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Slepi/Code/Utilities/spm12/spm_eeg_regressors_chandata.m
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spm_eeg_regressors_chandata.m
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function res = spm_eeg_regressors_chandata(S)
% Generate regressors from channel data
% 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
% $Id: spm_eeg_regressors_chandata.m 6186 2014-09-22 11:31:11Z vladimir $
SVNrev = '$Rev: 6186 $';
if nargin == 0
%--------------------------------------------------------------------------
% TF power dataset
%--------------------------------------------------------------------------
Dr = cfg_files;
Dr.tag = 'Dr';
Dr.name = 'Regressor dataset name';
Dr.filter = 'mat';
Dr.num = [1 1];
Dr.help = {'Select the M/EEG mat file containing regressor 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 the epoched case)'};
timewin.strtype = 'r';
timewin.num = [1 2];
timewin.val = {[-Inf Inf]};
chandata = cfg_branch;
chandata.tag = 'chandata';
chandata.name = 'Channel data';
chandata.val = {Dr, spm_cfg_eeg_channel_selector, timewin};
res = chandata;
return
end
%-Startup
%--------------------------------------------------------------------------
spm('sFnBanner', mfilename, SVNrev);
spm('FigName','Channel data regressors');
if ~isfield(S, 'timewin'), S.timewin = [-Inf Inf]; end
if iscell(S.Dr)
S.Dr = char(S.Dr);
end
Dr = spm_eeg_load(S.Dr);
D = spm_eeg_load(S.D);
if isequal(Dr.transformtype, 'TF');
error('Time domain dataset is expected as input.')
end
chanind = setdiff(Dr.selectchannels(spm_cfg_eeg_channel_selector(S.channels)), Dr.badchannels);
if isempty(chanind)
error('No channels were selected');
end
res.R = [];
for i = 1:length(chanind)
if isequal(D.type, 'continuous')
if ~isequal(Dr.type, 'continuous') || (D.time(1) < Dr.time(1)) || (D.time(end)>Dr.time(end))
error('All times of the input dataset should be within the power dataset.');
end
data = Dr(chanind(i), :);
if D.fsample ~= Dr.fsample
[data, alpha] = spm_timeseries_resample(data, D.fsample/Dr.fsample);
else
alpha = 1;
end
start = round(alpha*Dr.indsample(D.time(1)));
data = data(:, start:(start+D.nsamples-1));
else
if D.ntrials ~= Dr.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(Dr(chanind(i), timeind, :), 1);
if S.summarise
data = spm_squeeze(mean(data, 1), 1);
else
data = reshape(data, 1, []);
end
end
res.R = [res.R data(:)];
end
res.names = Dr.chanlabels(chanind);
spm('FigName', 'Channel data regressors: done');
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