ET_PDToolkit/PDToolkit/old

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%% define a new subject
s(1) = PDSubject('/Users/marsman/Documents/Werk/NIC/Teaching/Studenten/Nora_Parigi/data/Subject 5');
s(2) = PDSubject('/Users/marsman/Documents/Werk/NIC/Teaching/Studenten/Nora_Parigi/data/Subject 4')
%% load data
s(1) = s(1).loadData();
s(2) = s(2).loadData();
%% define trial markers
s = s.setPattern('trial start', 'Fixation6s starting');
s = s.setPatternRelativeToPattern('trial start', 'trial end', +10.5e3); % dependency of trial_start
%s = s.setPattern('trial end', 'Fixation15s starting');
s = s.setPattern('stimulus onset', 'Stimulus Presentation');
s = s.setPattern('stimulus offset', 'Fixation15s ending');
s = s.setLabel('information', 'Valence %s');
s = s.setLabel('information', 'TRIAL_VAR stim %s');
% s = s.setPattern('baseline start');
% s = s.setPattern('baseline end');
%% parse trials
s = s.rebuild;
% baseline is defined based on minimum value for baseline signal
e = PDExperiment(s, 'Min');
% baseline is defined based on average value for baseline signal
% e = PDExperiment(s, 'Average');
% calculate baseline corrected and filtered signals
e = PDExperiment(s, 'Average');
e = e.preprocess();
plot(e); %% plot trials of the experiment
e = build_averages(e);