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Slepi/Code/Utilities/spm12/spm_eeg_regressors_tfpower.m
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spm_eeg_regressors_tfpower.m
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function res = spm_eeg_regressors_tfpower(S)
% Generate regressors from power in TF dataset
% S - input structure
% fields of S:
% S.D - M/EEG object
%
% Additional parameters can be defined specific for each plugin
% Output:
% res -
% If no input is provided the plugin returns a cfg branch for itself
%
% If input is provided the plugin returns
%______________________________________________________________________________________
% Copyright (C) 2014 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
SVNrev = '$Rev: 6188 $';
if nargin == 0
%--------------------------------------------------------------------------
% TF power dataset
%--------------------------------------------------------------------------
Dtf = cfg_files;
Dtf.tag = 'Dtf';
Dtf.name = 'TF power dataset name';
Dtf.filter = 'mat';
Dtf.num = [1 1];
Dtf.help = {'Select the M/EEG mat file containing TF power data'};
%--------------------------------------------------------------------------
% timewin
%--------------------------------------------------------------------------
timewin = cfg_entry;
timewin.tag = 'timewin';
timewin.name = 'Time window';
timewin.help = {['Start and stop of the time window [ms]. ' ...
'(Used only for summarised epoched cases)']};
timewin.strtype = 'r';
timewin.num = [1 2];
timewin.val = {[-Inf Inf]};
%--------------------------------------------------------------------------
% freqwin
%--------------------------------------------------------------------------
freqwin = cfg_entry;
freqwin.tag = 'freqwin';
freqwin.name = 'Frequency window';
freqwin.help = {'Start and stop of the frequency window (Hz).'};
freqwin.strtype = 'r';
freqwin.num = [1 2];
freqwin.val = {[-Inf Inf]};
%--------------------------------------------------------------------------
% average
%--------------------------------------------------------------------------
average = cfg_menu;
average.tag = 'average';
average.name = 'Average over frequency';
average.labels = {'Yes', 'No'};
average.val = {1};
average.values = {1,0};
average.help = {'Average over the frequency window to produce one regressor',...
'or output each frequency as a separate regressor'};
%--------------------------------------------------------------------------
% standardize
%--------------------------------------------------------------------------
standardize = cfg_menu;
standardize.tag = 'standardize';
standardize.name = 'Standardize';
standardize.labels = {'Yes', 'No'};
standardize.val = {0};
standardize.values = {1,0};
standardize.help = {'Standardize (zscore) regressor values'};
%--------------------------------------------------------------------------
% regname
%--------------------------------------------------------------------------
regname = cfg_entry;
regname.tag = 'regname';
regname.name = 'Regressor name';
regname.help = {'Specify the string to be used as regressor name.'};
regname.strtype = 's';
regname.num = [1 Inf];
regname.val = {'TFpower'};
tfpower = cfg_branch;
tfpower.tag = 'tfpower';
tfpower.name = 'Time-frequency power';
tfpower.val = {Dtf, spm_cfg_eeg_channel_selector, timewin, freqwin, average, standardize, regname};
res = tfpower;
return
end
%-Startup
%--------------------------------------------------------------------------
spm('sFnBanner', mfilename, SVNrev);
spm('FigName','Time-frequency power regressors');
if ~isfield(S, 'timewin'), S.timewin = [-Inf Inf]; end
if ~isfield(S, 'freqwin'), S.freqwin = [-Inf Inf]; end
if iscell(S.Dtf)
S.Dtf = char(S.Dtf);
end
Dtf = spm_eeg_load(S.Dtf);
D = spm_eeg_load(S.D);
if ~isequal(Dtf.transformtype, 'TF');
error('Time-frequency power dataset is expected as input.')
end
freqind = Dtf.indfrequency(min(S.freqwin)):Dtf.indfrequency(max(S.freqwin)); %BW
if isempty(freqind) || any(isnan(freqind))
error('Selected frequency window is invalid.');
end
chanind = setdiff(Dtf.selectchannels(spm_cfg_eeg_channel_selector(S.channels)), Dtf.badchannels);
if isempty(chanind)
error('No channels were selected');
end
if isequal(D.type, 'continuous')
if ~isequal(Dtf.type, 'continuous') || (D.time(1) < Dtf.time(1)) || (D.time(end)>Dtf.time(end))
error('All times of the input dataset should be within the power dataset.');
end
data = spm_squeeze(mean(Dtf(chanind, freqind, :), 1), [1 3]);
if S.average
data = mean(data, 1);
end
if D.fsample ~= Dtf.fsample
[data, alpha] = spm_timeseries_resample(data, D.fsample/Dtf.fsample);
else
alpha = 1;
end
start = round(alpha*Dtf.indsample(D.time(1)));
data = data(:, start:(start+D.nsamples-1));
else
if D.ntrials ~= Dtf.ntrials
error('Trial numbers should be equal between input and power dataset.');
end
if S.summarise
timeind = D.indsample(1e-3*(min(S.timewin))):D.indsample(1e-3*(max(S.timewin)));
if isempty(timeind) || any(isnan(timeind))
error('Selected time window is invalid.');
end
else
timeind = 1:D.nsamples;
end
data = spm_squeeze(mean(Dtf(chanind, freqind, timeind, :), 1), 1);
if S.average
data = mean(data, 1);
end
if S.summarise
data = spm_squeeze(mean(data, 2), 2);
else
data = reshape(data, size(data, 1), []);
end
end
data = data';
if S.standardize
data = (data - repmat(mean(data, 1), size(data, 1), 1))./repmat(std(data, 1), size(data, 1), 1);
end
res.R = data;
if S.average
res.names = {S.regname};
else
freq = Dtf.frequencies(freqind);
for i = 1:length(freq)
res.names{i} = sprintf('%s_%.1fHz', S.regname, freq(i));
end
end
spm('FigName','Time-frequency power regressors: done');
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