diff --git a/Measures/FOOOFer.m b/Measures/FOOOFer.m
index 9ab5bea..794b2c2 100644
--- a/Measures/FOOOFer.m
+++ b/Measures/FOOOFer.m
@@ -1,199 +1,201 @@
-%% FOOOFer
-% This function parameterizes the neural power spectra using the Haller et
-% al. method.
-%
-% FOOOFer(fs, cf, nEpochs, dt, inDir, tStart, outTypes)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch
-% outTypes is the list of variables to save (offset, exponent,
-% peak parameters, gaussian parameters, error, r squared, fooofed
-% spectrum, bg fit, power spectrum), writen in an array as strings
-% in the same order as their output directories in the variable
-% outDirs (es. outDirs=["C:\offset\", "C:\exponent", "C:\bg_fit"],
-% outTypes=["offset", "exponent", "bg fit"])
-%
-% NB: peak and gaussian params will contain a matrix where every group of 3
-% numbers are relative to one peak (CF, Amp, BW) and the others zeros
-% are utilized to export only one matrix for each subject
-
-
-function FOOOFer(fs, cf, nEpochs, dt, inDir, tStart, outTypes, maxPeaks)
- switch nargin
- case 5
- tStart = 0;
- outTypes = "";
- maxPeaks = (cf(end)-cf(1))*2;
- case 6
- outTypes = "";
- maxPeaks = (cf(end)-cf(1))*2;
- case 7
- maxPeaks = (cf(end)-cf(1))*2;
- end
-
- f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
-
- dt = fs*dt;
- tStart = tStart*fs+1;
-
- askList = 'Insert the background measures separated by a comma';
-
- inDir = path_check(inDir);
- cases = define_cases(inDir);
- outTypes = string(outTypes);
-
- if strcmp(outTypes, "")
- outTypes = [];
- sup = string(inputdlg(askList));
- if stcmp(sup, "exit")
- return
- end
- sup = split(sup,",");
- for i = 1:length(sup)
- if contains(sup(i, 1), ["Exp", "exp", "EXP"])
- outTypes = [outTypes, "exp"];
- elseif contains(sup(i, 1), ["off", "OFF", "Off"])
- outTypes = [outTypes, "off"];
- elseif contains(sup(i, 1), ["peak", "PEAK", "Peak"])
- outTypes = [outTypes, "peak_params"];
- elseif contains(sup(i, 1), ["gau", "GAU", "Gau"])
- outTypes = [outTypes, "gaussian_params"];
- elseif contains(sup(i, 1), ["err", "ERR", "Err"])
- outTypes = [outTypes, "error"];
- elseif contains(sup(i, 1), ["squar", "Squar", "SQUAR"])
- outTypes = [outTypes, "r_squared"];
- elseif contains(sup(i, 1), ["foo", "FOO", "Foo"])
- outTypes = [outTypes, "fooofed_spectrum"];
- elseif contains(sup(i, 1), ["bg", "BG", "Bg"])
- outTypes = [outTypes, "bg_fit"];
- elseif contains(sup(i, 1), ["pow", "POW", "Pow"])
- outTypes = [outTypes, "power_spectrum"];
- end
- end
- end
-
- for i = 1:length(outTypes)
- if contains(outTypes(i), ["Exp","exp","EXP"])
- outTypes(i) = "exponent";
- elseif contains(outTypes(i), ["off","OFF", "Off"])
- outTypes(i) = "offset";
- elseif contains(outTypes(i), ["peak","PEAK", "Peak"])
- outTypes(i) = "peak_params";
- elseif contains(outTypes(i), ["gau","GAU", "Gau"])
- outTypes(i) = "gaussian_params";
- elseif contains(outTypes(i), ["err","ERR", "Err"])
- outTypes(i) = "error";
- elseif contains(outTypes(i), ["squar","Squar", "SQUAR"])
- outTypes(i) = "r_squared";
- elseif contains(outTypes(i), ["foo","FOO", "Foo"])
- outTypes(i) = "fooofed_spectrum";
- elseif contains(outTypes(i), ["bg","BG", "Bg", "fit", "FIT", ...
- "Fit"])
- outTypes(i) = "bg_fit";
- elseif contains(outTypes(i), ["pow","POW", "Pow"])
- outTypes(i) = "power_spectrum";
- end
- end
-
- % initial setting
- settings = struct();
- settings.max_n_peaks = maxPeaks;
- [time_series, fsOld] = load_data(strcat(inDir, cases(1).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- time_series = time_series(:, tStart:end);
- data = squeeze(time_series(1, 1:dt));
- [~, w] = pwelch(data, [], 0, [], fs);
- sup = [find(w > cf(1), 1), find(w > cf(1), 1)-1];
- [~, y] = min([w(find(w > cf(1), 1))-cf(1), ...
- cf(1)-w(find(w > cf(1), 1)-1)]);
- inferior = sup(y);
- sup = [find(w > cf(end), 1), find(w > cf(end), 1)-1];
- [~,y] = min([w(find(w > cf(end), 1))-cf(end), ...
- cf(end)-w(find(w > cf(end), 1)-1)]);
- superior = sup(y);
- f_range_ind = [inferior superior];
- f_range = [w(inferior) w(superior)];
- clear time_series
-
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- time_series = time_series(:, tStart:end);
- nLoc = size(time_series, 1);
-
- setup_data = zeros(nEpochs, nLoc);
- offset.data = setup_data;
- offset.locations = locations;
- offset.chanlocs = chanlocs;
- exponent = offset;
- error = offset;
- r_squared = offset;
- setup_data = zeros(nEpochs, nLoc, maxPeaks*3);
- peak_params.data = setup_data;
- peak_params.locations = locations;
- gaussian_params = peak_params;
- setup_data = zeros(nEpochs, nLoc, f_range_ind(2)-f_range_ind(1)+1);
- fooofed_spectrum.data = setup_data;
- fooofed_spectrum.locations = locations;
- bg_fit = fooofed_spectrum;
- power_spectrum = bg_fit;
- for k = 1:nEpochs
- for j = 1:nLoc
- data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
- [pxx, w] = pwelch(data, [], 0, [], fs);
- fooof_results = fooof(w', pxx, f_range, settings, 1);
-
- offset.data(k, j) = fooof_results.background_params(1);
- exponent.data(k, j) = ...
- fooof_results.background_params(2);
-
- r_squared.data(k, j) = fooof_results.r_squared;
- error.data(k, j) = fooof_results.error;
- fooofed_spectrum.data(k, j, :) = ...
- fooof_results.fooofed_spectrum;
- bg_fit.data(k, j, :) = fooof_results.bg_fit;
- power_spectrum.data(k, j, :) = ...
- fooof_results.power_spectrum;
-
- sizePeaks = size(fooof_results.peak_params, 1)*3;
- peak_params.data(k, j, 1:sizePeaks) = ...
- squeeze(reshape(...
- fooof_results.peak_params', 1, sizePeaks));
- gaussian_params.data(k, j, 1:sizePeaks) = ...
- squeeze(reshape(...
- fooof_results.gaussian_params', 1, sizePeaks));
- end
- end
-
- for s = 1:length(outTypes)
- outDir = path_check(subdir(inDir, outTypes(s)));
- name = split(cases(i).name, '\');
- if length(name) == 1
- name = split(cases(i).name, '\');
- end
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
- save(fullfile_check(filename), outTypes(s));
- end
- clear time_series
- catch
- end
- waitbar(i/length(cases), f)
- end
- close(f)
+%% FOOOFer
+% This function parameterizes the neural power spectra using the Haller et
+% al. method.
+%
+% FOOOFer(fs, cf, nEpochs, dt, inDir, tStart, outTypes)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch
+% outTypes is the list of variables to save (offset, exponent,
+% peak parameters, gaussian parameters, error, r squared, fooofed
+% spectrum, bg fit, power spectrum), writen in an array as strings
+% in the same order as their output directories in the variable
+% outDirs (es. outDirs=["C:\offset\", "C:\exponent", "C:\bg_fit"],
+% outTypes=["offset", "exponent", "bg fit"])
+%
+% NB: peak and gaussian params will contain a matrix where every group of 3
+% numbers are relative to one peak (CF, Amp, BW) and the others zeros
+% are utilized to export only one matrix for each subject
+
+
+function vargout = FOOOFer(fs, cf, nEpochs, dt, inDir, tStart, outTypes, maxPeaks)
+ switch nargin
+ case 5
+ tStart = 0;
+ outTypes = "";
+ maxPeaks = (cf(end)-cf(1))*2;
+ case 6
+ outTypes = "";
+ maxPeaks = (cf(end)-cf(1))*2;
+ case 7
+ maxPeaks = (cf(end)-cf(1))*2;
+ end
+
+ f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+
+ vargout = -1;
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+
+ askList = 'Insert the background measures separated by a comma';
+
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+ outTypes = string(outTypes);
+
+ if strcmp(outTypes, "")
+ outTypes = [];
+ sup = string(inputdlg(askList));
+ if stcmp(sup, "exit")
+ return
+ end
+ sup = split(sup,",");
+ for i = 1:length(sup)
+ if contains(sup(i, 1), ["Exp", "exp", "EXP"])
+ outTypes = [outTypes, "exp"];
+ elseif contains(sup(i, 1), ["off", "OFF", "Off"])
+ outTypes = [outTypes, "off"];
+ elseif contains(sup(i, 1), ["peak", "PEAK", "Peak"])
+ outTypes = [outTypes, "peak_params"];
+ elseif contains(sup(i, 1), ["gau", "GAU", "Gau"])
+ outTypes = [outTypes, "gaussian_params"];
+ elseif contains(sup(i, 1), ["err", "ERR", "Err"])
+ outTypes = [outTypes, "error"];
+ elseif contains(sup(i, 1), ["squar", "Squar", "SQUAR"])
+ outTypes = [outTypes, "r_squared"];
+ elseif contains(sup(i, 1), ["foo", "FOO", "Foo"])
+ outTypes = [outTypes, "fooofed_spectrum"];
+ elseif contains(sup(i, 1), ["bg", "BG", "Bg"])
+ outTypes = [outTypes, "bg_fit"];
+ elseif contains(sup(i, 1), ["pow", "POW", "Pow"])
+ outTypes = [outTypes, "power_spectrum"];
+ end
+ end
+ end
+
+ for i = 1:length(outTypes)
+ if contains(outTypes(i), ["Exp","exp","EXP"])
+ outTypes(i) = "exponent";
+ elseif contains(outTypes(i), ["off","OFF", "Off"])
+ outTypes(i) = "offset";
+ elseif contains(outTypes(i), ["peak","PEAK", "Peak"])
+ outTypes(i) = "peak_params";
+ elseif contains(outTypes(i), ["gau","GAU", "Gau"])
+ outTypes(i) = "gaussian_params";
+ elseif contains(outTypes(i), ["err","ERR", "Err"])
+ outTypes(i) = "error";
+ elseif contains(outTypes(i), ["squar","Squar", "SQUAR"])
+ outTypes(i) = "r_squared";
+ elseif contains(outTypes(i), ["foo","FOO", "Foo"])
+ outTypes(i) = "fooofed_spectrum";
+ elseif contains(outTypes(i), ["bg","BG", "Bg", "fit", "FIT", ...
+ "Fit"])
+ outTypes(i) = "bg_fit";
+ elseif contains(outTypes(i), ["pow","POW", "Pow"])
+ outTypes(i) = "power_spectrum";
+ end
+ end
+
+ % initial setting
+ settings = struct();
+ settings.max_n_peaks = maxPeaks;
+ [time_series, fsOld] = load_data(strcat(inDir, cases(1).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ time_series = time_series(:, tStart:end);
+ data = squeeze(time_series(1, 1:dt));
+ [~, w] = pwelch(data, [], 0, [], fs);
+ sup = [find(w > cf(1), 1), find(w > cf(1), 1)-1];
+ [~, y] = min([w(find(w > cf(1), 1))-cf(1), ...
+ cf(1)-w(find(w > cf(1), 1)-1)]);
+ inferior = sup(y);
+ sup = [find(w > cf(end), 1), find(w > cf(end), 1)-1];
+ [~,y] = min([w(find(w > cf(end), 1))-cf(end), ...
+ cf(end)-w(find(w > cf(end), 1)-1)]);
+ superior = sup(y);
+ f_range_ind = [inferior superior];
+ f_range = [w(inferior) w(superior)];
+ clear time_series
+
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ time_series = time_series(:, tStart:end);
+ nLoc = size(time_series, 1);
+
+ setup_data = zeros(nEpochs, nLoc);
+ offset.data = setup_data;
+ offset.locations = locations;
+ offset.chanlocs = chanlocs;
+ exponent = offset;
+ error = offset;
+ r_squared = offset;
+ setup_data = zeros(nEpochs, nLoc, maxPeaks*3);
+ peak_params.data = setup_data;
+ peak_params.locations = locations;
+ gaussian_params = peak_params;
+ setup_data = zeros(nEpochs, nLoc, f_range_ind(2)-f_range_ind(1)+1);
+ fooofed_spectrum.data = setup_data;
+ fooofed_spectrum.locations = locations;
+ bg_fit = fooofed_spectrum;
+ power_spectrum = bg_fit;
+ for k = 1:nEpochs
+ for j = 1:nLoc
+ data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
+ [pxx, w] = pwelch(data, [], 0, [], fs);
+ fooof_results = fooof(w', pxx, f_range, settings, 1);
+
+ offset.data(k, j) = fooof_results.background_params(1);
+ exponent.data(k, j) = ...
+ fooof_results.background_params(2);
+
+ r_squared.data(k, j) = fooof_results.r_squared;
+ error.data(k, j) = fooof_results.error;
+ fooofed_spectrum.data(k, j, :) = ...
+ fooof_results.fooofed_spectrum;
+ bg_fit.data(k, j, :) = fooof_results.bg_fit;
+ power_spectrum.data(k, j, :) = ...
+ fooof_results.power_spectrum;
+
+ sizePeaks = size(fooof_results.peak_params, 1)*3;
+ peak_params.data(k, j, 1:sizePeaks) = ...
+ squeeze(reshape(...
+ fooof_results.peak_params', 1, sizePeaks));
+ gaussian_params.data(k, j, 1:sizePeaks) = ...
+ squeeze(reshape(...
+ fooof_results.gaussian_params', 1, sizePeaks));
+ vargout = 0;
+ end
+ end
+
+ for s = 1:length(outTypes)
+ outDir = path_check(subdir(inDir, outTypes(s)));
+ name = split(cases(i).name, '\');
+ if length(name) == 1
+ name = split(cases(i).name, '\');
+ end
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+ save(fullfile_check(filename), outTypes(s));
+ end
+ clear time_series
+ catch
+ end
+ waitbar(i/length(cases), f)
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/PSDr.m b/Measures/PSDr.m
index 225070a..e1c697a 100644
--- a/Measures/PSDr.m
+++ b/Measures/PSDr.m
@@ -1,97 +1,102 @@
-%% PSDr
-% This function computes the Power Spectral Density relative over a total
-% band defined by the user, using the Welch's power formulation.
-%
-% PSDr(fs, cf, nEpochs, dt, inDir, tStart, relBand)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 4 8 13 30 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch (0 as default)
-% relBand is the total band used to obtain the relative band
-% ([min(min(cf),1) max(max(cf),40)] as default, the first value is
-% the minimum between 1 and the first cut frequency and the second
-% value is the maximum between 40 and the last cut frequency)
-
-
-function PSDr(fs, cf, nEpochs, dt, inDir, tStart, relBand)
- switch nargin
- case 5
- tStart = 0;
- relBand = [min(min(cf), 1) max(max(cf), 40)];
- case 6
- relBand = [min(min(cf), 1) max(max(cf), 40)];
- end
-
- f = waitbar(0,'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
-
- cfstart = 0;
- cfstop = length(cf)-1;
- nBands = cfstop-cfstart;
- dt = fs*dt;
- tStart = tStart*fs+1;
- inDir = path_check(inDir);
- if iscell(relBand)
- aux_relBand = [str2double(string(relBand{1})), ...
- str2double(string(relBand{2}))];
- relBand = aux_relBand;
- end
-
- cases = define_cases(inDir);
-
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- time_series = time_series(:, tStart:end);
- psdr.data = zeros(nBands, nEpochs, size(time_series, 1));
- psdr.locations = locations;
- psdr.chanlocs = chanlocs;
- for k = 1:nEpochs
-
- for j = 1:size(time_series, 1)
- data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
- [pxx, w] = pwelch(data, [], [], [], fs);
- bandPower = zeros(nBands, 1);
-
- for b = 1:nBands
- [infft, supft] = band_boundaries(w, ...
- cf(b+cfstart), cf(b+cfstart+1));
-
- bandPower(b, 1) = sum(pxx(infft:supft));
- end
-
- [infft, supft] = band_boundaries(w, relBand(1), ...
- relBand(end));
- totalPower = sum(pxx(infft:supft));
-
- for b = 1:nBands
- psdr.data(b, k, j) = bandPower(b, 1)/totalPower;
- end
- end
- end
-
- outDir = path_check(subdir(inDir, 'PSDr'));
- name = split(cases(i).name, filesep);
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
-
- save(fullfile_check(filename), 'psdr');
- catch
- end %end try
- waitbar(i/length(cases), f)
- end
- close(f)
+%% PSDr
+% This function computes the Power Spectral Density relative over a total
+% band defined by the user, using the Welch's power formulation.
+%
+% PSDr(fs, cf, nEpochs, dt, inDir, tStart, relBand)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 4 8 13 30 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch (0 as default)
+% relBand is the total band used to obtain the relative band
+% ([min(min(cf),1) max(max(cf),40)] as default, the first value is
+% the minimum between 1 and the first cut frequency and the second
+% value is the maximum between 40 and the last cut frequency)
+
+
+function vargout = PSDr(fs, cf, nEpochs, dt, inDir, tStart, relBand)
+ switch nargin
+ case 5
+ tStart = 0;
+ relBand = [min(min(cf), 1) max(max(cf), 40)];
+ case 6
+ relBand = [min(min(cf), 1) max(max(cf), 40)];
+ end
+
+ f = waitbar(0,'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+
+ vargout = -1;
+ cfstart = 0;
+ cfstop = length(cf)-1;
+ nBands = cfstop-cfstart;
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ inDir = path_check(inDir);
+ if iscell(relBand)
+ aux_relBand = [str2double(string(relBand{1})), ...
+ str2double(string(relBand{2}))];
+ relBand = aux_relBand;
+ end
+
+ cases = define_cases(inDir);
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ time_series = time_series(:, tStart:end);
+ psdr.data = zeros(nBands, nEpochs, size(time_series, 1));
+ psdr.locations = locations;
+ psdr.chanlocs = chanlocs;
+
+ for k = 1:nEpochs
+ for j = 1:size(time_series, 1)
+
+ data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
+ [pxx, w] = pwelch(data, [], [], [], fs);
+ bandPower = zeros(nBands, 1);
+
+ for b = 1:nEpochs
+ [infft, supft] = band_boundaries(w, ...
+ cf(b+cfstart), cf(b+cfstart+1));
+
+ bandPower(b, 1) = sum(pxx(infft:supft));
+ vargout = 0;
+ end
+
+ [infft, supft] = band_boundaries(w, relBand(1), ...
+ relBand(end));
+
+ totalPower = sum(pxx(infft:supft));
+
+ for b = 1:nBands
+ psdr.data(b, k, j) = bandPower(b, 1)/totalPower;
+ end
+
+ end
+ end
+
+ outDir = path_check(subdir(inDir, 'PSDr'));
+ name = split(cases(i).name, filesep);
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+
+ save(fullfile_check(filename), 'psdr');
+ catch
+ end
+ %end try
+ waitbar(i/length(cases), f)
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/autocorrelation_measures.m b/Measures/autocorrelation_measures.m
index 7167403..13eaeff 100644
--- a/Measures/autocorrelation_measures.m
+++ b/Measures/autocorrelation_measures.m
@@ -1,142 +1,144 @@
-%% autocorrelation_measures
-% This function computes the autocorrelation evaluation related to EEG
-% channels or ROIs.
-%
-% autocorrelation_measures(fs, cf, nEpochs, dt, inDir, outDirs, tStart, ...
-% outTypes, filter_name)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch
-% outTypes is the list of autocorrelation measures to compute (hurst
-% exponent)
-% filter_name is the name of the filtering function (athena_filter as
-% default)
-
-
-function autocorrelation_measures(fs, cf, nEpochs, dt, inDir, tStart, ...
- outTypes, filter_name)
-
- if nargin < 6
- tStart = 0;
- end
- if nargin < 7
- outTypes = "";
- end
- if nargin < 8
- filter_name = 'athena_filter';
- end
- if nargin < 9
- m = 2;
- end
- if nargin < 10
- r = 0.2;
- end
-
- f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
- askList = 'Insert the autocorrelation measures separated by a comma';
-
- filter_handle = eval(strcat('@', filter_name));
- dt = fs*dt;
- tStart = tStart*fs+1;
- nBands = length(cf)-1;
-
- hurst_names = ["hurst_exponent", "hurst"];
-
- inDir = path_check(inDir);
- cases = define_cases(inDir);
- outTypes = string(outTypes);
-
- ctrl = 0;
- if length(outTypes) == 1
- if strcmp(outTypes,'')
- ctrl = 1;
- end
- end
-
- while ctrl == 1
- outTypes = [];
- sup = string(inputdlg(askList));
- if strcmp(sup, 'exit')
- return
- end
- sup = split(sup, ',');
- for i = 1:length(sup)
- if contains(sup(i, 1), hurst_names)
- outTypes = [outTypes, "Hurst"];
- end
- end
- if length(outDirs) == length(outTypes)
- ctrl = 0;
- end
- end
-
- for i = 1:length(outTypes)
- if contains(outTypes(i), hurst_names)
- outTypes(i) = "Hurst";
- end
- end
-
- [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- check = check_filtering(time_series, dt, tStart, fs, cf, ...
- filter_handle);
- if check
- close(f)
- return;
- end
-
- for c = 1:length(outTypes)
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- nLoc = size(time_series, 1);
- he.data = zeros(nBands, nEpochs, nLoc);
- he.locations = locations;
- he.chanlocs = chanlocs;
- for j = 1:nBands
- for k = 1:nEpochs
- for loc = 1:nLoc
- ti = ((k-1)*dt)+tStart;
- tf = k*dt+tStart-1;
- data = filter_handle(...
- time_series(loc, ti:tf), fs, cf(j), ...
- cf(j+1));
- data = data';
- if strcmpi(outTypes(c), "Hurst")
- he.data(j, k, loc) = ...
- hurst_exponent(data);
- end
- end
- end
- end
- outDir = path_check(subdir(inDir, outTypes(c)));
- name = split(cases(i).name, '\');
- if length(name) == 1
- name = split(cases(i).name, '\');
- end
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
- save(filename, 'he');
- catch
- end %end try
- waitbar((i+(c-1)*length(cases))/...
- (length(cases)*length(outTypes)), f)
- end
- end
- close(f)
+%% autocorrelation_measures
+% This function computes the autocorrelation evaluation related to EEG
+% channels or ROIs.
+%
+% autocorrelation_measures(fs, cf, nEpochs, dt, inDir, outDirs, tStart, ...
+% outTypes, filter_name)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch
+% outTypes is the list of autocorrelation measures to compute (hurst
+% exponent)
+% filter_name is the name of the filtering function (athena_filter as
+% default)
+
+
+function vargout = autocorrelation_measures(fs, cf, nEpochs, dt, inDir, tStart, ...
+ outTypes, filter_name)
+
+ if nargin < 6
+ tStart = 0;
+ end
+ if nargin < 7
+ outTypes = "";
+ end
+ if nargin < 8
+ filter_name = 'athena_filter';
+ end
+ if nargin < 9
+ m = 2;
+ end
+ if nargin < 10
+ r = 0.2;
+ end
+
+ vargout = -1;
+ f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+ askList = 'Insert the autocorrelation measures separated by a comma';
+
+ filter_handle = eval(strcat('@', filter_name));
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ nBands = length(cf)-1;
+
+ hurst_names = ["hurst_exponent", "hurst"];
+
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+ outTypes = string(outTypes);
+
+ ctrl = 0;
+ if length(outTypes) == 1
+ if strcmp(outTypes,'')
+ ctrl = 1;
+ end
+ end
+
+ while ctrl == 1
+ outTypes = [];
+ sup = string(inputdlg(askList));
+ if strcmp(sup, 'exit')
+ return
+ end
+ sup = split(sup, ',');
+ for i = 1:length(sup)
+ if contains(sup(i, 1), hurst_names)
+ outTypes = [outTypes, "Hurst"];
+ end
+ end
+ if length(outDirs) == length(outTypes)
+ ctrl = 0;
+ end
+ end
+
+ for i = 1:length(outTypes)
+ if contains(outTypes(i), hurst_names)
+ outTypes(i) = "Hurst";
+ end
+ end
+
+ [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ check = check_filtering(time_series, dt, tStart, fs, cf, ...
+ filter_handle);
+ if check
+ close(f)
+ return;
+ end
+
+ for c = 1:length(outTypes)
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ nLoc = size(time_series, 1);
+ he.data = zeros(nBands, nEpochs, nLoc);
+ he.locations = locations;
+ he.chanlocs = chanlocs;
+ for j = 1:nBands
+ for k = 1:nEpochs
+ for loc = 1:nLoc
+ ti = ((k-1)*dt)+tStart;
+ tf = k*dt+tStart-1;
+ data = filter_handle(...
+ time_series(loc, ti:tf), fs, cf(j), ...
+ cf(j+1));
+ data = data';
+ if strcmpi(outTypes(c), "Hurst")
+ he.data(j, k, loc) = ...
+ hurst_exponent(data);
+ vargout = 0;
+ end
+ end
+ end
+ end
+ outDir = path_check(subdir(inDir, outTypes(c)));
+ name = split(cases(i).name, '\');
+ if length(name) == 1
+ name = split(cases(i).name, '\');
+ end
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+ save(filename, 'he');
+ catch
+ end %end try
+ waitbar((i+(c-1)*length(cases))/...
+ (length(cases)*length(outTypes)), f)
+ end
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/connectivity.m b/Measures/connectivity.m
index c80d80c..4d90594 100644
--- a/Measures/connectivity.m
+++ b/Measures/connectivity.m
@@ -1,201 +1,211 @@
-%% connectivity
-% This function computes the connectivity between EEG channels or ROIs,
-% using the phase locking value (PLV), the phase lag index (PLI), the
-% magnitude-squared coherence (MSC), the amplitude envelope correlation
-% corrected (also called orthogonalized, AECo), the amplitude envelope
-% correlation not corrected (AEC), the mutual information (MI), or the
-% weighted phase lag index (wPLI).
-%
-% connectivity(fs, cf, nEpochs, dt, inDir, tStart, outTypes, filter_name)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch
-% outTypes is the list of connectivity types to compute (PLI, PLV, etc.)
-% filter_name is the name of the filtering function (athena_filter as
-% default)
-
-function connectivity(fs, cf, nEpochs, dt, inDir, tStart, outTypes, ...
- filter_name)
-
- switch nargin
- case 5
- tStart=0;
- outTypes="";
- filter_name = 'athena_filter';
- case 6
- outTypes="";
- filter_name = 'athena_filter';
- case 7
- filter_name = 'athena_filter';
- end
-
- f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
- askList = 'Insert the connectivity measures separated by a comma';
-
- filter_handle = eval(strcat('@', filter_name));
- dt = fs*dt;
- tStart = tStart*fs+1;
- nBands = length(cf)-1;
-
- PLInames = ["pli", "PLI", "Pli"];
- PLVnames = ["plv", "PLV", "Plv"];
- AECnames = ["aec", "AEC", "Aec"];
- AECOnames = ["aeco", "aec_o", "AECo", "AEC_o", "Aeco", "Aec_o", ...
- "AECc", "AEC_c", "aecc", "aec_c", "Aecc", "Aec_c"];
- MSCnames = ["coherence", "MSC", "coh", "msc", "COH", "Coherence"];
- ICOHnames = ["ICOH", "Coherency", "coherency"];
- MInames = ["mutual_information", "MI", "Mutual_Information"];
- CCnames = ["correlation", "correlation_coefficient", "CC"];
- wPLInames = ["wPLI", "w_PLI"];
-
- inDir = path_check(inDir);
- cases = define_cases(inDir);
- outTypes = string(outTypes);
-
- ctrl = 0;
- if length(outTypes) == 1
- if strcmp(outTypes,'')
- ctrl = 1;
- end
- end
-
- while ctrl == 1
- outTypes = [];
- sup = string(inputdlg(askList));
- if strcmp(sup, 'exit')
- return
- end
- sup = split(sup, ',');
- for i = 1:length(sup)
- if contains(sup(i, 1), wPLInames)
- outTypes = [outTypes, "wPLI"];
- elseif contains(sup(i, 1), PLVnames)
- outTypes = [outTypes, "PLV"];
- elseif contains(sup(i, 1), AECOnames)
- outTypes = [outTypes, "AECo"];
- elseif contains(sup(i, 1), AECnames)
- outTypes = [outTypes, "AEC"];
- elseif contains(sup(i, 1), MSCnames)
- outTypes = [outTypes, "coherence"];
- elseif contains(sup(i, 1), ICOHnames)
- outTypes = [outTypes, "ICOH"];
- elseif contains(sup(i, 1), MInames)
- outTypes = [outTypes, "mutual_information"];
- elseif contains(sup(i, 1), CCnames)
- outTypes = [outTypes, "correlation_coefficient"];
- elseif contains(sup(i, 1), PLInames)
- outTypes = [outTypes, "PLI"];
- end
- end
- if length(outDirs) == length(outTypes)
- ctrl = 0;
- end
- end
-
- for i = 1:length(outTypes)
- if contains(outTypes(i), wPLInames)
- outTypes(i) = "wPLI";
- elseif contains(outTypes(i), PLVnames)
- outTypes(i) = "PLV";
- elseif contains(outTypes(i), AECOnames)
- outTypes(i) = "AECo";
- elseif contains(outTypes(i), AECnames)
- outTypes(i) = "AEC";
- elseif contains(outTypes(i), ICOHnames)
- outTypes(i) = "ICOH";
- elseif contains(outTypes(i), MSCnames)
- outTypes(i) = "coherence";
- elseif contains(outTypes(i), MInames)
- outTypes(i) = "mutual_information";
- elseif contains(outTypes(i), CCnames)
- outTypes(i) = "correlation_coefficient";
- elseif contains(outTypes(i), PLInames)
- outTypes(i) = "PLI";
- end
- end
-
- [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- check = check_filtering(time_series, dt, tStart, fs, cf, ...
- filter_handle);
- if check
- close(f)
- return;
- end
-
- for c = 1:length(outTypes)
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- nLoc = size(time_series, 1);
- conn.data = zeros(nBands, nEpochs, nLoc, nLoc);
- conn.locations = locations;
- conn.chanlocs = chanlocs;
- for j = 1:nBands
- for k = 1:nEpochs
- ti = ((k-1)*dt)+tStart;
- tf = k*dt+tStart-1;
- data = filter_handle(time_series(:, ti:tf), fs, ...
- cf(j), cf(j+1));
- data = data';
- if strcmpi(outTypes(c), "PLI")
- conn.data(j, k, :, :) = phase_lag_index(data);
- elseif strcmpi(outTypes(c), "PLV")
- conn.data(j, k, :, :) = ...
- phase_locking_value(data);
- elseif strcmpi(outTypes(c), "AECo")
- conn.data(j, k, :, :) = ...
- amplitude_envelope_correlation_orth(data);
- elseif strcmpi(outTypes(c), "AEC")
- conn.data(j, k, :, :) = ...
- amplitude_envelope_correlation(data);
- elseif strcmpi(outTypes(c), "coherence")
- conn.data(j, k, :, :) = ...
- magnitude_squared_coherence(data);
- elseif strcmpi(outTypes(c), "ICOH")
- conn.data(j, k, :, :) = ...
- imaginary_coherency(data);
- elseif strcmpi(outTypes(c), "mutual_information")
- conn.data(j, k, :, :) = ...
- mutual_information(data, 'max');
- elseif strcmpi(outTypes(c), ...
- "correlation_coefficient")
- conn.data(j, k, :, :) = ...
- correlation_coefficient(data);
- elseif strcmpi(outTypes(c), "wPLI")
- conn.data(j, k, :, :) = ...
- weighted_phase_lag_index(data);
- end
- end
- end
- outDir = path_check(subdir(inDir, outTypes(c)));
- name = split(cases(i).name, '\');
- if length(name) == 1
- name = split(cases(i).name, '\');
- end
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
- save(fullfile_check(filename), 'conn');
- catch
- end %end try
- waitbar((i+(c-1)*length(cases))/...
- (length(cases)*length(outTypes)), f)
- end
- end
- close(f)
+%% connectivity
+% This function computes the connectivity between EEG channels or ROIs,
+% using the phase locking value (PLV), the phase lag index (PLI), the
+% magnitude-squared coherence (MSC), the amplitude envelope correlation
+% corrected (also called orthogonalized, AECo), the amplitude envelope
+% correlation not corrected (AEC), the mutual information (MI), or the
+% weighted phase lag index (wPLI).
+%
+% connectivity(fs, cf, nEpochs, dt, inDir, tStart, outTypes, filter_name)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch
+% outTypes is the list of connectivity types to compute (PLI, PLV, etc.)
+% filter_name is the name of the filtering function (athena_filter as
+% default)
+
+function vargout = connectivity(fs, cf, nEpochs, dt, inDir, tStart, outTypes, ...
+ filter_name)
+
+ switch nargin
+ case 5
+ tStart=0;
+ outTypes="";
+ filter_name = 'athena_filter';
+ case 6
+ outTypes="";
+ filter_name = 'athena_filter';
+ case 7
+ filter_name = 'athena_filter';
+ end
+
+ vargout = -1;
+ f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+ askList = 'Insert the connectivity measures separated by a comma';
+
+ filter_handle = eval(strcat('@', filter_name));
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ nBands = length(cf)-1;
+
+ PLInames = ["pli", "PLI", "Pli"];
+ PLVnames = ["plv", "PLV", "Plv"];
+ AECnames = ["aec", "AEC", "Aec"];
+ AECOnames = ["aeco", "aec_o", "AECo", "AEC_o", "Aeco", "Aec_o", ...
+ "AECc", "AEC_c", "aecc", "aec_c", "Aecc", "Aec_c"];
+ MSCnames = ["coherence", "MSC", "coh", "msc", "COH", "Coherence"];
+ ICOHnames = ["ICOH", "Coherency", "coherency"];
+ MInames = ["mutual_information", "MI", "Mutual_Information"];
+ CCnames = ["correlation", "correlation_coefficient", "CC"];
+ wPLInames = ["wPLI", "w_PLI"];
+
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+ outTypes = string(outTypes);
+
+ ctrl = 0;
+ if length(outTypes) == 1
+ if strcmp(outTypes,'')
+ ctrl = 1;
+ end
+ end
+
+ while ctrl == 1
+ outTypes = [];
+ sup = string(inputdlg(askList));
+ if strcmp(sup, 'exit')
+ return
+ end
+ sup = split(sup, ',');
+ for i = 1:length(sup)
+ if contains(sup(i, 1), wPLInames)
+ outTypes = [outTypes, "wPLI"];
+ elseif contains(sup(i, 1), PLVnames)
+ outTypes = [outTypes, "PLV"];
+ elseif contains(sup(i, 1), AECOnames)
+ outTypes = [outTypes, "AECo"];
+ elseif contains(sup(i, 1), AECnames)
+ outTypes = [outTypes, "AEC"];
+ elseif contains(sup(i, 1), MSCnames)
+ outTypes = [outTypes, "coherence"];
+ elseif contains(sup(i, 1), ICOHnames)
+ outTypes = [outTypes, "ICOH"];
+ elseif contains(sup(i, 1), MInames)
+ outTypes = [outTypes, "mutual_information"];
+ elseif contains(sup(i, 1), CCnames)
+ outTypes = [outTypes, "correlation_coefficient"];
+ elseif contains(sup(i, 1), PLInames)
+ outTypes = [outTypes, "PLI"];
+ end
+ end
+ if length(outDirs) == length(outTypes)
+ ctrl = 0;
+ end
+ end
+
+ for i = 1:length(outTypes)
+ if contains(outTypes(i), wPLInames)
+ outTypes(i) = "wPLI";
+ elseif contains(outTypes(i), PLVnames)
+ outTypes(i) = "PLV";
+ elseif contains(outTypes(i), AECOnames)
+ outTypes(i) = "AECo";
+ elseif contains(outTypes(i), AECnames)
+ outTypes(i) = "AEC";
+ elseif contains(outTypes(i), ICOHnames)
+ outTypes(i) = "ICOH";
+ elseif contains(outTypes(i), MSCnames)
+ outTypes(i) = "coherence";
+ elseif contains(outTypes(i), MInames)
+ outTypes(i) = "mutual_information";
+ elseif contains(outTypes(i), CCnames)
+ outTypes(i) = "correlation_coefficient";
+ elseif contains(outTypes(i), PLInames)
+ outTypes(i) = "PLI";
+ end
+ end
+
+ [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ check = check_filtering(time_series, dt, tStart, fs, cf, ...
+ filter_handle);
+ if check
+ close(f)
+ return;
+ end
+
+ for c = 1:length(outTypes)
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ nLoc = size(time_series, 1);
+ conn.data = zeros(nBands, nEpochs, nLoc, nLoc);
+ conn.locations = locations;
+ conn.chanlocs = chanlocs;
+ for j = 1:nBands
+ for k = 1:nEpochs
+ ti = ((k-1)*dt)+tStart;
+ tf = k*dt+tStart-1;
+ data = filter_handle(time_series(:, ti:tf), fs, ...
+ cf(j), cf(j+1));
+ data = data';
+ if strcmpi(outTypes(c), "PLI")
+ conn.data(j, k, :, :) = phase_lag_index(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "PLV")
+ conn.data(j, k, :, :) = ...
+ phase_locking_value(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "AECo")
+ conn.data(j, k, :, :) = ...
+ amplitude_envelope_correlation_orth(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "AEC")
+ conn.data(j, k, :, :) = ...
+ amplitude_envelope_correlation(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "coherence")
+ conn.data(j, k, :, :) = ...
+ magnitude_squared_coherence(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "ICOH")
+ conn.data(j, k, :, :) = ...
+ imaginary_coherency(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "mutual_information")
+ conn.data(j, k, :, :) = ...
+ mutual_information(data, 'max');
+ vargout = 0;
+ elseif strcmpi(outTypes(c), ...
+ "correlation_coefficient")
+ conn.data(j, k, :, :) = ...
+ correlation_coefficient(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "wPLI")
+ conn.data(j, k, :, :) = ...
+ weighted_phase_lag_index(data);
+ vargout = 0;
+ end
+ end
+ end
+ outDir = path_check(subdir(inDir, outTypes(c)));
+ name = split(cases(i).name, '\');
+ if length(name) == 1
+ name = split(cases(i).name, '\');
+ end
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+ save(fullfile_check(filename), 'conn');
+ catch
+ end %end try
+ waitbar((i+(c-1)*length(cases))/...
+ (length(cases)*length(outTypes)), f)
+ end
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/spectral_entropy.m b/Measures/spectral_entropy.m
index f79b498..63fafbe 100644
--- a/Measures/spectral_entropy.m
+++ b/Measures/spectral_entropy.m
@@ -1,74 +1,75 @@
-%% spectral_entropy
-% This function computes the Spectral Entropy, on the single frequency
-% bands and on the single epochs.
-%
-% spectral_entropy(fs, cf, nEpochs, dt, inDir, outDir, tStart)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 4 8 13 30 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch (0 as default)
-
-
-function spectral_entropy(fs, cf, nEpochs, dt, inDir, tStart)
- switch nargin
- case 5
- tStart = 0;
- end
-
- f = waitbar(0,'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
-
- cfstart = 0;
- cfstop = length(cf)-1;
- nBands = cfstop-cfstart;
- dt = fs*dt;
- tStart = tStart*fs+1;
- inDir = path_check(inDir);
-
- cases = define_cases(inDir);
-
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- time_series = time_series(:, tStart:end);
- se.data = zeros(nBands, nEpochs, size(time_series, 1));
- se.locations = locations;
- se.chanlocs = chanlocs;
- for k = 1:nEpochs
-
- for j = 1:size(time_series, 1)
- data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
- for b = 1:nBands
- [p, fp, tp] = pspectrum(data, fs, 'spectrogram');
- se.data(b, k, j) = pentropy(p, fp, tp, ...
- 'Instantaneous', false, ...
- 'FrequencyLimits', [cf(b), cf(b+1)]);
- end
- end
- end
-
- outDir = path_check(subdir(inDir, 'PEntropy'));
- name = split(cases(i).name, filesep);
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
-
- save(fullfile_check(filename), 'se');
- catch
- end
- waitbar(i/length(cases), f)
- end
- close(f)
+%%
+% This function computes the Spectral Entropy, on the single frequency
+% bands and on the single epochs.
+%
+% spectral_entropy(fs, cf, nEpochs, dt, inDir, outDir, tStart)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 4 8 13 30 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch (0 as default)
+
+
+function vargout = spectral_entropy(fs, cf, nEpochs, dt, inDir, tStart)
+ switch nargin
+ case 5
+ tStart = 0;
+ end
+
+ f = waitbar(0,'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+
+ vargout = -1;
+ cfstart = 0;
+ cfstop = length(cf)-1;
+ nBands = cfstop-cfstart;
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ time_series = time_series(:, tStart:end);
+ se.data = zeros(nBands, nEpochs, size(time_series, 1));
+ se.locations = locations;
+ se.chanlocs = chanlocs;
+ for k = 1:nEpochs
+
+ for j = 1:size(time_series, 1)
+ data = squeeze(time_series(j, dt*(k-1)+1:k*dt));
+ for b = 1:nBands
+ [p, fp, tp] = pspectrum(data, fs, ' ');
+ se.data(b, k, j) = pentropy(p, fp, tp, ...
+ 'Instantaneous', false, ...
+ 'FrequencyLimits', [cf(b), cf(b+1)]);
+ vargout = 0;
+ end
+ end
+ end
+
+ outDir = path_check(subdir(inDir, 'PEntropy'));
+ name = split(cases(i).name, filesep);
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+
+ save(fullfile_check(filename), 'se');
+ catch
+ end
+ waitbar(i/length(cases), f)
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/statistical_information.m b/Measures/statistical_information.m
index 372b122..b8a51d8 100644
--- a/Measures/statistical_information.m
+++ b/Measures/statistical_information.m
@@ -1,176 +1,183 @@
-%% statistical_information
-% This function computes statistical measures on EEG channels or ROIs.
-%
-% statistical_information(fs, cf, nEpochs, dt, inDir, outDirs, tStart, ...
-% outTypes, filter_name)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch
-% outTypes is the list of statistical measures to compute (median, mean,
-% std, variance, skewness, kurtosis)
-% filter_name is the name of the filtering function (athena_filter as
-% default)
-
-
-function statistical_information(fs, cf, nEpochs, dt, inDir, tStart, ...
- outTypes, filter_name)
-
- if nargin < 6
- tStart = 0;
- end
- if nargin < 7
- outTypes = "";
- end
- if nargin < 8
- filter_name = 'athena_filter';
- end
- if nargin < 9
- m = 2;
- end
- if nargin < 10
- r = 0.2;
- end
-
- f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
- askList = 'Insert the statistical measures separated by a comma';
-
- filter_handle = eval(strcat('@', filter_name));
- dt = fs*dt;
- tStart = tStart*fs+1;
- nBands = length(cf)-1;
-
- variance_names = ["variance", "var"];
- mean_names = ["mean"];
- median_names = ["median"];
- std_names = ["std", "standard_deviation"];
- kurtosis_names = ["kurtosis"];
- skewness_names = ["skewness"];
-
- inDir = path_check(inDir);
- cases = define_cases(inDir);
- outTypes = string(outTypes);
-
- ctrl = 0;
- if length(outTypes) == 1
- if strcmp(outTypes,'')
- ctrl = 1;
- end
- end
-
- while ctrl == 1
- outTypes = [];
- sup = string(inputdlg(askList));
- if strcmp(sup, 'exit')
- return
- end
- sup = split(sup, ',');
- for i = 1:length(sup)
- if contains(sup(i, 1), variance_names)
- outTypes = [outTypes, "variance"];
- elseif contains(sup(i, 1), median_names)
- outTypes = [outTypes, "median"];
- elseif contains(sup(i, 1), mean_names)
- outTypes = [outTypes, "mean"];
- elseif contains(sup(i, 1), kurtosis_names)
- outTypes = [outTypes, "kurtosis"];
- elseif contains(sup(i, 1), skewness_names)
- outTypes = [outTypes, "skewness"];
- elseif contains(sup(i, 1), std_names)
- outTypes = [outTypes, "Standard_deviation"];
- end
- end
- if length(outDirs) == length(outTypes)
- ctrl = 0;
- end
- end
-
- for i = 1:length(outTypes)
- if contains(outTypes(i), variance_names)
- outTypes(i) = "Variance";
- elseif contains(outTypes(i), median_names)
- outTypes(i) = "Median";
- elseif contains(outTypes(i), mean_names)
- outTypes(i) = "Mean";
- elseif contains(outTypes(i), kurtosis_names)
- outTypes(i) = "Kurtosis";
- elseif contains(outTypes(i), skewness_names)
- outTypes(i) = "Skewness";
- elseif contains(outTypes(i), std_names)
- outTypes(i) = "Standard_deviation";
- end
- end
-
- [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- check = check_filtering(time_series, dt, tStart, fs, cf, ...
- filter_handle);
- if check
- close(f)
- return;
- end
-
- for c = 1:length(outTypes)
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- nLoc = size(time_series, 1);
- sa.data = zeros(nBands, nEpochs, nLoc);
- sa.locations = locations;
- sa.chanlocs = chanlocs;
- for j = 1:nBands
- for k = 1:nEpochs
- for loc = 1:nLoc
- ti = ((k-1)*dt)+tStart;
- tf = k*dt+tStart-1;
- data = filter_handle(...
- time_series(loc, ti:tf), fs, cf(j), ...
- cf(j+1));
- data = data';
- if strcmpi(outTypes(c), "Variance")
- sa.data(j, k, loc) = var(data);
- elseif strcmpi(outTypes(c), ...
- "Standard_deviation")
- sa.data(j, k, loc) = std(data);
- elseif strcmpi(outTypes(c), "Mean")
- sa.data(j, k, loc) = mean(data);
- elseif strcmpi(outTypes(c), "Median")
- sa.data(j, k, loc) = median(data);
- elseif strcmpi(outTypes(c), "Kurtosis")
- sa.data(j, k, loc) = kurtosis(data);
- elseif strcmpi(outTypes(c), "Skewness")
- sa.data(j, k, loc) = skewness(data);
- end
- end
- end
- end
- outDir = path_check(subdir(inDir, outTypes(c)));
- name = split(cases(i).name, '\');
- if length(name) == 1
- name = split(cases(i).name, '\');
- end
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
- save(fullfile_check(filename), 'sa');
- catch
- end %end try
- waitbar((i+(c-1)*length(cases))/...
- (length(cases)*length(outTypes)), f)
- end
- end
- close(f)
+%% statistical_information
+% This function computes statistical measures on EEG channels or ROIs.
+%
+% statistical_information(fs, cf, nEpochs, dt, inDir, outDirs, tStart, ...
+% outTypes, filter_name)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch
+% outTypes is the list of statistical measures to compute (median, mean,
+% std, variance, skewness, kurtosis)
+% filter_name is the name of the filtering function (athena_filter as
+% default)
+
+
+function vargout = statistical_information(fs, cf, nEpochs, dt, inDir, tStart, ...
+ outTypes, filter_name)
+
+ if nargin < 6
+ tStart = 0;
+ end
+ if nargin < 7
+ outTypes = "";
+ end
+ if nargin < 8
+ filter_name = 'athena_filter';
+ end
+ if nargin < 9
+ m = 2;
+ end
+ if nargin < 10
+ r = 0.2;
+ end
+
+ f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+ askList = 'Insert the statistical measures separated by a comma';
+
+ vargout = -1;
+ filter_handle = eval(strcat('@', filter_name));
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ nBands = length(cf)-1;
+
+ variance_names = ["variance", "var"];
+ mean_names = ["mean"];
+ median_names = ["median"];
+ std_names = ["std", "standard_deviation"];
+ kurtosis_names = ["kurtosis"];
+ skewness_names = ["skewness"];
+
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+ outTypes = string(outTypes);
+
+ ctrl = 0;
+ if length(outTypes) == 1
+ if strcmp(outTypes,'')
+ ctrl = 1;
+ end
+ end
+
+ while ctrl == 1
+ outTypes = [];
+ sup = string(inputdlg(askList));
+ if strcmp(sup, 'exit')
+ return
+ end
+ sup = split(sup, ',');
+ for i = 1:length(sup)
+ if contains(sup(i, 1), variance_names)
+ outTypes = [outTypes, "variance"];
+ elseif contains(sup(i, 1), median_names)
+ outTypes = [outTypes, "median"];
+ elseif contains(sup(i, 1), mean_names)
+ outTypes = [outTypes, "mean"];
+ elseif contains(sup(i, 1), kurtosis_names)
+ outTypes = [outTypes, "kurtosis"];
+ elseif contains(sup(i, 1), skewness_names)
+ outTypes = [outTypes, "skewness"];
+ elseif contains(sup(i, 1), std_names)
+ outTypes = [outTypes, "Standard_deviation"];
+ end
+ end
+ if length(outDirs) == length(outTypes)
+ ctrl = 0;
+ end
+ end
+
+ for i = 1:length(outTypes)
+ if contains(outTypes(i), variance_names)
+ outTypes(i) = "Variance";
+ elseif contains(outTypes(i), median_names)
+ outTypes(i) = "Median";
+ elseif contains(outTypes(i), mean_names)
+ outTypes(i) = "Mean";
+ elseif contains(outTypes(i), kurtosis_names)
+ outTypes(i) = "Kurtosis";
+ elseif contains(outTypes(i), skewness_names)
+ outTypes(i) = "Skewness";
+ elseif contains(outTypes(i), std_names)
+ outTypes(i) = "Standard_deviation";
+ end
+ end
+
+ [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ check = check_filtering(time_series, dt, tStart, fs, cf, ...
+ filter_handle);
+ if check
+ close(f)
+ return;
+ end
+
+ for c = 1:length(outTypes)
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ nLoc = size(time_series, 1);
+ sa.data = zeros(nBands, nEpochs, nLoc);
+ sa.locations = locations;
+ sa.chanlocs = chanlocs;
+ for j = 1:nBands
+ for k = 1:nEpochs
+ for loc = 1:nLoc
+ ti = ((k-1)*dt)+tStart;
+ tf = k*dt+tStart-1;
+ data = filter_handle(...
+ time_series(loc, ti:tf), fs, cf(j), ...
+ cf(j+1));
+ data = data';
+ if strcmpi(outTypes(c), "Variance")
+ sa.data(j, k, loc) = var(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), ...
+ "Standard_deviation")
+ sa.data(j, k, loc) = std(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "Mean")
+ sa.data(j, k, loc) = mean(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "Median")
+ sa.data(j, k, loc) = median(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "Kurtosis")
+ sa.data(j, k, loc) = kurtosis(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), "Skewness")
+ sa.data(j, k, loc) = skewness(data);
+ vargout = 0;
+ end
+ end
+ end
+ end
+ outDir = path_check(subdir(inDir, outTypes(c)));
+ name = split(cases(i).name, '\');
+ if length(name) == 1
+ name = split(cases(i).name, '\');
+ end
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+ save(fullfile_check(filename), 'sa');
+ catch
+ end %end try
+ waitbar((i+(c-1)*length(cases))/...
+ (length(cases)*length(outTypes)), f)
+ end
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/Measures/time_entropy.m b/Measures/time_entropy.m
index a15a045..8c09791 100644
--- a/Measures/time_entropy.m
+++ b/Measures/time_entropy.m
@@ -1,163 +1,167 @@
-%% time_entropy
-% This function computes the entropy related to EEG channels or ROIs.
-%
-% time_entropy(fs, cf, nEpochs, dt, inDir, outDirs, tStart, outTypes, ...
-% filter_name, m, r)
-%
-% input:
-% fs is the sampling frequency
-% cf is an array containing the cut frequencies (es, [1 40])
-% nEpochs contains the number of epochs to compute
-% dt contains the time (in seconds) of each epoch
-% inDir is the directory containing each case
-% tStart is the starting time (in seconds) to computate the first sample
-% of the first epoch
-% outTypes is the list of entropy types to compute (sample_entropy,
-% approximate entropy or discretized entropy)
-% filter_name is the name of the filtering function (athena_filter as
-% default)
-% m is the embedding dimension (its maximum value is the number of
-% samples of the time series minus 1, 2 by default)
-% r is the fraction of standard deviation of the time series, which have
-% to be used as tolerance (0.2 by default)
-
-
-function time_entropy(fs, cf, nEpochs, dt, inDir, tStart, outTypes, ...
- filter_name, m, r)
-
- if nargin < 6
- tStart = 0;
- end
- if nargin < 7
- outTypes = "";
- end
- if nargin < 8
- filter_name = 'athena_filter';
- end
- if nargin < 9
- m = 2;
- end
- if nargin < 10
- r = 0.2;
- end
-
- f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
- fchild = allchild(f);
- fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
- fchild(1).JavaPeer.setStringPainted(true)
- askList = 'Insert the entropy measures separated by a comma';
-
- filter_handle = eval(strcat('@', filter_name));
- dt = fs*dt;
- tStart = tStart*fs+1;
- nBands = length(cf)-1;
-
- SampEn_names = ["sample_entropy", "SampEn", "SaEn"];
- AppEn_names = ["approximate_entropy", "AppEn", "ApEn"];
- DiscEn_names = ["discretized_entropy", "DiscEn", "DE"];
-
- inDir = path_check(inDir);
- cases = define_cases(inDir);
- outTypes = string(outTypes);
-
- ctrl = 0;
- if length(outTypes) == 1
- if strcmp(outTypes,'')
- ctrl = 1;
- end
- end
-
- while ctrl == 1
- outTypes = [];
- sup = string(inputdlg(askList));
- if strcmp(sup, 'exit')
- return
- end
- sup = split(sup, ',');
- for i = 1:length(sup)
- if contains(sup(i, 1), SampEn_names)
- outTypes = [outTypes, "sample_entropy"];
- elseif contains(sup(i, 1), DiscEn_names)
- outTypes = [outTypes, "discretized_entropy"];
- elseif contains(sup(i, 1), AppEn_names)
- outTypes = [outTypes, "approximate_entropy"];
- end
- end
- if length(outDirs) == length(outTypes)
- ctrl = 0;
- end
- end
-
- for i = 1:length(outTypes)
- if contains(outTypes(i), SampEn_names)
- outTypes(i) = "sample_entropy";
- elseif contains(outTypes(i), DiscEn_names)
- outTypes(i) = "discretized_entropy";
- elseif contains(outTypes(i), AppEn_names)
- outTypes(i) = "approximate_entropy";
- end
- end
-
- [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- check = check_filtering(time_series, dt, tStart, fs, cf, ...
- filter_handle);
- if check
- close(f)
- return;
- end
-
- for c = 1:length(outTypes)
- for i = 1:length(cases)
- try
- [time_series, fsOld, locations, chanlocs] = ...
- load_data(strcat(inDir, cases(i).name), 1);
- time_series = resample_signal(time_series, fs, fsOld);
- nLoc = size(time_series, 1);
- en.data = zeros(nBands, nEpochs, nLoc);
- en.locations = locations;
- en.chanlocs = chanlocs;
- for j = 1:nBands
- for k = 1:nEpochs
- for loc = 1:nLoc
- ti = ((k-1)*dt)+tStart;
- tf = k*dt+tStart-1;
- data = filter_handle(...
- time_series(loc, ti:tf), fs, cf(j), ...
- cf(j+1));
- data = data';
- if strcmpi(outTypes(c), "sample_entropy")
- en.data(j, k, loc) = ...
- sample_entropy(data, m, r);
- elseif strcmpi(outTypes(c), ...
- "discretized_entropy")
- en.data(j, k, loc) = ...
- discretized_entropy(data);
- elseif strcmpi(outTypes(c), ...
- "approximate_entropy")
- en.data(j, k, loc) = ...
- approximate_entropy(data, m, r);
- end
- end
- end
- end
- outDir = path_check(subdir(inDir, outTypes(c)));
- name = split(cases(i).name, '\');
- if length(name) == 1
- name = split(cases(i).name, '\');
- end
- if length(name) > 1
- name = name{2};
- else
- name = cases(i).name;
- end
- filename = strcat(outDir, strtok(name, '.'), '.mat');
- save(fullfile_check(filename), 'en');
- catch
- end %end try
- waitbar((i+(c-1)*length(cases))/...
- (length(cases)*length(outTypes)), f)
- end
- end
- close(f)
+%% time_entropy
+% This function computes the entropy related to EEG channels or ROIs.
+%
+% time_entropy(fs, cf, nEpochs, dt, inDir, outDirs, tStart, outTypes, ...
+% filter_name, m, r)
+%
+% input:
+% fs is the sampling frequency
+% cf is an array containing the cut frequencies (es, [1 40])
+% nEpochs contains the number of epochs to compute
+% dt contains the time (in seconds) of each epoch
+% inDir is the directory containing each case
+% tStart is the starting time (in seconds) to computate the first sample
+% of the first epoch
+% outTypes is the list of entropy types to compute (sample_entropy,
+% approximate entropy or discretized entropy)
+% filter_name is the name of the filtering function (athena_filter as
+% default)
+% m is the embedding dimension (its maximum value is the number of
+% samples of the time series minus 1, 2 by default)
+% r is the fraction of standard deviation of the time series, which have
+% to be used as tolerance (0.2 by default)
+
+
+function vargout = time_entropy(fs, cf, nEpochs, dt, inDir, tStart, outTypes, ...
+ filter_name, m, r)
+
+ if nargin < 6
+ tStart = 0;
+ end
+ if nargin < 7
+ outTypes = "";
+ end
+ if nargin < 8
+ filter_name = 'athena_filter';
+ end
+ if nargin < 9
+ m = 2;
+ end
+ if nargin < 10
+ r = 0.2;
+ end
+
+ f = waitbar(0, 'Processing your data', 'Color', '[1 1 1]');
+ fchild = allchild(f);
+ fchild(1).JavaPeer.setForeground(fchild(1).JavaPeer.getBackground.BLUE)
+ fchild(1).JavaPeer.setStringPainted(true)
+ askList = 'Insert the entropy measures separated by a comma';
+
+ vargout = -1;
+ filter_handle = eval(strcat('@', filter_name));
+ dt = fs*dt;
+ tStart = tStart*fs+1;
+ nBands = length(cf)-1;
+
+ SampEn_names = ["sample_entropy", "SampEn", "SaEn"];
+ AppEn_names = ["approximate_entropy", "AppEn", "ApEn"];
+ DiscEn_names = ["discretized_entropy", "DiscEn", "DE"];
+
+ inDir = path_check(inDir);
+ cases = define_cases(inDir);
+ outTypes = string(outTypes);
+
+ ctrl = 0;
+ if length(outTypes) == 1
+ if strcmp(outTypes,'')
+ ctrl = 1;
+ end
+ end
+
+ while ctrl == 1
+ outTypes = [];
+ sup = string(inputdlg(askList));
+ if strcmp(sup, 'exit')
+ return
+ end
+ sup = split(sup, ',');
+ for i = 1:length(sup)
+ if contains(sup(i, 1), SampEn_names)
+ outTypes = [outTypes, "sample_entropy"];
+ elseif contains(sup(i, 1), DiscEn_names)
+ outTypes = [outTypes, "discretized_entropy"];
+ elseif contains(sup(i, 1), AppEn_names)
+ outTypes = [outTypes, "approximate_entropy"];
+ end
+ end
+ if length(outDirs) == length(outTypes)
+ ctrl = 0;
+ end
+ end
+
+ for i = 1:length(outTypes)
+ if contains(outTypes(i), SampEn_names)
+ outTypes(i) = "sample_entropy";
+ elseif contains(outTypes(i), DiscEn_names)
+ outTypes(i) = "discretized_entropy";
+ elseif contains(outTypes(i), AppEn_names)
+ outTypes(i) = "approximate_entropy";
+ end
+ end
+
+ [time_series, fsOld] = load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ check = check_filtering(time_series, dt, tStart, fs, cf, ...
+ filter_handle);
+ if check
+ close(f)
+ return;
+ end
+
+ for c = 1:length(outTypes)
+ for i = 1:length(cases)
+ try
+ [time_series, fsOld, locations, chanlocs] = ...
+ load_data(strcat(inDir, cases(i).name), 1);
+ time_series = resample_signal(time_series, fs, fsOld);
+ nLoc = size(time_series, 1);
+ en.data = zeros(nBands, nEpochs, nLoc);
+ en.locations = locations;
+ en.chanlocs = chanlocs;
+ for j = 1:nBands
+ for k = 1:nEpochs
+ for loc = 1:nLoc
+ ti = ((k-1)*dt)+tStart;
+ tf = k*dt+tStart-1;
+ data = filter_handle(...
+ time_series(loc, ti:tf), fs, cf(j), ...
+ cf(j+1));
+ data = data';
+ if strcmpi(outTypes(c), "sample_entropy")
+ en.data(j, k, loc) = ...
+ sample_entropy(data, m, r);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), ...
+ "discretized_entropy")
+ en.data(j, k, loc) = ...
+ discretized_entropy(data);
+ vargout = 0;
+ elseif strcmpi(outTypes(c), ...
+ "approximate_entropy")
+ en.data(j, k, loc) = ...
+ approximate_entropy(data, m, r);
+ vargout = 0;
+ end
+ end
+ end
+ end
+ outDir = path_check(subdir(inDir, outTypes(c)));
+ name = split(cases(i).name, '\');
+ if length(name) == 1
+ name = split(cases(i).name, '\');
+ end
+ if length(name) > 1
+ name = name{2};
+ else
+ name = cases(i).name;
+ end
+ filename = strcat(outDir, strtok(name, '.'), '.mat');
+ save(fullfile_check(filename), 'en');
+ catch
+ end %end try
+ waitbar((i+(c-1)*length(cases))/...
+ (length(cases)*length(outTypes)), f)
+ end
+ end
+ close(f)
end
\ No newline at end of file
diff --git a/README.md b/README.md
index 1140df9..9ae4012 100644
--- a/README.md
+++ b/README.md
@@ -1,147 +1,153 @@
-# ATHENA
-
- 
-
-
-***ATHENA*** *(Automatic Toolbox for Handling Experimental Neural Analysis)* is a toolbox which allow to
-automatically extract commonly analyzed measures, used to study neural time series, such as EEG and MEG.
-These measures, which can be connectivity, power or background measures, can be studied through this toolbox, through correlation and
-statistical analysis.
-
-The *pipeline* followed by this toolbox is easy and repetible.
-It is possible to choose between a guided or a batch mode:
-- The guided mode drives the user throw all the steps which compose the pipeline, allowing him to change parameters during the study
-- The batch mode allows the user to compute the entire study automatically, chosing all the parameters before starting it
-
-
- 
-
-
-The *pipeline* provided by *ATHENA* can be subdivided in 3 main steps:
-1) **Feature Engineering**: in this step the user can choose the measure to extract and its parameters as sampling frequency, cut
- frequencies to define the studied frequency bands, the number of epochs and the time window of each one and the starting time, extract
- it, and finally compute the temporal and spatial average of the measure values and subdivide the studied subjects in their group
-2) **Analysis**: in this step the user can choose the analysis to compute, between statistical and visual analysis, and their parameters
-3) **Classification**: in this step the user can build a random forest, a decision tree or a multi-layer neural network classifier and its
- performance, allowing to change parameters and to study the discriminant capability of the extracted measures
-
-
- 
-
-
-
-It is also possible to **display the signals**, easily switching between them if they are contained in the same directory.
-The graphic interface allows:
-- To *zoom* or *dezoom* the signal for a better visualization
-- To choose the *length of the time window* to show
-- To select a *specific time instant* to look at
-- To *change the sampling frequency* (if it is not already present inside the signal files)
-- To *select the locations* to show
-- To *save a piece of signal*, by choosing the *starting time*, the *locations* and the *length of the time window* to save
-- To make a *screenshot* of the displayed time window
-
-Furthermore, it is possible to *display the spectra* of the signals, and to execute a **time-frequency analysis** on them.
-
-
- 
-
-
-[**Download Athena now to start you analysis!**](https://github.com/smlacava/Athena/archive/master.zip)
-
-
-## Measure extraction
-The measures extractably by ATHENA can be divided in:
-- *Connectivity measures*
- - **PLI** (Phase Lag Index)
- - **PLV** (Phase Locking Value)
- - **AEC** (Amplitude Envelope Correlation)
- - **Corrected AEC** (Amplitude Envelope Correlation)
- - **Coherence**
- - **ICOH** (Cohenency)
- - **Mutual Information**
- - **Correlation coefficient**
-- *Power measures*
- - **relative PSD** (Power Spectral Density)
-- *Background measures*
- - **Exponent**
- - **Offset**
-- *Entropy measures*
- - **Spectral Entropy**
- - **Sample Entropy**
- - **Approximate Entropy**
- - **Discretized Entropy**
-- *Autocorrelation measures*
- - **Hurst exponent**
-- *Statistical information*
- - **Mean**
- - **Median**
- - **Standard deviation**
- - **Skewness**
- - **Kurtosis**
- - **Variance**
-
-The *parameters* to choose to extract the measures are:
-- **fs**: it is the sampling frequency, it can be automatically chosen if the parameter is present in the time series
-- **cf**: it is the list of cut frequencies, they define each studied frequency band which will be define between two of the chosen cut
- frequencies
-- **epochs number**: it is the number of time epochs to study
-- **epochs time**: it is the time window of each epoch
-- **starting time**: it is the starting time of the first epoch, it is useful to avoid initial noise or altered signal due to preprocessing,
- sources reconstruction, etc.
-- **relative band**: it is used to extract the relative PSD as relative band
-
-
-## Averaging and Grouping
-In this step a table which contains the subjects names and their group (patients group or healthy controls group) is required.
-If it is not present, the user can select the patients between the subject list in a graphic interface that will be create by *ATHENA*
-if the button **␚** is pressed and save the resulting table to use it to compute the step.
-
-
-## Analysis
-For every analysis a file which contains the locations representing each row of the time series is requested.
-This file allow to choose to compute the analysis on the measure pattern of *each location*, on the *asymmetry* between the pattern of the
-right hemisphere and the left one, on the *global* pattern or on the *frontal, temporal, central, parietal and occipital areas*.
-Furthermore, the user can select the conservativeness level which will define the alpha level for the correlation and statistical
-analysis.
-In these analysis, the results will be showed to the user through a p-values table and the significant results will be showed through a
-table and in a graphical way.
-It also possible to investigate the differences intra-subject of the values of a measure in the epochs, or to compute other visual analysis
-
-
-The user can choose between various statistical analysis:
-- **U Test**: the user can verify the presence of statistical differences between the patterns of the patients and the patterns of the
- healthy controls
-- **Index Correlation analysis**: the user can verify the correlation between a group pattern and an index corresponding to each
- subject, an external file containing the index for each subject is required
-- **Measures Correlation analysis**: the user can verify the presence of a correlation in a group between the patterns of two different
- measures
-- **Distribution analysis**: the user can compare the distribution of a measure of the group of patients with the one of the group of healty
- controls
-- **Histogram analysis**: the user can visually analyze the histogram related to the extracted measures
-- **Descriptive statistics**: the user can compute some descriptive statistical measures, such as the median, the variance and the kurtosis,
- and compare these value between the analyzed groups
-
-
-Furthermore, the user can execute some visual analysis:
-- **Scatter Plot analysis**: the user can visually analyze the scatter plot related to two measures, also with different spatial and
- frequency parameters, or to different parameters of the same measure
-- **Epochs Analysis**: the user can study the variation of a measure through the epochs in every frequency band for a subject
-- **Network Measures**: the user can extract some network measures related to the connectivity measures
-
-
- 
-
-
-## Classification
-Finally, the user can train and test a **Decision Tree** classifier, a **Random Forest** classifier or a Multi-Layer **Neural Network** classifier in order
-to verify the discriminant capability of the extracted features and to check if it is possible to distinguish between the two groups of subjects.
-
-
-The user can also **merge the data of the significant results** of every previously computed statistical analysis in a csv file which
-can be used for a classification or other external analysis.
-
-
-## The wiki
-For any doubt, you can consult the toolbox [**wiki**](https://github.com/smlacava/Athena/wiki/Home).
-
-> The toolbox is still under construction
+# ATHENA
+
+
+
+
+***ATHENA*** *(Automatic Toolbox for Handling Experimental Neural Analysis)* is a toolbox which allow to
+automatically extract commonly analyzed measures, used to study neural time series, such as EEG and MEG.
+These measures, which can be connectivity, power or background measures, can be studied through this toolbox, through correlation and
+statistical analysis.
+
+The *pipeline* followed by this toolbox is easy and repetible.
+It is possible to choose between a guided or a batch mode:
+- The guided mode drives the user throw all the steps which compose the pipeline, allowing him to change parameters during the study
+- The batch mode allows the user to compute the entire study automatically, chosing all the parameters before starting it
+
+
+
+
+
+The *pipeline* provided by *ATHENA* can be subdivided in 3 main steps:
+1) **Feature Engineering**: in this step the user can choose the measure to extract and its parameters as sampling frequency, cut
+ frequencies to define the studied frequency bands, the number of epochs and the time window of each one and the starting time, extract
+ it, and finally compute the temporal and spatial average of the measure values and subdivide the studied subjects in their group
+2) **Analysis**: in this step the user can choose the analysis to compute, between statistical and visual analysis, and their parameters
+3) **Classification**: in this step the user can build a random forest, a decision tree or a multi-layer neural network classifier and its
+ performance, allowing to change parameters and to study the discriminant capability of the extracted measures
+
+
+
+
+
+
+It is also possible to **display the signals**, easily switching between them if they are contained in the same directory.
+The graphic interface allows:
+- To *zoom* or *dezoom* the signal for a better visualization
+- To choose the *length of the time window* to show
+- To select a *specific time instant* to look at
+- To *change the sampling frequency* (if it is not already present inside the signal files)
+- To *select the locations* to show
+- To *save a piece of signal*, by choosing the *starting time*, the *locations* and the *length of the time window* to save
+- To make a *screenshot* of the displayed time window
+
+Furthermore, it is possible to *display the spectra* of the signals, and to execute a **time-frequency analysis** on them.
+
+
+
+
+
+[**Download Athena now to start you analysis!**](https://github.com/smlacava/Athena/archive/master.zip)
+
+
+## Measure extraction
+The measures extractably by ATHENA can be divided in:
+- *Connectivity measures*
+ - **PLI** (Phase Lag Index)
+ - **PLV** (Phase Locking Value)
+ - **AEC** (Amplitude Envelope Correlation)
+ - **Corrected AEC** (Amplitude Envelope Correlation)
+ - **Coherence**
+ - **ICOH** (Cohenency)
+ - **Mutual Information**
+ - **Correlation coefficient**
+- *Power measures*
+ - **relative PSD** (Power Spectral Density)
+- *Background measures*
+ - **Exponent**
+ - **Offset**
+- *Entropy measures*
+ - **Spectral Entropy**
+ - **Sample Entropy**
+ - **Approximate Entropy**
+ - **Discretized Entropy**
+- *Autocorrelation measures*
+ - **Hurst exponent**
+- *Statistical information*
+ - **Mean**
+ - **Median**
+ - **Standard deviation**
+ - **Skewness**
+ - **Kurtosis**
+ - **Variance**
+
+The *parameters* to choose to extract the measures are:
+- **fs**: it is the sampling frequency, it can be automatically chosen if the parameter is present in the time series
+- **cf**: it is the list of cut frequencies, they define each studied frequency band which will be define between two of the chosen cut
+ frequencies
+- **epochs number**: it is the number of time epochs to study
+- **epochs time**: it is the time window of each epoch
+- **starting time**: it is the starting time of the first epoch, it is useful to avoid initial noise or altered signal due to preprocessing,
+ sources reconstruction, etc.
+- **relative band**: it is used to extract the relative PSD as relative band
+
+
+## Averaging and Grouping
+In this step a table which contains the subjects names and their group (patients group or healthy controls group) is required.
+If it is not present, the user can select the patients between the subject list in a graphic interface that will be create by *ATHENA*
+if the button **␚** is pressed and save the resulting table to use it to compute the step.
+
+
+## Analysis
+For every analysis a file which contains the locations representing each row of the time series is requested.
+This file allow to choose to compute the analysis on the measure pattern of *each location*, on the *asymmetry* between the pattern of the
+right hemisphere and the left one, on the *global* pattern or on the *frontal, temporal, central, parietal and occipital areas*.
+Furthermore, the user can select the conservativeness level which will define the alpha level for the correlation and statistical
+analysis.
+In these analysis, the results will be showed to the user through a p-values table and the significant results will be showed through a
+table and in a graphical way.
+It also possible to investigate the differences intra-subject of the values of a measure in the epochs, or to compute other visual analysis
+
+
+The user can choose between various statistical analysis:
+- **U Test**: the user can verify the presence of statistical differences between the patterns of the patients and the patterns of the
+ healthy controls
+- **Index Correlation analysis**: the user can verify the correlation between a group pattern and an index corresponding to each
+ subject, an external file containing the index for each subject is required
+- **Measures Correlation analysis**: the user can verify the presence of a correlation in a group between the patterns of two different
+ measures
+- **Distribution analysis**: the user can compare the distribution of a measure of the group of patients with the one of the group of healty
+ controls
+- **Histogram analysis**: the user can visually analyze the histogram related to the extracted measures
+- **Descriptive statistics**: the user can compute some descriptive statistical measures, such as the median, the variance and the kurtosis,
+ and compare these value between the analyzed groups
+
+
+Furthermore, the user can execute some visual analysis:
+- **Scatter Plot analysis**: the user can visually analyze the scatter plot related to two measures, also with different spatial and
+ frequency parameters, or to different parameters of the same measure
+- **Epochs Analysis**: the user can study the variation of a measure through the epochs in every frequency band for a subject
+- **Network Measures**: the user can extract some network measures related to the connectivity measures
+
+
+
+
+
+## Classification
+Finally, the user can train and test a **Decision Tree** classifier, a **Random Forest** classifier or a Multi-Layer **Neural Network** classifier in order
+to verify the discriminant capability of the extracted features and to check if it is possible to distinguish between the two groups of subjects.
+
+
+The user can also **merge the data of the significant results** of every previously computed statistical analysis in a csv file which
+can be used for a classification or other external analysis.
+
+
+## The wiki
+For any doubt, you can consult the toolbox [**wiki**](https://github.com/smlacava/Athena/wiki/Home).
+
+> The toolbox is still under construction
+
+
+## Test
+To execute tests, run the command **runtests(directory,'IncludeSubfolders',true)** with the address of the folder **Test** as **directory** or run **runtest('IncludeSubfolders',true)** with the tests directory as **current folder**.
+
+> The toolbox is still under construction
\ No newline at end of file
diff --git a/Test/MeasuresTest/Example/signal.mat b/Test/MeasuresTest/Example/signal.mat
new file mode 100644
index 0000000..a11234e
Binary files /dev/null and b/Test/MeasuresTest/Example/signal.mat differ
diff --git a/Test/MeasuresTest/FOOOFer_Test.m b/Test/MeasuresTest/FOOOFer_Test.m
new file mode 100644
index 0000000..044765a
--- /dev/null
+++ b/Test/MeasuresTest/FOOOFer_Test.m
@@ -0,0 +1,219 @@
+%This unit test the autocorrelation_measures function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the autocorrelation_measures function for different input
+%parameters. Change outTypes for different entropy measures
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%outTyeps type of measure
+
+
+function tests = FOOOFer_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ for x = {'Offset', 'Exponent'}
+
+ try
+ delete(append(dir, x{1}, '\*'));
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ delete(append(dir, '1\*'));
+ delete(append(dir, 'wrong\*'));
+ rmdir(append(dir, '1'));
+ rmdir(append(dir, 'wrong'));
+ catch
+ end
+
+ clear
+ close all
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ for x = {'Offset', 'Exponent'}
+
+ delete(append(dir, x{1}, '\*'));
+
+ try
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+
+end
+
+
+function testoutTypes_FOOOFer(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = FOOOFer(fs, cf, n_ep, dt, dir, t_start, 1);
+ verifyEqual(testCase, resp, -1);
+ bk = loadFromDisk(append(dir, '1\', file_name));
+ verifyEmpty(testCase, bk); %verify bad out type, data should be empty
+
+ resp = FOOOFer(fs, cf, n_ep, dt, dir, t_start,'wrong');
+ verifyEqual(testCase, resp, -1);
+ bk = loadFromDisk(append(dir, 'wrong\', file_name));
+ verifyEmpty(testCase, bk); %verify wrong out type, data should be empty
+
+end
+
+
+function testTimeAndEpochs_offset(testCase)
+
+ global fs cf dir file_name
+
+ resp = FOOOFer(fs, cf, 5, 20, dir, 0,'OFF');
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off); %verify too many epochs, should be empty
+
+ resp = FOOOFer(fs, cf, 1, 70, dir, 0,'OFF');
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off); %verify too long windows, should be empty
+
+ resp = FOOOFer(fs, cf, 3, 20, dir, 70, 'OFF');
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_offset(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = FOOOFer(0, cf, n_ep, dt, dir, t_start, 'OFF'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off);
+
+ resp = FOOOFer([], cf, n_ep, dt, dir, t_start, 'OFF'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off);
+
+end
+
+
+function testCutFrequencies_offset(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = FOOOFer(fs, [0], n_ep, dt, dir, t_start, 'OFF');
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off); %verify cut frequencies zero, data should be empty
+
+ resp = FOOOFer(fs, [], n_ep, dt, dir, t_start, 'OFF');
+ verifyEqual(testCase, resp, -1);
+ off = loadFromDisk(append(dir, 'Offset\', file_name));
+ verifyEmpty(testCase, off); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_exponent(testCase)
+
+ global fs cf dir file_name
+
+ resp = FOOOFer(fs, cf, 5, 20, dir, 0,'EXP');
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp); %verify too many epochs, should be empty
+
+ resp = FOOOFer(fs, cf, 1, 70, dir, 0,'EXP');
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp); %verify too long windows, should be empty
+
+ resp = FOOOFer(fs, cf, 3, 20, dir, 70, 'EXP');
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_exponent(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = FOOOFer(0, cf, n_ep, dt, dir, t_start, 'EXP'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp);
+
+ resp = FOOOFer([], cf, n_ep, dt, dir, t_start, 'EXP'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp);
+
+end
+
+
+function testCutFrequencies_exponent(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = FOOOFer(fs, [0], n_ep, dt, dir, t_start, 'EXP');
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp); %verify cut frequencies zero, data should be empty
+
+ resp = FOOOFer(fs, [], n_ep, dt, dir, t_start, 'EXP');
+ verifyEqual(testCase, resp, -1);
+ exp = loadFromDisk(append(dir, 'Exponent\', file_name));
+ verifyEmpty(testCase, exp); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/PSDr_Test.m b/Test/MeasuresTest/PSDr_Test.m
new file mode 100644
index 0000000..41e7436
--- /dev/null
+++ b/Test/MeasuresTest/PSDr_Test.m
@@ -0,0 +1,140 @@
+%This unit test the PSDr function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the PSDr function for different input
+%parameters
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%band frequency bands
+
+function tests = PSDr_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ try
+ delete(append(dir, 'PSDr\*'));
+ rmdir(append(dir, 'PSDr'));
+ catch
+ end
+
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ rmdir(append(dir, 'PSDr'));
+ catch
+ end
+
+ clear
+ close all
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ delete(append(dir, 'PSDr\*'));
+
+end
+
+
+function testBandPSDr(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = PSDr(fs, cf, n_ep, dt, dir, t_start, []);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify empty band, data should be empty
+
+ resp = PSDr(fs, cf, n_ep, dt, dir, t_start, [0]);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ PSDr(fs, cf, n_ep, dt, dir, t_start, [0 0]);
+ exp = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEqual(testCase, psdr, exp); %verify zero band, data should be like a single value band
+
+ resp = PSDr(fs, cf, 3, 20, dir, 70, [50]);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ PSDr(fs, cf, n_ep, dt, dir, t_start, [50 50]);
+ exp = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEqual(testCase, psdr, exp); %verify bad band, data should be like a single value band
+
+end
+
+function testTimeAndEpochsPSDr(testCase)
+
+ global fs cf dir band file_name
+
+ resp = PSDr(fs, cf, 5, 20, dir, 0, band);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify too many epochs, data should be empty
+
+ resp = PSDr(fs, cf, 1, 70, dir, 0, band);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify too long windows, should be empty
+
+ resp = PSDr(fs, cf, 3, 20, dir, 70, band);
+ verifyEqual(testCase, resp, 0);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify too long windows, should be a matrix of zeros
+end
+
+
+function testSamplingFrequencyPSDr(testCase)
+
+ global cf n_ep dt t_start dir band file_name
+
+ resp = PSDr(0, cf, n_ep, dt, dir, t_start, band);
+ verifyEqual(testCase, resp, -1);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify sampling frequency zero, data should be empty
+
+end
+
+
+function testCutFrequenciesPSDr(testCase)
+
+ global fs n_ep dt t_start dir band series file_name
+
+ resp = PSDr(fs, [0], n_ep, dt, dir, t_start, band);
+ verifyEqual(testCase, resp, -1);
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify cut frequencies null, data should be empty
+
+ resp = PSDr(fs, [], n_ep, dt, dir, t_start, band);
+ verifyEqual(testCase, resp, -1)
+ psdr = loadFromDisk(append(dir, 'PSDr\', file_name));
+ verifyEmpty(testCase, psdr); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ % %how to test function on single sample
+ % end
+ %end
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/autocorrelation_measures_Test.m b/Test/MeasuresTest/autocorrelation_measures_Test.m
new file mode 100644
index 0000000..efb3a97
--- /dev/null
+++ b/Test/MeasuresTest/autocorrelation_measures_Test.m
@@ -0,0 +1,162 @@
+%This unit test the autocorrelation_measures function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the autocorrelation_measures function for different input
+% parameters. Change outTypes for different entropy measures
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%outTyeps type of measure
+%filter_name name of the filtering functio
+
+
+function tests = autocorrelation_measures_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ try
+ delete(append(dir, 'Hurst\*'));
+ rmdir(append(dir, 'Hurst'));
+ catch
+ end
+
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ clear
+ close all
+
+ try
+ rmdir(append(dir, 'Hurst'));
+ catch
+ end
+
+ try
+ delete(append(dir, '1\*'));
+ delete(append(dir, 'wrong\*'));
+ rmdir(append(dir, '1'));
+ rmdir(append(dir, 'wrong'));
+ catch
+ end
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ delete(append(dir, 'Hurst\*'));
+
+end
+
+
+function testoutTypes_autocorrelation_measures(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = autocorrelation_measures(fs, cf, n_ep, dt, dir, t_start, 1);
+ verifyEqual(testCase, resp, -1);
+ am = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, am); %verify bad out type, data should be empty
+
+ resp = autocorrelation_measures(fs, cf, n_ep, dt, dir, t_start,'wrong');
+ verifyEqual(testCase, resp, -1);
+ am = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, am); %verify wrong out type, data should be empty
+
+end
+
+
+function testFilterName_autocorrelation_measures(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = autocorrelation_measures(fs, cf, n_ep, dt, dir, t_start, 'Hurst', 'wrong');
+ verifyEqual(testCase, resp, -1);
+ am = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, am); %verify wrong filter name, data should be empty
+
+end
+
+
+function testTimeAndEpochs_hurst_index(testCase)
+
+ global fs cf dir file_name
+
+ resp = autocorrelation_measures(fs, cf, 5, 20, dir, 0,'Hurst');
+ verifyEqual(testCase, resp, 0);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he); %verify too many epochs, data should be empty
+
+ resp = autocorrelation_measures(fs, cf, 1, 70, dir, 0,'Hurst');
+ verifyEqual(testCase, resp, -1);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifympty(testCase, he); %verify too long windows, should be empty
+
+ resp = autocorrelation_measures(fs, cf, 3, 20, dir, 70, 'Hurst');
+ verifyEqual(testCase, resp, 0);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_hurst_index(testCase)
+
+ global cf n_ep dt t_start dir
+
+ resp = autocorrelation_measures(0, cf, n_ep, dt, dir, t_start, 'Hurst'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, 0);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he);
+
+ resp = autocorrelation_measures([], cf, n_ep, dt, dir, t_start, 'Hurst'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, 0);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he);
+
+end
+
+
+function testCutFrequencies_hurst_index(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = autocorrelation_measures(fs, [0], n_ep, dt, dir, t_start, 'Hurst');
+ verifyEqual(testCase, resp, -1);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he); %verify cut frequencies zero, data should be empty
+
+ resp = autocorrelation_measures(fs, [], n_ep, dt, dir, t_start, 'Hurst');
+ verifyEqual(testCase, resp, 0);
+ he = loadFromDisk(append(dir, 'Hurst\', file_name));
+ verifyEmpty(testCase, he); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/connectivity_Test.m b/Test/MeasuresTest/connectivity_Test.m
new file mode 100644
index 0000000..7d90e7d
--- /dev/null
+++ b/Test/MeasuresTest/connectivity_Test.m
@@ -0,0 +1,670 @@
+%This unit test the connectivity function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the connectivity function for different input
+%parameters. Change outTypes for different entropy measures
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%outTyeps type of measure
+%filter_name name of the filter
+
+function tests = connectivity_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ for x = {'PLI', 'PLV', 'AEC', 'AECo', 'Coherence', 'ICOH', 'correlation_coefficient', 'wPLI', 'mutual_information'}
+
+ try
+ delete(append(dir, x{1}, '\*'));
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ delete(append(dir, '1\*'));
+ delete(append(dir, 'wrong\*'));
+ rmdir(append(dir, '1'));
+ rmdir(append(dir, 'wrong'));
+ catch
+ end
+
+ clear
+ close all
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ for x = {'PLI', 'PLV', 'AEC', 'AECo', 'Coherence', 'ICOH', 'correlation_coefficient', 'wPLI', 'mutual_information'}
+
+ delete(append(dir, x{1}, '\*'));
+
+ try
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+
+end
+
+
+function testoutTypes_connectivity(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = connectivity(fs, cf, n_ep, dt, dir, t_start, 1);
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, '1\', file_name));
+ verifyEqual(testCase, co, zeros(length(co(:, 1)), length(co(1, :)))); %verify bad out type, data should be zero
+
+ resp = connectivity(fs, cf, n_ep, dt, dir, t_start,'wrong');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'wrong\', file_name));
+ verifyEqual(testCase, co, zeros(length(co(:, 1)), length(co(1, :)))); %verify wrong out type, data should be zero
+
+end
+
+function testFilterName_connectivity(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = connectivity(fs, cf, n_ep, dt, dir, t_start,'PLI', 'wrong');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, co); %verify wrong filter name, data should be empty
+
+end
+
+function testTimeAndEpochs_PLI(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'PLI');
+ verifyEqual(testCase, resp, 0);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli); %verify too many epochs, should be empty
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'PLI');
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli); %verify too long windows, should be empty
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'PLI');
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_PLI(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'PLI'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'PLI'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli);
+
+end
+
+
+function testCutFrequencies_PLI(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'PLI');
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'PLI');
+ verifyEqual(testCase, resp, -1);
+ pli = loadFromDisk(append(dir, 'PLI\', file_name));
+ verifyEmpty(testCase, pli); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_PLV(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'PLV');
+ verifyEqual(testCase, resp, 0);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv); %verify too many epochs, should be empty
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'PLV');
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv); %verify too long windows, should be empty
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'PLV');
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_PLV(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'PLV'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'PLV'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv);
+
+end
+
+
+function testCutFrequencies_PLV(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'PLV');
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'PLV');
+ verifyEqual(testCase, resp, -1);
+ plv = loadFromDisk(append(dir, 'PLV\', file_name));
+ verifyEmpty(testCase, plv); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_AEC(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'AEC');
+ verifyEqual(testCase, resp, 0);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec); %verify too many epochs, should be empty
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'AEC');
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec); %verify too long windows, should be empty
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'AEC');
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_AEC(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'AEC'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'AEC'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec);
+
+end
+
+
+function testCutFrequencies_AEC(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'AEC');
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'AEC');
+ verifyEqual(testCase, resp, -1);
+ aec = loadFromDisk(append(dir, 'AEC\', file_name));
+ verifyEmpty(testCase, aec); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_AECo(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'AECo');
+ verifyEqual(testCase, resp, 0);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco); %verify too many epochs, should be empty
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'AECo');
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco); %verify too long windows, should be empty
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'AECo');
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_AECo(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'AECo'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'AECo'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco);
+
+end
+
+
+function testCutFrequencies_AECo(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'AECo');
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'AECo');
+ verifyEqual(testCase, resp, -1);
+ aeco = loadFromDisk(append(dir, 'AECo\', file_name));
+ verifyEmpty(testCase, aeco); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_coherence(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'Coherence');
+ verifyEqual(testCase, resp, 0);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co); %verify too many epochs, should be empty
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'Coherence');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co); %verify too long windows, should be empty
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'Coherence');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co); %verify too long windows, should be empty
+
+end
+
+
+function testSamplingFrequency_coherence(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'Coherence'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'Coherence'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co);
+
+end
+
+
+function testCutFrequencies_coherence(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'Coherence');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'Coherence');
+ verifyEqual(testCase, resp, -1);
+ co = loadFromDisk(append(dir, 'Coherence\', file_name));
+ verifyEmpty(testCase, co); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_ICOH(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'ICOH');
+ verifyEqual(testCase, resp, 0);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico); %verify too many epochs, last column should be zero
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'ICOH');
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico); %verify too long windows, single column should be zero
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'ICOH');
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico); %verify too long windows, should be a matrix of zeros
+
+end
+
+
+function testSamplingFrequency_ICOH(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'ICOH'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'ICOH'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico);
+
+end
+
+
+function testCutFrequencies_ICOH(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'ICOH');
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'ICOH');
+ verifyEqual(testCase, resp, -1);
+ ico = loadFromDisk(append(dir, 'ICOH\', file_name));
+ verifyEmpty(testCase, ico); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_correlation_coefficient(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'correlation_coefficient');
+ verifyEqual(testCase, resp, 0);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc); %verify too many epochs, last column should be zero
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'correlation_coefficient');
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc); %verify too long windows, single column should be zero
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'correlation_coefficient');
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc); %verify too long windows, should be a matrix of zeros
+
+end
+
+
+function testSamplingFrequency_correlation_coefficient(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'correlation_coefficient'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'correlation_coefficient'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc);
+
+end
+
+
+function testCutFrequencies_correlation_coefficient(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'correlation_coefficient');
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'correlation_coefficient');
+ verifyEqual(testCase, resp, -1);
+ cc = loadFromDisk(append(dir, 'correlation_coefficient\', file_name));
+ verifyEmpty(testCase, cc); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_wPLI(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'wPLI');
+ verifyEqual(testCase, resp, 0);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w); %verify too many epochs, last column should be zero
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'wPLI');
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w); %verify too long windows, single column should be zero
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'wPLI');
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w); %verify too long windows, should be a matrix of zeros
+
+end
+
+
+function testSamplingFrequency_wPLI(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'wPLI'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'wPLI'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w);
+
+end
+
+
+function testCutFrequencies_wPLI(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'wPLI');
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'wPLI');
+ verifyEqual(testCase, resp, -1);
+ w = loadFromDisk(append(dir, 'wPLI\', file_name));
+ verifyEmpty(testCase, w); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_mutual_information(testCase)
+
+ global fs cf dir file_name
+
+ resp = connectivity(fs, cf, 5, 20, dir, 0,'mutual_information');
+ verifyEqual(testCase, resp, 0);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi); %verify too many epochs, last column should be zero
+
+ resp = connectivity(fs, cf, 1, 70, dir, 0,'mutual_information');
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi); %verify too long windows, single column should be zero
+
+ resp = connectivity(fs, cf, 3, 20, dir, 70, 'mutual_information');
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi); %verify too long windows, should be a matrix of zeros
+
+end
+
+
+function testSamplingFrequency_mutual_information(testCase)
+
+ global cf n_ep dt t_start dir
+
+ resp = connectivity(0, cf, n_ep, dt, dir, t_start, 'mutual_information'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi);
+
+ resp = connectivity([], cf, n_ep, dt, dir, t_start, 'mutual_information'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi);
+
+end
+
+
+function testCutFrequencies_mutual_information(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = connectivity(fs, [0], n_ep, dt, dir, t_start, 'mutual_information');
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi); %verify cut frequencies zero, data should be empty
+
+ resp = connectivity(fs, [], n_ep, dt, dir, t_start, 'mutual_information');
+ verifyEqual(testCase, resp, -1);
+ mi = loadFromDisk(append(dir, 'mutual_information\', file_name));
+ verifyEmpty(testCase, mi); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/loadFromDisk.m b/Test/MeasuresTest/loadFromDisk.m
new file mode 100644
index 0000000..bd6ce0d
--- /dev/null
+++ b/Test/MeasuresTest/loadFromDisk.m
@@ -0,0 +1,14 @@
+%This function load data from disk
+%Many Athena functions use disk instead of cache to save the big amount of data returned
+
+function data = loadFromDisk(file_dir)
+
+ try
+ data = load(file_dir);
+ data = struct2cell(data);
+ data = data{1}.data;
+ catch
+ data = [];
+ end
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/load_test_parameters.m b/Test/MeasuresTest/load_test_parameters.m
new file mode 100644
index 0000000..6ee095a
--- /dev/null
+++ b/Test/MeasuresTest/load_test_parameters.m
@@ -0,0 +1,16 @@
+%This function load the parameters used for the test from a csv file
+%Edit the csv file and this function to add parameters
+
+function [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters(file)
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = readvars(file);
+ cf = cf';
+ n_ep = n_ep(1);
+ dt = dt(1);
+ t_start = t_start(1);
+ band = band';
+ band = band(1:2);
+ dir = dir{1, 1};
+ file_name = file_name{1, 1};
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/parameters.csv b/Test/MeasuresTest/parameters.csv
new file mode 100644
index 0000000..8b4c3d0
--- /dev/null
+++ b/Test/MeasuresTest/parameters.csv
@@ -0,0 +1,7 @@
+cf;n_ep;dt;t_start;band;dir;file_name
+1;3;20;0;1;C:\Users\Piermario Zoroddu\Desktop\Athena\Test\MeasuresTest\Example\;signal.mat
+4;;;;40;;
+5;;;;;;
+13;;;;;;
+30;;;;;;
+40;;;;;;
diff --git a/Test/MeasuresTest/spectral_entropy_Test.m b/Test/MeasuresTest/spectral_entropy_Test.m
new file mode 100644
index 0000000..813c982
--- /dev/null
+++ b/Test/MeasuresTest/spectral_entropy_Test.m
@@ -0,0 +1,122 @@
+%This unit test the spectral_entropy function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the spectral_entropy function for different input
+%parameters
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+
+function tests = spectral_entropy_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ try
+ delete(append(dir, 'PEntropy\*'));
+ rmdir(append(dir, 'PEntropy'));
+ catch
+ end
+
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ rmdir(append(dir, 'PEntropy'));
+ catch
+ end
+
+ clear
+ close all
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ delete(append(dir, 'PEntropy\*'));
+
+end
+
+
+function testTimeAndEpochs_spectral_entropy(testCase)
+
+ global fs cf dir file_name
+
+ resp = spectral_entropy(fs, cf, 5, 20, dir, 0);
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se); %verify too many epochs, data should be empty
+
+ resp = spectral_entropy(fs, cf, 1, 70, dir, 0);
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se); %verify too long windows, should be empty
+
+ resp = spectral_entropy(fs, cf, 3, 20, dir, 70);
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se); %verify too long windows, should be mepty
+
+end
+
+
+function testSamplingFrequency_spectral_entropy(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = spectral_entropy(0, cf, n_ep, dt, dir, t_start); %verify sampling frequency zero, should be empty
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se);
+
+ resp = spectral_entropy([], cf, n_ep, dt, dir, t_start); %verify sampling frequency null, should be empty
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se);
+
+end
+
+
+function testCutFrequencies_spectral_entropy(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = spectral_entropy(fs, [0], n_ep, dt, dir, t_start);
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se); %verify cut frequencies zero, should be empty
+
+ resp = spectral_entropy(fs, [], n_ep, dt, dir, t_start);
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'PEntropy\', file_name));
+ verifyEmpty(testCase, se); %verify cut frequencies empty, should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/statistical_information_Test.m b/Test/MeasuresTest/statistical_information_Test.m
new file mode 100644
index 0000000..a61fbe2
--- /dev/null
+++ b/Test/MeasuresTest/statistical_information_Test.m
@@ -0,0 +1,479 @@
+%This unit test the statistical_information function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the statistical_information function for different input
+%parameters. Change outTypes for different entropy measures
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%outTyeps type of measure
+%filter_name name of the filter
+
+function tests = statistical_information_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ for x = {'Median', 'Mean', 'Standard_deviation', 'Variance', 'Skewness', 'Kurtosis'}
+
+ try
+ delete(append(dir, x{1}, '\*'));
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ delete(append(dir, '1\*'));
+ delete(append(dir, 'wrong\*'));
+ rmdir(append(dir, '1'));
+ rmdir(append(dir, 'wrong'));
+ catch
+ end
+
+ clear
+ close all
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ for x = {'Median', 'Mean', 'Standard_deviation', 'Variance', 'Skewness', 'Kurtosis'}
+
+ delete(append(dir, x{1}, '\*'));
+
+ try
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+
+end
+
+
+function testoutTypes_statistical_information(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(fs, cf, n_ep, dt, dir, t_start, 1);
+ verifyEqual(testCase, resp, -1);
+ sa = loadFromDisk(append(dir, '1\', file_name));
+ verifyEqual(testCase, sa, zeros(length(sa(:, 1)), length(sa(1, :)))); %verify bad out type, data should be a matrix of zeros
+
+ resp = statistical_information(fs, cf, n_ep, dt, dir, t_start,'wrong');
+ verifyEqual(testCase, resp, -1);
+ sa = loadFromDisk(append(dir, 'wrong\', file_name));
+ verifyEqual(testCase, sa, zeros(length(sa(:, 1)), length(sa(1, :)))); %verify wrong out type, data should be a matrix of zeros
+
+end
+
+
+function testFilterName_statistical_information(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(fs, cf, n_ep, dt, dir, t_start,'Median', 'wrong');
+ verifyEqual(testCase, resp, -1);
+ sa = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, sa); %verify wrong filter name, data should be empty
+
+end
+
+
+function testTimeAndEpochs_median(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Median');
+ verifyEqual(testCase, resp, 0);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Median');
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Median');
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_median(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Median'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Median'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md);
+
+end
+
+
+function testCutFrequencies_median(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Median');
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Median');
+ verifyEqual(testCase, resp, -1);
+ md = loadFromDisk(append(dir, 'Median\', file_name));
+ verifyEmpty(testCase, md); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_mean(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Mean');
+ verifyEqual(testCase, resp, 0);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Mean');
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Mean');
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_mean(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Mean'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Mean'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m);
+
+end
+
+
+function testCutFrequencies_mean(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Mean');
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Mean');
+ verifyEqual(testCase, resp, -1);
+ m = loadFromDisk(append(dir, 'Mean\', file_name));
+ verifyEmpty(testCase, m); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_standard_deviation(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Standard_deviation');
+ verifyEqual(testCase, resp, 0);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Standard_deviation');
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Standard_deviation');
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_standard_deviation(testCase)
+
+ global cf n_ep dt t_start dir
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Standard_deviation'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Standard_deviation'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std);
+
+end
+
+
+function testCutFrequencies_standard_deviation(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Standard_deviation');
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Standard_deviation');
+ verifyEqual(testCase, resp, -1);
+ std = loadFromDisk(append(dir, 'Standard_deviation\', file_name));
+ verifyEmpty(testCase, std); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_variance(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Variance');
+ verifyEqual(testCase, resp, 0);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Variance');
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Variance');
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v); %verify too long windows, data should be empty
+end
+
+
+function testSamplingFrequency_variance(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Variance'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Variance'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v);
+
+end
+
+
+function testCutFrequencies_variance(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Variance');
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Variance');
+ verifyEqual(testCase, resp, -1);
+ v = loadFromDisk(append(dir, 'Variance\', file_name));
+ verifyEmpty(testCase, v); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+end
+
+
+function testTimeAndEpochs_skewness(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Skewness');
+ verifyEqual(testCase, resp, 0);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Skewness');
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Skewness');
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_skewness(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Skewness'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Skewness'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk);
+
+end
+
+
+function testCutFrequencies_skewness(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Skewness');
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Skewness');
+ verifyEqual(testCase, resp, -1);
+ sk = loadFromDisk(append(dir, 'Skewness\', file_name));
+ verifyEmpty(testCase, sk); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+end
+
+
+function testTimeAndEpochs_kurtosis(testCase)
+
+ global fs cf dir file_name
+
+ resp = statistical_information(fs, cf, 5, 20, dir, 0,'Kurtosis');
+ verifyEqual(testCase, resp, 0);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku); %verify too many epochs, data should be empty
+
+ resp = statistical_information(fs, cf, 1, 70, dir, 0,'Kurtosis');
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku); %verify too long windows, data should be empty
+
+ resp = statistical_information(fs, cf, 3, 20, dir, 70, 'Kurtosis');
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_kurtosis(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = statistical_information(0, cf, n_ep, dt, dir, t_start, 'Kurtosis'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku);
+
+ resp = statistical_information([], cf, n_ep, dt, dir, t_start, 'Kurtosis'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku);
+
+end
+
+
+function testCutFrequencies_kurtosis(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = statistical_information(fs, [0], n_ep, dt, dir, t_start, 'Kurtosis');
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku); %verify cut frequencies zero, data should be empty
+
+ resp = statistical_information(fs, [], n_ep, dt, dir, t_start, 'Kurtosis');
+ verifyEqual(testCase, resp, -1);
+ ku = loadFromDisk(append(dir, 'Kurtosis\', file_name));
+ verifyEmpty(testCase, ku); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+end
\ No newline at end of file
diff --git a/Test/MeasuresTest/time_entropy_Test.m b/Test/MeasuresTest/time_entropy_Test.m
new file mode 100644
index 0000000..d7d39ee
--- /dev/null
+++ b/Test/MeasuresTest/time_entropy_Test.m
@@ -0,0 +1,296 @@
+%This unit test the time_entropy function. Recommended use a .mat signal named
+%signal.mat (time serie index must be named data) in Example folder
+%These functions test the output of the time_entropy function for different input
+%parameters. Change outTypes for different entropy measures
+%fs sample frequency
+%cf cut frequencies
+%n_ep number of epochs
+%dt time window
+%dir directory
+%t_start starting time
+%outTyeps type of measure
+%filter_name name of the filtering functio
+%m embedding dimension
+%r fraction of standard deviation
+
+function tests = time_entropy_Test
+
+ tests = functiontests(localfunctions);
+
+end
+
+
+function setupOnce(testCase)
+
+ global fs cf n_ep dt t_start band dir file_name series
+
+ [cf,n_ep,dt,t_start,band,dir,file_name] = load_test_parameters("parameters.csv");
+
+ signal = load(append(dir, file_name));
+ series = signal.data.time_series; %time series
+ fs = signal.data.fs; %sample frequency
+
+ for x = {'discretized_entropy', 'sample_entropy', 'approximate_entropy'}
+
+ try
+ delete(append(dir, x{1}, '\*'));
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+end
+
+
+function teardownOnce(testCase)
+
+ global dir
+
+ try
+ delete(append(dir, '1\*'));
+ delete(append(dir, 'wrong\*'));
+ rmdir(append(dir, '1'));
+ rmdir(append(dir, 'wrong'));
+ catch
+ end
+
+ clear
+ close all
+
+end
+
+
+function teardown(testCase)
+
+ global dir
+
+ for x = {'discretized_entropy', 'sample_entropy', 'approximate_entropy'}
+
+ delete(append(dir, x{1}, '\*'));
+
+ try
+ rmdir(append(dir, x{1}));
+ catch
+ end
+
+ end
+
+end
+
+
+function testoutTypes_time_entropy(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = time_entropy(fs, cf, n_ep, dt, dir, t_start, 1);
+ verifyEqual(testCase, resp, -1);
+ te = loadFromDisk(append(dir, '1\', file_name));
+ verifyEqual(testCase, te(: , end), zeros(length(te(:, end)), 1)); %verify bad out type, data should be a matrix of zeros
+
+ resp = time_entropy(fs, cf, n_ep, dt, dir, t_start,'wrong');
+ verifyEqual(testCase, resp, -1);
+ te = loadFromDisk(append(dir, 'wrong\', file_name));
+ verifyEqual(testCase, te(: , end), zeros(length(te(:, end)), 1)); %verify wrong out type, data should be a matrix of zeros
+
+end
+
+
+function testFilterName_time_entropy(testCase)
+
+ global fs cf n_ep dt t_start dir file_name
+
+ resp = time_entropy(fs, cf, n_ep, dt, dir, t_start,'discretized_entropy', 'wrong');
+ verifyEqual(testCase, resp, -1);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify wrong filter name, data should be empty
+
+end
+
+
+function testTimeAndEpochs_discretized_entropy(testCase)
+
+ global fs cf dir file_name
+
+ resp = time_entropy(fs, cf, 5, 20, dir, 0,'discretized_entropy');
+ verifyEqual(testCase, resp, 0);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify too many epochs, last column should be empty
+
+ resp = time_entropy(fs, cf, 1, 70, dir, 0,'discretized_entropy');
+ verifyEqual(testCase, resp, -1);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify too long windows, data should be empty
+
+ resp = time_entropy(fs, cf, 3, 20, dir, 70, 'discretized_entropy');
+ verifyEqual(testCase, resp, -1);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_discretized_entropy(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = time_entropy(0, cf, n_ep, dt, dir, t_start, 'discretized_entropy'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, 0);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de);
+
+ resp = time_entropy([], cf, n_ep, dt, dir, t_start, 'discretized_entropy'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, 0);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de);
+
+end
+
+
+function testCutFrequencies_discretized_entropy(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = time_entropy(fs, [0], n_ep, dt, dir, t_start, 'discretized_entropy');
+ verifyEqual(testCase, resp, -1);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify cut frequencies zero, data should be empty
+
+ resp = time_entropy(fs, [], n_ep, dt, dir, t_start, 'discretized_entropy');
+ verifyEqual(testCase, resp, -1);
+ de = loadFromDisk(append(dir, 'discretized_entropy\', file_name));
+ verifyEmpty(testCase, de); %verify cut frequencies empty, data should be empty
+
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_sample_entropy(testCase)
+
+ global fs cf dir file_name
+
+ resp = time_entropy(fs, cf, 5, 20, dir, 0,'sample_entropy');
+ verifyEqual(testCase, resp, 0);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEmpty(testCase, se); %verify too many epochs, last column should be empty
+
+ resp = time_entropy(fs, cf, 1, 70, dir, 0,'sample_entropy');
+ verifyEqual(testCase, resp, 0);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEqual(testCase, se(: , 1), zeros(length(se(:, 1)), 1)); %verify too long windows, single column should be zero
+
+ resp = time_entropy(fs, cf, 3, 20, dir, 70, 'sample_entropy');
+ verifyEqual(testCase, resp, 0);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEqual(testCase, se, zeros(length(se(1, :)), length(se(:, 1)))); %verify too long windows, should be a matrix of zeros
+
+end
+
+
+function testSamplingFrequency_sample_entropy(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = time_entropy(0, cf, n_ep, dt, dir, t_start, 'sample_entropy'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, 0);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEmpty(testCase, se);
+
+ resp = time_entropy([], cf, n_ep, dt, dir, t_start, 'sample_entropy'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, 0);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEmpty(testCase, se);
+
+end
+
+
+function testCutFrequencies_sample_entropy(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = time_entropy(fs, [0], n_ep, dt, dir, t_start, 'sample_entropy');
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEmpty(testCase, se); %verify cut frequencies zero, data should be empty
+
+ resp = time_entropy(fs, [], n_ep, dt, dir, t_start, 'sample_entropy');
+ verifyEqual(testCase, resp, -1);
+ se = loadFromDisk(append(dir, 'sample_entropy\', file_name));
+ verifyEmpty(testCase, se); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ % for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+ %
+ % end
+ %end
+
+end
+
+
+function testTimeAndEpochs_approximate_entropy(testCase)
+
+ global fs cf dir file_name
+
+ resp = time_entropy(fs, cf, 5, 20, dir, 0,'approximate_entropy');
+ verifyEqual(testCase, resp, 0);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae); %verify too many epochs, data should be empty
+
+ resp = time_entropy(fs, cf, 1, 70, dir, 0,'approximate_entropy');
+ verifyEqual(testCase, resp, -1);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae); %verify too long windows, data should be empty
+
+ resp = time_entropy(fs, cf, 3, 20, dir, 70, 'approximate_entropy');
+ verifyEqual(testCase, resp, -1);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae); %verify too long windows, data should be empty
+
+end
+
+
+function testSamplingFrequency_approximate_entropy(testCase)
+
+ global cf n_ep dt t_start dir file_name
+
+ resp = time_entropy(0, cf, n_ep, dt, dir, t_start, 'approximate_entropy'); %verify sampling frequency zero, data should be empty
+ verifyEqual(testCase, resp, 0);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae);
+
+ resp = time_entropy([], cf, n_ep, dt, dir, t_start, 'approximate_entropy'); %verify sampling frequency null, data should be empty
+ verifyEqual(testCase, resp, 0);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae);
+
+end
+
+
+function testCutFrequencies_approximate_entropy(testCase)
+
+ global fs n_ep dt t_start dir series file_name
+
+ resp = time_entropy(fs, [0], n_ep, dt, dir, t_start, 'approximate_entropy');
+ verifyEqual(testCase, resp, -1);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae); %verify cut frequencies zero, data should be empty
+
+ resp = time_entropy(fs, [], n_ep, dt, dir, t_start, 'approximate_entropy');
+ verifyEqual(testCase, resp, -1);
+ ae = loadFromDisk(append(dir, 'approximate_entropy\', file_name));
+ verifyEmpty(testCase, ae); %verify cut frequencies empty, data should be empty
+
+ %for k = 1:n_ep
+ %for j = 1:size(series, 1)
+ % data = squeeze(series(j, dt*(k-1)+1:k*dt));
+
+ %end
+ %end
+end
\ No newline at end of file