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- clear all
- addpath(genpath(['subfunctions/']))
- mainfolder=''
- %All partis
- partis_sub= [{'pi03'};{'p04'};{'p05'};{'p06'};{'p07'};{'p08'};...
- {'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'};{'p53'};{'p54'};{'p55'};{'p56'};{'p57'}...
- ]
- %participant 09 not included (wearing glasses a considerable amount of signal missed)
- %participant 33 not included (really low accuracy, feeling unwell and data to be reconstructed)
- %participant 52 not included (the participants had glasses full of scratches, the eyetracker did not work)
- %% Excluding based on accuracy
- accuracy_plots=0;
- exclude;
- %% Excluding outliers with a high percetange of NaN in the signal
- figure_NaNs=1;
- check_percentage_of_NaN_eyes_2022
- %% Saving
- save('good_partis.mat','good_partis')
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