68 lines
1.9 KiB
Matlab
68 lines
1.9 KiB
Matlab
function[baseline] = getBaseline(trial, settings)
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%% Based on baseline interval samples, this routine
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% determines a baseline value that can be subsequently
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% used to correct the raw signal
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%
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% In this routine, currently 4 methods are available:
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% 1) no correction
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% 2) mean value across baseline samples
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% 3) minimum value of all baseline samples
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% 4) mean value for the lowest n percent of baseline samples
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% 5) offset value for a fitted line of all baseline samples
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baseline=NaN;
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if isempty(trial.baseline_onset)
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trial.baseline_onset = trial.trial_start;
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end
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if isempty(trial.baseline_offset)
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if isempty(trial.stimulus_offset)
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warning('Stimulus onset has not been set! Bailing out...');
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return;
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end
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trial.baseline_offset = trial.stimulus_onset;
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end
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eyes = fieldnames(trial.data);
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for e = 1:length(eyes)
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eye = eyes{e};
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signal = trial.data.(eye).uncorrected.interpolated;
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if isempty(signal)
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baseline_signal = NaN;
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else
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baseline_signal = trial.data.(eye).uncorrected.interpolated(intersect(find(trial.time >= trial.baseline_onset),...
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find(trial.time < trial.baseline_offset)));
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end
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end
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switch settings.BaselineCorrection
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case 1
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baseline = NaN; % No correction
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case 2 % Mean value
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baseline = nanmean(baseline_signal);
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case 3 % Min-value between baseline
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baseline = min(baseline_signal);
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case 4 % Percentile
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percentage = settings.BaselineCorrectionPercentile;
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perc_val = prctile(baseline_signal, percentage);
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baseline = perc_val; %mean(baseline_signal(find(baseline_signal <= perc_val)));
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case 5 % Offset (fitted line)
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P = polyfit(1:length(baseline_signal), baseline_signal, 0);
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baseline = P(1);
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case 6 % Fixed point
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baseline = baseline_signam
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end
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end
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