mpib_memoreye/preprocessing_scripts/Preprocessing_pipeline_epoch_object_onset_2022.m

% Preprocessing eye data

clear all

subjects_eyedata={'pil01';'pil02';'pi03';'p04';'p05';'p06';'p07';'p08';'p09';...
    'p10';'p11';'p12';'p13';'p14';'p15';'p16';'p17';'p18';...
    'p19';'p20';'p21';'p22';'p23';'p24';'p25';'p26';'p27';'p28';'p29';'p30'...
    ;'p31';'p32';'p34';'p35';'p36';'p37';'p38';'p39';'p40';'p41';...
    'p42';'p43';'p44';'p45';'p46';'p47';'p48';'p49';'p50';'p51';'p52';...
    'p53';'p54';'p55';'p56';'p57'};

subjects_sub= [{'pil01'};{'pil02'};{'pi03'};{'p04'};{'p05'};{'p06'};{'p07'};{'p08'};{'p09'};...
    {'p10'};{'p11'};{'p12'};{'p13'};{'p14'};{'p15'};{'p16'};{'p17'};{'p18'};...
    {'p19'};{'p20'};{'p21'};{'p22'};{'p23'};{'p24'};{'p25'};{'p26'};{'p27'};{'p28'};{'p29'};{'p30'};...
    {'p31'};{'p32'};{'p34'};{'p35'};{'p36'};{'p37'};...
    {'p38'};{'p39'};{'p40'};{'p41'};{'p42'};{'p43'};{'p44'};{'p45'};{'p46'};{'p47'};...
    {'p48'};{'p49'};{'p50'};{'p51'};{'p52'};{'p53'};{'p54'};{'p55'};{'p56'};{'p57'}...
    ]
%%
for ppp=1:length(subjects_eyedata) %per participant

%%  Trigger info (EEG and Eye-Tracking)

% % Changes: Gaze-off triggers removed (this info is included in the
% output file). Triggers for "Thanks" messages included.
    
% % -orientations (1:16): 001:1:016
% % -sample item code (1:3): 20:20:60
% % -1st/2nd sample position: 000/100
% %
% % i.e. item 2 at 45?? in first sample position would be 42; in 2nd sample position t would be 142
% % 
% % - first retrocue: 1/2 (first or second item respectively)
% % - second retrocue (for the uncued item): 3/4 (first or second item respectively)
% %
% % - thanks message after test for the CUED item: 5
% % - thanks message after test for the UNCUED item: 6
% % 
% % -Object onset in abstract test for the CUED item: 201:209 (object identity from 1 to 9)
% % -Object onset in abstract test for the UNCUED item: 211:219 (object identity from 1 to 9)
% %
% % -Object onset in concrete test for the CUED item: 220/221 (for left/right tilted)
% % -Object onset in concrete test for the UNCUED item: 223/224 (for left/right tilted)
% % 
% % -230 Ping - First delay
% % -231 Oddball Ping - First delay
% %
% % -232 Ping - Second delay
% % -233 Oddball - Second delay
% % 
% % -241 left button press
% % -242 right button press
% % -243 up/accept button press

% % -244 feedback cued item
% % -245 feedback uncued item
% %
% % -246 beginning/end of the task


partID_prepro=subjects_eyedata{ppp};
partID_outpufile=subjects_sub{ppp};

mainfolder=''

vinstoload=[mainfolder 'eyelink_matlab/' partID_prepro '.mat']
load(vinstoload)

if strcmp( partID_outpufile,'p11')
    load([mainfolder '/logfiles/resultfile_p11_table_repaired.mat'])
    subtable=sub
else

sub = tdfread([mainfolder 'logfiles/resultfile_' partID_outpufile '.txt'],'tab'); %Logfile for this participant
subtable=struct2table(sub);
end

% Setting all possible triggers (just the first item on screen)

counter=1
allposi=[]
for ori = 1:16
    for item=1:3
        for posi=1       
            positri=ori+(item*20)+((posi==2)*100);
            postri(counter)=(positri);
            allposi=[allposi postri];
        end
    end
end

if ppp>=20
allposi=[allposi 100];
end

cfg.data=mdat
cfg.triggers=allposi
cfg.prestim = 1000 %time prestim in ms
cfg.poststim = 10000 %time prestim in ms

data_epoch= maketrials_eyetracking_V1(cfg)

%%

%% Using logfile to add info of interest to the EEG data

% Since we have 2 trigger per trial, let create a better table
t_counter=0;

stimuli_info=[];
rotlist=unique(subtable.object_1_rot)

  for t = 1:length(subtable.trial)
       
       found = find(subtable.trial == t);
                
        t_counter=t_counter+1;       
        stimuli_info.trial(t_counter,1) = subtable.trial(t); %Trial number;
        stimuli_info.object_1_id(t_counter,1) = subtable.object_1_id(t); %Object presented first;
        stimuli_info.object_1_rot(t_counter,1) = subtable.object_1_rot(t); %Object presented first;
        stimuli_info.object_1_position(t_counter,1) = 1; %Object presented first
        stimuli_info.object_1_trigger(t_counter,1) = subtable.trigger_object_1(t); %Object presented first;
        stimuli_info.object_1_rot_n(t_counter,1) = find(subtable.object_1_rot(t)==rotlist); %Object presented first;
                       
        stimuli_info.object_2_id(t_counter,1) = subtable.object_2_id(t); %Object presented 2;
        stimuli_info.object_2_rot(t_counter,1) = subtable.object_2_rot(t); %Object presented 2;
        stimuli_info.object_2_position(t_counter,1) = 2; %Object presented 2
        stimuli_info.object_2_trigger(t_counter,1) = subtable.trigger_object_2(t); %Object presented first;
        stimuli_info.object_2_rot_n(t_counter,1) = find(subtable.object_2_rot(t)==rotlist); %Object presented first;

        stimuli_info.object_cued_id(t_counter,1) =subtable.object_cued_id(t);
        stimuli_info.object_cued_rot(t_counter,1) =subtable.object_cued_rot(t);
        
        stimuli_info.object_uncued_id(t_counter,1) =subtable.object_uncued_id(t);
        stimuli_info.object_uncued_rot(t_counter,1) =subtable.object_uncue_rot(t);
        
        stimuli_info.retrocue(t_counter,1) =subtable.retro_cue(t);       
        stimuli_info.retrocue_uncued(t_counter,1) =subtable.retro_cue_uncued(t);

        stimuli_info.testing_uncued(t_counter,1) =subtable.testing_uncued(t);
             
  end
  
  
  stimuli_info=struct2table(stimuli_info) %double check
  
  
  %%
  if sum(stimuli_info.object_1_trigger'~=data_epoch.trigger_onset) %this should be 0
      error('Mismatch trials and triggers')
  end
  
   %% Recentered using mean

 data_epoch_recenter=data_epoch;
 
 allx=data_epoch.eyedat(:,1,:);
 mx=nanmean(allx(:))
 
 ally=data_epoch.eyedat(:,2,:);
 my=nanmean(ally(:))

 data_epoch_recenter.eyedat(:,1,:)=data_epoch.eyedat(:,1,:)-mx;
 data_epoch_recenter.eyedat(:,2,:)=data_epoch.eyedat(:,2,:)-my;
 
%% if we need to visualise
%  figure; plot(squeeze(nanmean(data_epoch_recenter.eyedat(:,1,:),1)));hold on;
%  plot(squeeze(nanmean(data_epoch_recenter.eyedat(:,2,:),1)));

%% Using a threshold to excluded not centered gaze

data_epoch_thr=data_epoch_recenter;

th=100; %pixel threshold

cXY = [0,0]; %this should be the new center after re-centering

Euc_dist=NaN(size(data_epoch_recenter.eyedat,1),size(data_epoch_recenter.eyedat,3));

for tr = 1:size(data_epoch_recenter.eyedat,1) %trial
    for tt=1:size(data_epoch_recenter.eyedat,3) %time
        dataXY = [data_epoch_recenter.eyedat(tr,1,tt),data_epoch_recenter.eyedat(tr,2,tt)];
        Euc_dist(tr,tt) = norm( cXY - dataXY );
        
        if Euc_dist(tr,tt)>th
            data_epoch_thr.eyedat(tr,:,tt)=NaN;
        end
        
    end
end

%% if we need to visualise
%  figure; plot(squeeze(nanmean(data_epoch_thr.eyedat(:,1,:),1)));hold on;
%  plot(squeeze(nanmean(data_epoch_thr.eyedat(:,2,:),1)));

  %%
  data_epoch_thr.trialinfo = [stimuli_info]; %adding trial info
  
 %% Done. Save it.

partID=subjects_eyedata{ppp}
placetosave=[mainfolder 'eyelink_preprocessed/mean_recentered_th100/object_1_onset_11_seconds/' partID '.mat']
save (placetosave,'data_epoch_thr','-v7.3')
 
 end