function[baseline] = getBaseline(trial, settings)
%% Based on baseline interval samples, this routine 
%  determines a baseline value that can be subsequently
%  used to correct the raw signal
%
%  In this routine, currently 4 methods are available:
%  1) no correction
%  2) mean value across baseline samples
%  3) minimum value of all baseline samples
%  4) mean value for the lowest n percent of baseline samples
%  5) offset value for a fitted line of all baseline samples

baseline=NaN;

if isempty(trial.baseline_onset)
    trial.baseline_onset = trial.trial_start;
end

if isempty(trial.baseline_offset) 
    
    if isempty(trial.stimulus_offset)
        warning('Stimulus onset has not been set! Bailing out...');
        return;
    end
    
    trial.baseline_offset = trial.stimulus_onset;
end

eyes = fieldnames(trial.data);

for e = 1:length(eyes)
    eye = eyes{e};
    signal = trial.data.(eye).uncorrected.interpolated;
    
    if isempty(signal)
        baseline_signal = NaN;
    else
        baseline_signal = trial.data.(eye).uncorrected.interpolated(intersect(find(trial.time >= trial.baseline_onset),...
                                                                find(trial.time < trial.baseline_offset)));
    end
end


switch settings.BaselineCorrection
    
    case 1 
        baseline = NaN; % No correction
    case 2 % Mean value 
        baseline = nanmean(baseline_signal);
        
    case 3 % Min-value between baseline
        baseline = min(baseline_signal);
        
    case 4 % Percentile 
         
        percentage = settings.BaselineCorrectionPercentile;
        perc_val   = prctile(baseline_signal, percentage);
        
        baseline = perc_val; %mean(baseline_signal(find(baseline_signal <= perc_val)));
    case 5 % Offset (fitted line)
        P = polyfit(1:length(baseline_signal), baseline_signal, 0);
        baseline = P(1);
    case 6 % Fixed point
        baseline = baseline_signam
end
end