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SaccadesExtractor.m
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SaccadesExtractor.m
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classdef SaccadesExtractor < handle
% class for storing the parameterizations and variables relating to
% saccades extraction, and to perform the saccades extraction
properties (Access= public, Constant)
EYEBALLER_MAIN_GUI_BACKGROUND_COLOR= [0.8, 0.8, 0.8];
ENUM_VEL_CALC_TYPE_1= 1;
ENUM_VEL_CALC_TYPE_RUNNING_MEAN= 2;
ENUM_VEL_CALC_TYPE_3= 3;
end
properties (Access= private)
subjects_etas;
subjects_nr;
dpps;
sampling_rates;
engbert_algorithm_params;
% engbert_algorithm_interm_vars: caches intermediate variables computed by engbert's algorithm
% that are reused multiple times
engbert_algorithm_interm_vars;
eyeballer_main_gui_pos;
end
methods (Access= public)
function obj= SaccadesExtractor(subjects_etas)
% SaccadesExtractor ctr
% input:
% subjects_etas -> cell array of EyeTrackerAnalysisRecord objects.
if ~iscell(subjects_etas)
subjects_etas= {subjects_etas};
end
obj.subjects_etas= subjects_etas;
obj.subjects_nr= numel(subjects_etas);
obj.dpps = NaN(1,obj.subjects_nr);
for subject_i = 1:obj.subjects_nr
obj.dpps(subject_i)= subjects_etas{subject_i}.getDpp();
obj.sampling_rates(subject_i)= subjects_etas{subject_i}.getSamplingRate();
end
screen_size= get(0,'monitorpositions');
if any(screen_size(1)<0)
screen_size= get(0,'ScreenSize');
end
screen_size= screen_size(1,:);
obj.eyeballer_main_gui_pos= round([0.2*screen_size(3), -0.2*screen_size(4), 0.6*screen_size(3), 0.8*screen_size(4)]);
end
function [eye_data_struct, saccades_struct, eyeballing_stats, metadata]= extractSaccadesByEngbert(obj, detection_requested, vel_calc_type, vel_threshold, amp_lim, amp_low_lim, saccade_dur_min, frequency_max, filter_bandpass, extract_pupil_size, perform_eyeballing, eyeballer_display_range_multiplier, eyeballer_timeline_left_offset, etas_full_paths, progress_contribution, progress_screen, logger)
% perform the saccades extraction
% input:
% * detection_requested -> monocular or binocular detection.
% values according to the enumeration defined in EyeTrackerAnalysisRecord.
% * vel_calc_type -> method to calculate the eyes velocity.
% values according to the VEL_CALC enumeration defined above.
% * vel_threshold -> threshold velocity for the ellipse
% equation in engbert's algorithm test criterion.
% * amp_lim -> maximum amplitude for a saccade above which
% to discard the saccade.
% * amp_low_lim -> minimum amplitude for a saccade below which
% to discard the saccade.
% * saccade_dur_min -> minimum duration for a saccade below which
% to discard the saccade.
% * frequency_max -> time interval after a detected saccade
% within which to discard later found saccades.
% * filter_bandpass -> filter lowpass frequency to apply on
% the eyes tracking data
% * extract_pupil_size -> whether pupil size should be
% extracted
% * perform_eyeballing -> whether or not to perform a manual
% analysis inspection
% * eyeballer_display_range_multiplier [perform_eyeballing == true] ->
% define this as a factor in computing the eyes'
% data plots Y axes ranges.
% * eyeballer_timeline_left_offset [perform_eyeballing == true] ->
% offset to apply to the time axes in the eyeballer's eyes' data plots.
% * etas_full_paths [perform_eyeballing == true] -> full file paths to
% save the updated EyeTrackerAnalysisRecord files.
% * progress_contribution -> factor to scale progress with
% * progress_screen -> DualBarProgressScreen to add progress to
% * logger -> Logger object with which to log messages.
%
% output:
% * eye_data_struct
% * saccades_struct
% * eyeballing_stats
if perform_eyeballing
% eye positions data to pass to the eyeballer. this is the
% data the eyeballer plots.
raw_eye_data_for_eyeballer= cell(1, obj.subjects_nr);
% parameters to be passed by the eyeballer to the function
% performing a saccade search when an eye positions plot is
% clicked on in the eyeballer
manual_saccades_search_func_params_for_eyeballer= cell(1, obj.subjects_nr);
end
% segmentized data per subject
subjects_data_structs= cell(1,obj.subjects_nr);
% output variable
saccades_struct= cell(1, obj.subjects_nr);
% output variable
eye_data_struct = cell(1, obj.subjects_nr);
% output variable
eyeballing_stats= [];
% output variable
metadata = cell(1, obj.subjects_nr);
% initializing the parameters for the saccades extraction.
% if the user requests a re-extraction in the eyeballer, these
% variables will be assigned with the re-extraction parameters
curr_requested_vel_calc_type= vel_calc_type;
curr_requested_vel_threshold= vel_threshold;
curr_requested_amp_lim= amp_lim;
curr_requested_amp_low_lim = amp_low_lim;
curr_requested_saccade_dur_min= saccade_dur_min;
curr_requested_frequency_max= frequency_max;
curr_requested_low_pass_filter = filter_bandpass;
was_new_extraction_requested_by_eyeballer= false;
is_extraction_go= true;
% the while condition runs every time the user requests a saccades
% re-extraction in the eyeballer
while is_extraction_go
% segmentizing the data to conditions for all subjects.
progress_screen.updateProgress(0);
for subject_i= 1:obj.subjects_nr
[subjects_data_structs{subject_i}, detection_performed]= obj.subjects_etas{subject_i}.getSegmentizedData(detection_requested, progress_screen, 0.8*progress_contribution/obj.subjects_nr, curr_requested_low_pass_filter);
previous_saccades_analysis= obj.subjects_etas{subject_i}.loadSaccadesAnalysis();
if ~isempty(previous_saccades_analysis)
% if a previous analysis was saved via the eyeballer
% for the segmentization defined by the current run
% parameters, save the saccades data into the output
% variables, and later skip the saccades extraction
% code.
if perform_eyeballing
raw_eye_data_for_eyeballer{subject_i}= previous_saccades_analysis.raw_eye_data;
manual_saccades_search_func_params_for_eyeballer{subject_i}= previous_saccades_analysis.manual_saccades_search_func_params;
end
saccades_struct{subject_i}= previous_saccades_analysis.saccades_struct;
eye_data_struct{subject_i}= previous_saccades_analysis.eye_data_struct;
end
end
for subject_i= 1:obj.subjects_nr
if ~was_new_extraction_requested_by_eyeballer && ~isempty(saccades_struct{subject_i})
% skip the saccades extraction for the current
% subject if re-extraction was not requested in the
% gui and if a previous extraction was found.
progress_screen.addProgress(0.2*progress_contribution/obj.subjects_nr);
continue;
end
curr_subject_data_struct= subjects_data_structs{subject_i};
if isempty(curr_subject_data_struct)
% if the segmentization produced no data segments.
progress_screen.addProgress(0.2*progress_contribution/obj.subjects_nr);
continue;
end
% Extracting metadata
metadata{subject_i}.sampling_rate = obj.sampling_rates(subject_i);
[~, filename, file_ext] = fileparts(etas_full_paths{subject_i});
metadata{subject_i}.original_filename = strcat(filename, file_ext);
% Extracting condition names and going through each
conds_names= fieldnames(curr_subject_data_struct);
for cond_i= 1:numel(conds_names)
curr_cond_name= conds_names{cond_i};
curr_cond_struct= curr_subject_data_struct.(curr_cond_name);
eye_data_struct{subject_i}.(curr_cond_name) = [];
saccades_struct{subject_i}.(curr_cond_name) = [];
if perform_eyeballing
% initialize data expected by the eyeballer
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name) = [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name) = [];
end
% Going over each trial
for trial_i= 1:numel(curr_cond_struct)
blink= squeeze(curr_cond_struct(trial_i).blinks);
if isempty(blink) || all(blink)
% no data was recorded during the entire trial's length.
% assign empty vectors to all output variables.
if perform_eyeballing
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_x= [];
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_y= [];
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_x= [];
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_y= [];
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec= [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels= [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels= [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data= [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data= [];
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec= [];
end
fillSaccadesStructWithVal([]);
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec = [];
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).vergence = [];
if extract_pupil_size
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).pupil_size = [];
end
continue;
end
raw_eye_data_mat= [1:curr_cond_struct(trial_i).samples_nr; ...
curr_cond_struct(trial_i).gazeRight.x; ...
curr_cond_struct(trial_i).gazeRight.y; ...
curr_cond_struct(trial_i).gazeLeft.x; ...
curr_cond_struct(trial_i).gazeLeft.y]';
%non_nan_times_logical_vec holds 1s for non-nan data times and 0 for nan data times
non_nan_times_logical_vec= ~isnan(raw_eye_data_mat(:,2)) & ~isnan(raw_eye_data_mat(:,3)) & ~blink';
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec = non_nan_times_logical_vec;
%now raw_eye_data_mat should contain only non-null data points
raw_eye_data_mat= raw_eye_data_mat(non_nan_times_logical_vec,:);
% baseline-correct the eyes data
% xr - right eye positions. xl - left eye positions
% xr(:,1), xl(:,1) - eye positions x coordinates
% xr(:,2), xl(:,2) - eye positions y coordinates
xr = obj.dpps(subject_i)*raw_eye_data_mat(:,2:3);
xr(:,1) = xr(:,1) - mean(xr(:,1));
xr(:,2) = xr(:,2) - mean(xr(:,2));
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data= xr;
xl = obj.dpps(subject_i)*raw_eye_data_mat(:,4:5);
xl(:,1) = xl(:,1) - mean(xl(:,1));
xl(:,2) = xl(:,2) - mean(xl(:,2));
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data= xl;
% Extracting raw eye position and vergence
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).vergence = NaN(curr_cond_struct(trial_i).samples_nr,2);
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).vergence(non_nan_times_logical_vec, :) = [xr(:,1) - xl(:,1), xr(:,2) - xl(:,2)];
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).raw_eye_data.right_eye = [curr_cond_struct(trial_i).gazeRight.x; curr_cond_struct(trial_i).gazeRight.y]';
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).raw_eye_data.left_eye = [curr_cond_struct(trial_i).gazeLeft.x; curr_cond_struct(trial_i).gazeLeft.y]';
% Extracting pupil size
if extract_pupil_size
% Extracting pupil size data for each eye
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).pupil_size.left_eye = curr_subject_data_struct.(curr_cond_name)(trial_i).gazeLeft.pupil;
eye_data_struct{subject_i}.(curr_cond_name)(trial_i).pupil_size.right_eye = curr_subject_data_struct.(curr_cond_name)(trial_i).gazeRight.pupil;
end
% Compute 2D velocity vectors
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels = ...
SaccadesExtractor.vecvel(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data, ...
curr_requested_vel_calc_type, ...
obj.sampling_rates(subject_i));
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels = ...
SaccadesExtractor.vecvel(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data, ...
curr_requested_vel_calc_type, ...
obj.sampling_rates(subject_i));
obj.engbert_algorithm_params.vel_calc_type= curr_requested_vel_calc_type;
if perform_eyeballing
% cache variables to be used by the saccade search function
% that is run when the user requests to find a saccade in the
% insepector on a specific time.
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels= ...
NaN(numel(raw_eye_data_mat(:,1)), 2);
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels(non_nan_times_logical_vec,:)= ...
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels;
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels= ...
NaN(numel(raw_eye_data_mat(:,1)), 2);
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels(non_nan_times_logical_vec,:)= ...
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels;
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data= ...
NaN(numel(raw_eye_data_mat(:,1)), 2);
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data(non_nan_times_logical_vec,:)= ...
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data;
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data= ...
NaN(numel(raw_eye_data_mat(:,1)), 2);
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data(non_nan_times_logical_vec,:)= ...
obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data;
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_x= ...
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data(:,1)';
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_y= ...
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data(:,2)';
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_x= ...
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data(:,1)';
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_y= ...
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data(:,2)';
manual_saccades_search_func_params_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec= non_nan_times_logical_vec;
raw_eye_data_for_eyeballer{subject_i}.(curr_cond_name)(trial_i).non_nan_times_logical_vec= non_nan_times_logical_vec';
end
% Detection of saccades
try
[sacl, ~] = SaccadesExtractor.engbertAlgorithm(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels, ...
curr_requested_vel_threshold, ...
max(ceil(curr_requested_saccade_dur_min*obj.sampling_rates(subject_i)/1000),2));
[sacr, ~] = SaccadesExtractor.engbertAlgorithm(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels, ...
curr_requested_vel_threshold, ...
max(ceil(curr_requested_saccade_dur_min*obj.sampling_rates(subject_i)/1000), 2));
catch exception
exception_identifier= strsplit(exception.identifier,':');
exception_identifier= exception_identifier{2};
if strcmp(exception_identifier, 'msdxZero') || strcmp(exception_identifier, 'msdyZero')
logger.logi(['On condition ', curr_cond_name, ' trial #', num2str(trial_i), ': ', exception.message]);
fillSaccadesStructWithVal([]);
else
logger.loge(['On condition ', curr_cond_name, ' trial #', num2str(trial_i), ': ', exception.message]);
progress_screen.displayMessage('<<ERROR: see log file>>');
end
continue;
end
if isempty(sacl) || isempty(sacr)
fillSaccadesStructWithVal([]);
continue;
end
% Calculate 1d saccades displacements
ampsl= SaccadesExtractor.calcSaccadesAmplitudes(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.baseline_corrected_eye_data, ...
sacl(:,1), sacl(:,2));
ampsr= SaccadesExtractor.calcSaccadesAmplitudes(obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.baseline_corrected_eye_data, ...
sacr(:,1), sacr(:,2));
% Testing for binocular saccades via temporal overlap
[sacl, sacr, ~, ~, kept_left_is, kept_right_is] = SaccadesExtractor.binsacc(sacl, sacr, ampsl, ampsr);
if isempty(sacl) || isempty(sacr)
fillSaccadesStructWithVal([]);
continue;
end
% keeping only saccades displacements corresponding to the binsacc
% kept saccades
ampsl= ampsl(kept_left_is,:);
ampsr= ampsr(kept_right_is,:);
% calculate saccades amplitudes averaged over both eyes
amplitudes = (sqrt(ampsr(:,1).^2 + ampsr(:,2).^2) + sqrt(ampsl(:,1).^2 + ampsl(:,2).^2)) / 2;
non_nan_times= find(non_nan_times_logical_vec);
% find saccades onsets as the earliest non-nan samples between
% left and right found saccades
curr_trial_onsets = non_nan_times(min([sacr(:,1)'; sacl(:,1)'])');
% find saccades offsets as the latest non-nan samples between
% left and right found saccades
curr_trial_offsets = non_nan_times(max([sacr(:,2)'; sacl(:,2)'])');
% keep saccades that occur on time inervals during which
% all samples are non-nan
saccades_on_non_nan_times_idxs = [];
for saccade_idx = 1:numel(curr_trial_onsets)
if all(non_nan_times_logical_vec(curr_trial_onsets(saccade_idx):curr_trial_offsets(saccade_idx)))
saccades_on_non_nan_times_idxs = [saccades_on_non_nan_times_idxs, saccade_idx];
end
end
sacl= sacl(saccades_on_non_nan_times_idxs,:);
sacr= sacr(saccades_on_non_nan_times_idxs,:);
ampsl= ampsl(saccades_on_non_nan_times_idxs,:);
ampsr= ampsr(saccades_on_non_nan_times_idxs,:);
amplitudes= amplitudes(saccades_on_non_nan_times_idxs,:);
curr_trial_onsets = curr_trial_onsets(saccades_on_non_nan_times_idxs);
curr_trial_offsets = curr_trial_offsets(saccades_on_non_nan_times_idxs);
%allow only one saccade within a time window of curr_requested_frequency_max
%keep only the largest saccades within this time window
if numel(curr_trial_onsets) > 1
spoints=curr_requested_frequency_max*(obj.sampling_rates(subject_i)/1000);
donsets= curr_trial_onsets(2:end) - curr_trial_offsets(1:end-1);
maxAmp=amplitudes(1);
inds=[];
iMaxInd=1;
i=2;
while i<=length(curr_trial_onsets)
if donsets(i-1)<spoints
if amplitudes(i)>maxAmp
iMaxInd=i;
maxAmp=amplitudes(i);
end
else %we finished scannign one saccade - add the maximum amplitude
inds=[inds iMaxInd];
iMaxInd=i;
maxAmp=amplitudes(i);
end
i=i+1;
end
inds=[inds iMaxInd];
if ~any(inds)
fillSaccadesStructWithVal([]);
continue;
end
sacl= sacl(inds,:);
sacr= sacr(inds,:);
ampsl= ampsl(inds,:);
ampsr= ampsr(inds,:);
amplitudes= amplitudes(inds,:);
curr_trial_onsets= curr_trial_onsets(inds);
curr_trial_offsets = curr_trial_offsets(inds);
end
% keep saccades whose amplitude is within the range specified by the user
amplitudes_inside_limits_is= curr_requested_amp_low_lim < amplitudes & amplitudes < curr_requested_amp_lim;
if ~any(amplitudes_inside_limits_is)
fillSaccadesStructWithVal([]);
continue;
end
sacl= sacl(amplitudes_inside_limits_is,:);
sacr= sacr(amplitudes_inside_limits_is,:);
ampsl= ampsl(amplitudes_inside_limits_is,:);
ampsr= ampsr(amplitudes_inside_limits_is,:);
amplitudes= amplitudes(amplitudes_inside_limits_is,:);
curr_trial_onsets= curr_trial_onsets(amplitudes_inside_limits_is);
curr_trial_offsets = curr_trial_offsets(amplitudes_inside_limits_is);
% calculate durations for the kept saccades
DR = (sacr(:,2)-sacr(:,1)+1)*1000/obj.sampling_rates(subject_i);
DL = (sacl(:,2)-sacl(:,1)+1)*1000/obj.sampling_rates(subject_i);
curr_trial_saccades_durs= (DR+DL)/2;
% calculate delays between eyes for the kept saccades
curr_trial_delays_between_eyes= (sacr(:,1) - sacl(:,1))*1000/obj.sampling_rates(subject_i);
% calculate directions for the kept saccades
curr_trial_directions = atan2((ampsr(:,2)+ampsl(:,2))/2,(ampsr(:,1)+ampsl(:,1))/2);
% calculate mean speed and peak speed for the kept saccades
curr_trial_saccades_nr= numel(curr_trial_onsets);
curr_trial_peak_vels= zeros(curr_trial_saccades_nr, 1);
vels = zeros(curr_trial_saccades_nr, 1);
left_eye_vels= obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).left_eye.eye_vels;
right_eye_vels= obj.engbert_algorithm_interm_vars{subject_i}.(curr_cond_name)(trial_i).right_eye.eye_vels;
for saccade_i= 1:curr_trial_saccades_nr
left_eye_vels_on_saccade= left_eye_vels(sacl(saccade_i,1):sacl(saccade_i,2), :);
left_eye_vels_on_saccade_szs = sqrt( left_eye_vels_on_saccade(:,1).^2 + left_eye_vels_on_saccade(:,2).^2 );
right_eye_vels_on_saccade= right_eye_vels(sacr(saccade_i,1):sacr(saccade_i,2), :);
right_eye_vels_on_saccade_szs = sqrt( right_eye_vels_on_saccade(:,1).^2 + right_eye_vels_on_saccade(:,2).^2 );
vels(saccade_i) = (mean(left_eye_vels_on_saccade_szs) + mean(right_eye_vels_on_saccade_szs))/2;
left_eye_peak_vel= max(left_eye_vels_on_saccade_szs);
right_eye_peak_vel= max(right_eye_vels_on_saccade_szs);
curr_trial_peak_vels(saccade_i)= (left_eye_peak_vel + right_eye_peak_vel)/2;
end
% save all calculated variables in the output struct
saccades_struct{subject_i}.(curr_cond_name)(trial_i).onsets= curr_trial_onsets;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).offsets= curr_trial_offsets;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).durations = curr_trial_saccades_durs;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).delays_between_eyes= curr_trial_delays_between_eyes;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).amplitudes= amplitudes;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).directions= -curr_trial_directions;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).velocities= vels;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).peak_vels= curr_trial_peak_vels;
end
end
progress_screen.addProgress(0.2*progress_contribution/obj.subjects_nr);
end
% check if any of the triggers requested was found for any of the subjects
was_any_trigger_ever_found = false;
for subject_i = 1:obj.subjects_nr
if ~isempty(saccades_struct{subject_i})
was_any_trigger_ever_found = true;
break;
end
end
% if none of the triggers requested was found for any of the subjects, skip the data inspection
if perform_eyeballing && was_any_trigger_ever_found
% aggregate saccades extraction parameters to a structure for the Eyeballer ctor
manual_saccade_search_params.manual_saccade_search_func = @SaccadesExtractor.findSaccadeForcefullyOnDefinedTimesByEngbert;
manual_saccade_search_params.manual_saccade_search_func_input = manual_saccades_search_func_params_for_eyeballer;
manual_saccade_search_params.saccades_detecetion_algorithm_params.amp_lim = curr_requested_amp_lim;
manual_saccade_search_params.saccades_detecetion_algorithm_params.amp_low_lim = curr_requested_amp_low_lim;
manual_saccade_search_params.saccades_detecetion_algorithm_params.vel_threshold = curr_requested_vel_threshold;
manual_saccade_search_params.saccades_detecetion_algorithm_params.saccade_dur_min = curr_requested_saccade_dur_min;
manual_saccade_search_params.saccades_detecetion_algorithm_params.frequency_max = curr_requested_frequency_max;
manual_saccade_search_params.saccades_detecetion_algorithm_params.low_pass_filter = curr_requested_low_pass_filter;
% create an Eyeballer
eyeballer= Eyeballer(@eyeballer_save_func, raw_eye_data_for_eyeballer, detection_performed, eyeballer_timeline_left_offset, obj.sampling_rates, ...
manual_saccade_search_params, saccades_struct, eyeballer_display_range_multiplier, ...
obj.eyeballer_main_gui_pos, obj.EYEBALLER_MAIN_GUI_BACKGROUND_COLOR);
% run the data inspection
[was_new_extraction_requested_by_eyeballer, new_extraction_params]= eyeballer.run();
if ~was_new_extraction_requested_by_eyeballer
is_extraction_go= false;
[saccades_struct, eyeballing_stats]= eyeballer.getSaccadesStruct();
else
curr_requested_amp_lim= new_extraction_params.amp_lim;
curr_requested_amp_low_lim= new_extraction_params.amp_low_lim;
curr_requested_vel_threshold= new_extraction_params.vel_threshold;
curr_requested_saccade_dur_min = new_extraction_params.min_dur_for_saccade;
curr_requested_frequency_max = new_extraction_params.min_dur_between_saccades;
curr_requested_low_pass_filter = new_extraction_params.low_pass_filter;
saccades_struct= cell(1, obj.subjects_nr);
eyeballer_display_range_multiplier = new_extraction_params.eyeballer_display_range_multiplier;
end
else
is_extraction_go= false;
end
end
%add a field for the saccades' onsets relative to the start of the session.
for subject_i= 1:obj.subjects_nr
if isempty(saccades_struct{subject_i})
continue;
end
conds_names= fieldnames(saccades_struct{subject_i});
for cond_i= 1:numel(conds_names)
curr_cond_name= conds_names{cond_i};
for trial_i= 1:numel(saccades_struct{subject_i}.(curr_cond_name))
saccades_struct{subject_i}.(curr_cond_name)(trial_i).onset_from_session_start= ...
saccades_struct{subject_i}.(curr_cond_name)(trial_i).onsets + ...
subjects_data_structs{subject_i}.(curr_cond_name)(trial_i).onset_from_session_start;
end
end
end
function fillSaccadesStructWithVal(val)
% fills a saccades data structure with a specific value for the currently iterated
% subject-condition-trial combination.
% input:
% * val -> the value to fill the saccades data structure with
saccades_struct{subject_i}.(curr_cond_name)(trial_i).onsets= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).offsets= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).durations = val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).delays_between_eyes= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).amplitudes= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).directions= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).velocities= val;
saccades_struct{subject_i}.(curr_cond_name)(trial_i).peak_vels= val;
end
function eyeballer_save_func(saccades_struct)
% saves the data handled by an eyeballer session for each subject
% input:
% * saccades_struct -> the saccades data generated with the eyeballer
for saved_subject_i= 1:numel(obj.subjects_etas)
curr_saccades_analysis_struct.raw_eye_data= raw_eye_data_for_eyeballer{saved_subject_i};
curr_saccades_analysis_struct.manual_saccades_search_func_params= manual_saccades_search_func_params_for_eyeballer{saved_subject_i};
curr_saccades_analysis_struct.saccades_struct= saccades_struct{saved_subject_i};
curr_saccades_analysis_struct.eye_data_struct= eye_data_struct{saved_subject_i};
obj.subjects_etas{saved_subject_i}.registerSaccadesAnalysis(curr_saccades_analysis_struct);
obj.subjects_etas{saved_subject_i}.save(etas_full_paths{saved_subject_i});
end
end
end
end
methods (Access= private, Static)
function v = vecvel(xx, type, sampling_rate)
% calculate eyes velocities per sample
% input:
% * xx -> matrix of eye positions.
% xx(:,1) - x coordinates. xx(:,2) - y coordinates.
% * type -> int representing the algorithm to calculate the velocities with.
% * sampling_rate -> the sampling rate of the recording
%
% output:
% * v -> matrix of velocities
% v(:,1) - x axis velocities. v(:,2) - y axis velocities.
N = length(xx); % length of the time series
%v = zeros(N,2);
v = zeros(size(xx));
switch type
case 1
v(2:N-1,:) = sampling_rate/2*(xx(3:end,:) - xx(1:end-2,:));
case 2
v(3:N-2,:) = sampling_rate/6*(xx(5:end,:) + xx(4:end-1,:) - xx(2:end-3,:) - xx(1:end-4,:));
v(2,:) = sampling_rate/2*(xx(3,:) - xx(1,:));
v(N-1,:) = sampling_rate/2*(xx(end,:) - xx(end-2,:));
case 3
if sampling_rate == 1000
n = 10;
Xm2 = (xx(n-9:end-18,:) + xx(n-8:end-17,:) + xx(n-7:end-16,:) + xx(n-6:end-15,:)) / 4;
Xm1 = (xx(n-5:end-14,:) + xx(n-4:end-13,:) + xx(n-3:end-12,:) + xx(n-2:end-11,:)) / 4;
Xp1 = (xx(n+5:end-4,:) + xx(n+4:end-5,:) + xx(n+3:end-6,:) + xx(n+2:end-7,:)) / 4;
Xp2 = (xx(n+9:end-0,:) + xx(n+8:end-1,:) + xx(n+7:end-2,:) + xx(n+6:end-3,:)) / 4;
v(n:N-(n-1),:) = (sampling_rate*(Xp2+Xp1-Xm1-Xm2)) / 24;
% recursively call the SAMPLING==500 case below, just so I don't have
% to write the huge chunk of code. Not ideal practice but isn't too bad
% in this case
v_strt = vecvel(xx(1:13,:), 500,3)*2;
v_end = vecvel(xx(N-12:N,:),500,3)*2;
v(1:9, :) = v_strt(1:9,:);
v(end-8:end,:) = v_end(end-8:end,:);
elseif sampling_rate == 500
n = 5;
Xm2 = (xx(n-4:end-8,:) + xx(n-3:end-7,:)) / 2;
Xm1 = (xx(n-2:end-6,:) + xx(n-1:end-5,:)) / 2;
Xp1 = (xx(n+2:end-2,:) + xx(n+1:end-3,:)) / 2;
Xp2 = (xx(n+4:end-0,:) + xx(n+3:end-1,:)) / 2;
v(n:N-(n-1),:) = (sampling_rate*(Xp2+Xp1-Xm1-Xm2)) / 12;
v(3:4,:) = sampling_rate/6*(xx(5:6,:) + xx(4:5,:) - xx(2:3,:) - xx(1:2,:));
v(N-3:N-2,:) = sampling_rate/6*(xx(N-1:N,:) + xx(N-2:N-1,:) - xx(N-4:N-3,:) - xx(N-5:N-4,:));
v(2,:) = sampling_rate/2*(xx(3,:) - xx(1,:));
v(N-1,:) = sampling_rate/2*(xx(end,:) - xx(end-2,:));
% this is the same above and might be a little easier to read, but avoiding
% recursion in Matlab is generally a good idea
% v_strt = vecvel(xx(1:6,:), 500, 2);
% v_end = vecvel(xx(end-5:end,:), 500,2);
% v(1:4) = v_strt(1:4,:);
% v(end-3:end) = v_end(end-3:end,:);
end
end
end
%sac(r,1:2) : [onset offset] of saccade r
function [sac, radius]= engbertAlgorithm(vel,VFAC,MINDUR) % MINDUR actually means minimum samples number here
msdx = sqrt( nanmedian(vel(:,1).^2) - (nanmedian(vel(:,1)))^2 );
msdy = sqrt( nanmedian(vel(:,2).^2) - (nanmedian(vel(:,2)))^2 );
if msdx<realmin
msdx = sqrt( nanmean(vel(:,1).^2) - (nanmean(vel(:,1)))^2 );
if msdx<realmin
throw( MException('microsacc:msdxZero', 'msdx<realmin in microsacc.m') );
end
end
if msdy<realmin
msdy = sqrt( nanmean(vel(:,2).^2) - (nanmean(vel(:,2)))^2 );
if msdy<realmin
throw( MException('microsacc:msdyZero', 'msdy<realmin in microsacc.m') );
end
end
radiusx = VFAC*msdx;
radiusy = VFAC*msdy;
radius = [radiusx radiusy];
% compute test criterion: ellipse equation
test = (vel(:,1)/radiusx).^2 + (vel(:,2)/radiusy).^2;
indx = find(test>1);
% determine saccades
N = length(indx);
sac = [];
nsac = 0;
dur = 1;
a = 1;
k = 1;
while k<N
if indx(k+1)-indx(k)==1
dur = dur + 1;
else
if dur>=MINDUR
nsac = nsac + 1;
b = k;
sac(nsac,:) = [indx(a) indx(b)];
end
a = k+1;
dur = 1;
end
k = k + 1;
end
if dur>=MINDUR
nsac = nsac + 1;
b = k;
sac(nsac,:) = [indx(a) indx(b)];
end
end
%amps(r,1:2) : [dX dY] of saccade r
function amps= calcSaccadesAmplitudes(x, onsets, offsets)
saccades_nr= numel(onsets);
amps= zeros(saccades_nr,2);
for saccade_i=1:saccades_nr
i = onsets(saccade_i):offsets(saccade_i);
[minx, ix1] = min(x(i,1));
[maxx, ix2] = max(x(i,1));
[miny, iy1] = min(x(i,2));
[maxy, iy2] = max(x(i,2));
dX = sign(ix2-ix1)*(maxx-minx);
dY = sign(iy2-iy1)*(maxy-miny);
amps(saccade_i,:) = [dX dY];
end
end
function [sacl_revised, sacr_revised, monol, monor, kept_left_is, kept_right_is] = binsacc(sacl, sacr, ampsl, ampsr)
if size(sacr,1)*size(sacl,1)>0
% determine saccade clusters
TR = max(sacr(:,2));
TL = max(sacl(:,2));
T = max([TL TR]);
s = zeros(1,T+1);
for i=1:size(sacl,1)
s(sacl(i,1)+1:sacl(i,2)) = 1;
end
for i=1:size(sacr,1)
s(sacr(i,1)+1:sacr(i,2)) = 1;
end
s(1) = 0;
s(end) = 0;
m = find(diff(s~=0));
N = length(m)/2;
m = reshape(m,2,N)';
% determine binocular saccades
NB = 0;
NR = 0;
NL = 0;
sacr_revised= [];
sacl_revised= [];
monol = [];
monor = [];
kept_left_is= [];
kept_right_is= [];
for i=1:N
l = find( m(i,1)<=sacl(:,1) & sacl(:,2)<=m(i,2) );
r = find( m(i,1)<=sacr(:,1) & sacr(:,2)<=m(i,2) );
if length(l)*length(r)>0
ampr = sqrt(ampsr(r,1).^2+ampsr(r,2).^2);
ampl = sqrt(ampsl(l,1).^2+ampsl(l,2).^2);
[h, ir] = max(ampr);
[h, il] = max(ampl);
NB = NB + 1;
kept_right_is(NB)= r(ir);
kept_left_is(NB)= l(il);
sacr_revised(NB,:) = sacr(r(ir),:) ;
sacl_revised(NB,:) = sacl(l(il),:) ;
else
% determine monocular saccades
if length(l)==0
NR = NR + 1;
monor(NR,:) = sacr(r,:);
end
if length(r)==0
NL = NL + 1;
monol(NL,:) = sacl(l,:);
end
end
end
else
% special cases of exclusively monocular saccades
if size(sacr,1)==0
sacr_revised= [];
sacl_revised= [];
monor = [];
monol = sacl;
end
if size(sacl,1)==0
sacr_revised= [];
sacl_revised= [];
monol = [];
monor = sacr;
end
end
end
function saccade_data= findSaccadeForcefullyOnDefinedTimesByEngbert(eye_data, times)
mapped_times= mapTimesToValidEyeDataTimeSpace();
eye_data.left_eye.eye_vels= eye_data.left_eye.eye_vels(eye_data.non_nan_times_logical_vec,:);
eye_data.right_eye.eye_vels= eye_data.right_eye.eye_vels(eye_data.non_nan_times_logical_vec,:);
eye_data.left_eye.baseline_corrected_eye_data= eye_data.left_eye.baseline_corrected_eye_data(eye_data.non_nan_times_logical_vec,:);
eye_data.right_eye.baseline_corrected_eye_data= eye_data.right_eye.baseline_corrected_eye_data(eye_data.non_nan_times_logical_vec,:);
[left_eye_onset, left_eye_offset]= findSaccadeTimes(eye_data.left_eye.eye_vels);
if isempty(left_eye_onset)
saccade_data= [];
return;
end
[right_eye_onset, right_eye_offset]= findSaccadeTimes(eye_data.right_eye.eye_vels);
if isempty(left_eye_onset)
saccade_data= [];
return;
end
saccade_data.onset = min([right_eye_onset, left_eye_onset]);
saccade_data.offset = max([right_eye_offset, left_eye_offset])';
saccade_data.onset= foundSaccadeTimeToOriginalTimeSpace(saccade_data.onset);
saccade_data.offset= foundSaccadeTimeToOriginalTimeSpace(saccade_data.offset);
left_eye_amp= SaccadesExtractor.calcSaccadesAmplitudes(eye_data.left_eye.baseline_corrected_eye_data(mapped_times,:), left_eye_onset, left_eye_offset);
right_eye_amp= SaccadesExtractor.calcSaccadesAmplitudes(eye_data.right_eye.baseline_corrected_eye_data(mapped_times,:), right_eye_onset, right_eye_offset);
saccade_data.amplitude = (sqrt(right_eye_amp(:,1).^2+right_eye_amp(:,2).^2)+sqrt(left_eye_amp(:,1).^2+left_eye_amp(:,2).^2))/2;
DR = right_eye_offset - right_eye_onset + 1;
DL = left_eye_offset - left_eye_onset + 1;
saccade_data.duration= (DR+DL)/2;
saccade_data.delay_between_eyes= right_eye_onset - right_eye_offset;
saccade_data.direction = atan2((right_eye_amp(:,2)+left_eye_amp(:,2))/2,(right_eye_amp(:,1)+left_eye_amp(:,1))/2);
left_eye_vels_on_saccade= eye_data.left_eye.eye_vels(left_eye_onset:left_eye_offset, :);
left_eye_vels_on_saccade_szs = sqrt( left_eye_vels_on_saccade(:,1).^2 + left_eye_vels_on_saccade(:,2).^2 );
right_eye_vels_on_saccade= eye_data.right_eye.eye_vels(right_eye_onset:right_eye_offset, :);
right_eye_vels_on_saccade_szs = sqrt( right_eye_vels_on_saccade(:,1).^2 + right_eye_vels_on_saccade(:,2).^2 );
saccade_data.velocity = (mean(left_eye_vels_on_saccade_szs) + mean(right_eye_vels_on_saccade_szs))/2;
left_eye_peak_vel= max(left_eye_vels_on_saccade_szs);
right_eye_peak_vel= max(right_eye_vels_on_saccade_szs);
saccade_data.peak_vel= (left_eye_peak_vel + right_eye_peak_vel)/2;
function mapped_times= mapTimesToValidEyeDataTimeSpace()
pivot_i= floor( (1+numel(times))/2 );
times_before_pivot= times(1:pivot_i-1);
good_time_samples_nr= sum(eye_data.non_nan_times_logical_vec(times_before_pivot));
times_before_pivot_nr= numel(times_before_pivot);
start_time= times(1);
while start_time>1 && good_time_samples_nr<times_before_pivot_nr
start_time= start_time - 1;
good_time_samples_nr= good_time_samples_nr + eye_data.non_nan_times_logical_vec(start_time);
end
times_from_pivot_onwards= times(pivot_i:end);
good_time_samples_nr= good_time_samples_nr + sum(eye_data.non_nan_times_logical_vec(times_from_pivot_onwards));
end_time= times(end);
while end_time<numel(eye_data.non_nan_times_logical_vec) && good_time_samples_nr<numel(times)
end_time= end_time + 1;
good_time_samples_nr= good_time_samples_nr + eye_data.non_nan_times_logical_vec(end_time);
end
if start_time==1
mapped_start_time= 1;
else
mapped_start_time= sum(eye_data.non_nan_times_logical_vec(1:start_time));
end
mapped_end_time= sum(eye_data.non_nan_times_logical_vec(1:end_time));
mapped_times= mapped_start_time:mapped_end_time;
end
function original_time= foundSaccadeTimeToOriginalTimeSpace(found_saccade_time)
saccade_time_in_valid_data_time_space= found_saccade_time + mapped_times(1) - 1;
accumulator= 0;
time= 0;
while accumulator<saccade_time_in_valid_data_time_space
time= time + 1;
accumulator= accumulator + eye_data.non_nan_times_logical_vec(time);
end
original_time= time;
end
function [onset, offset]= findSaccadeTimes(eye_vels)
msdx = sqrt( nanmedian(eye_vels(mapped_times,1).^2) - (nanmedian(eye_vels(mapped_times,1)))^2 );
msdy = sqrt( nanmedian(eye_vels(mapped_times,2).^2) - (nanmedian(eye_vels(mapped_times,2)))^2 );
if msdx<realmin
msdx = sqrt( nanmean(eye_vels(mapped_times,1).^2) - (nanmean(eye_vels(mapped_times,1)))^2 );
if msdx<realmin
throw( MException('microsacc:msdxZero', 'msdx<realmin in microsacc.m') );
end
end
if msdy<realmin
msdy = sqrt( nanmean(eye_vels(mapped_times,1).^2) - (nanmean(eye_vels(mapped_times,1)))^2 );
if msdy<realmin
throw( MException('microsacc:msdyZero', 'msdy<realmin in microsacc.m') );
end
end
for vel_threshold= 6:-1:1 % TODO: FIX... the 6 means a total breakdown
radiusx = vel_threshold*msdx;
radiusy = vel_threshold*msdy;
% compute test criterion: ellipse equation
test = (eye_vels(mapped_times,1)/radiusx).^2 + (eye_vels(mapped_times,1)/radiusy).^2;
saccadic_time_points = find(test>1);
if isempty(saccadic_time_points)
continue;
else
onsets_dists_from_mid_time = abs(saccadic_time_points - floor(length(mapped_times)/2));
closest_saccadic_time_point_to_mid = saccadic_time_points(onsets_dists_from_mid_time == min(onsets_dists_from_mid_time));
curr_tested_time= closest_saccadic_time_point_to_mid + 1;
while curr_tested_time<=length(mapped_times) && test(curr_tested_time)>1
curr_tested_time= curr_tested_time + 1;
end
if curr_tested_time>length(mapped_times)
continue;
else
offset= curr_tested_time - 1;
curr_tested_time = closest_saccadic_time_point_to_mid;
while curr_tested_time >= 1 && test(curr_tested_time)>1
curr_tested_time= curr_tested_time - 1;
end
if curr_tested_time == 0
continue;
else
onset = curr_tested_time;
return;
end
end
end
end
%got here if couldnt find a saccade
onset= [];
offset= [];
end
end
end
end