
slopes=diff(pupildata);
slopes_zscores=(slopes-nanmean(slopes))./nanstd(slopes);
%filter the data so i can follow the slope without gigsaw patterns.
%first make sure it doenst end or starts with a nan or else
%extrapolation will not work.
if isnan(pupildata(end))
pupildata(end)=nanmean(pupildata);
end
if isnan(pupildata(1))
pupildata(1)=nanmean(pupildata);
end
%keep the original raw vector
original_pupildata=pupildata;
%create a boolean vector of nan values: to fix samples that are
%sournded by nans
temp_pupildata=zeros(1,length(pupildata));
temp_pupildata(isnan(pupildata))=1;
%this code runs over the nan values and marks as nan every segment that
%is too short (10 samples atm) and has a nan value before and after it.
valid_cnt=0;
for i=2:length(temp_pupildata)-1
if temp_pupildata(i)==0
valid_cnt=valid_cnt+1;
else
if valid_cnt<10
pupildata(i-valid_cnt:i-1)=nan;
valid_cnt=0;
else
valid_cnt=0;
end
end
% progress_screen.addProgress(0.3/(length(temp_pupildata)-2)*progress_contribution);
end
%filter the slopes: optional - causes some problems
% slopes=diff(pupildataclean);
%
% %try filtering the slopes: (not sure);
% filtered_slopes=lowPassFilter(10,slopes,Fs);
% filtered_slopes_extrapolated=filtered_slopes;
% filtered_slopes(isnan(pupildata))=nan;
% slopes=filtered_slopes;
% slopes_zscores=(slopes-nanmean(slopes))./nanstd(slopes);
if any(~isnan(slopes))
pupildataclean= EyeTrackerAnalysisRecord.naninterp(pupildata);
problemflag=0; %will rise this flag to signal that non alternating onsets and offsets were found
%suspect onsets:
suspect_onsets_indexes=find(slopes_zscores<-1*std);
%refine samples:
real_onsets=[];
if ~isempty(suspect_onsets_indexes)
cur_index=suspect_onsets_indexes(1);
cnt=1;
for i=2:length(suspect_onsets_indexes)
if ismember(suspect_onsets_indexes(i),cur_index:cur_index+tolerance)
cnt=cnt+1;
cur_index=suspect_onsets_indexes(i);
elseif cnt>=consq_samples
real_onsets=[real_onsets,suspect_onsets_indexes(i-1)-cnt];
cnt=0;
cur_index=suspect_onsets_indexes(i);
else
cnt=0;
cur_index=suspect_onsets_indexes(i);
end
% progress_screen.addProgress(0.2/(length(suspect_onsets_indexes)-1)*progress_contribution);
end
%add the last onset:
if cnt>=consq_samples
real_onsets=[real_onsets,suspect_onsets_indexes(i-1)-cnt];
cnt=0;
end
else
% progress_screen.addProgress(0.2*progress_contribution);
end
%% code:offsets:
%create a pupil size slope vector:
slopes=diff(pupildata);
slopes_zscores=-1*(slopes-nanmean(slopes))./nanstd(slopes);
% slopes=diff(pupildataclean);
%
% %try filtering the slopes: (not sure);
% filtered_slopes=lowPassFilter(10,slopes,Fs);
% filtered_slopes_extrapolated=filtered_slopes;
% filtered_slopes(isnan(pupildata))=nan;
% slopes=filtered_slopes;
% slopes_zscores=-1*(slopes-nanmean(slopes))./nanstd(slopes);
%suspect offsets:
suspect_offsets_indexes=find(slopes_zscores<-1*std);
%refine samples:
real_offsets=[];
if ~isempty(suspect_offsets_indexes)
cur_index=suspect_offsets_indexes(1);
cnt=1;
for i=2:length(suspect_offsets_indexes)
if ismember(suspect_offsets_indexes(i),cur_index:cur_index+5)
cnt=cnt+1;
cur_index=suspect_offsets_indexes(i);
elseif cnt>=consq_samples
real_offsets=[real_offsets,suspect_offsets_indexes(i-1)];
cnt=0;
cur_index=suspect_offsets_indexes(i);
else
cur_index=suspect_offsets_indexes(i);
cnt=0;
end
% progress_screen.addProgress(0.1/(length(suspect_offsets_indexes) - 1)*progress_contribution);
end
%add the last onset:
if cnt>=consq_samples
real_offsets=[real_offsets,suspect_offsets_indexes(i-1)-cnt];
end
else
% progress_screen.addProgress(0.1*progress_contribution);
end
%% initial testing graph:
% if to_plot
%
% figure();
% subplot(2,1,1);
% plot(pupildata); hold on;
% plot(suspect_onsets_indexes,pupildata(suspect_onsets_indexes),'*m');
% hold on;
% plot(suspect_offsets_indexes,pupildata(suspect_offsets_indexes),'*g');
% legend({'pupil','onsets','offsets'});
% end
% fix the onset timings (use ronen's method, of going backwards untill we find a non-negative slope
final_onsets=[];
%filter the raw pupil to have smooth curves:
filtered_pupil=EyeTrackerAnalysisRecord.lowPassFilter(10,pupildataclean,Fs);
filtered_pupil_extrapolated=filtered_pupil;
filtered_pupil(isnan(pupildata))=nan;
slopes=diff(filtered_pupil);
cur_onset=[];
for i=1:length(real_onsets)
stop=0;
cur_onset=real_onsets(i);
temp_onset=real_onsets(i);
while ~stop && cur_onset>1