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Slepi/Code/Utilities/spm12/toolbox/MEEGtools/spm_eeg_split_conditions.m
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spm_eeg_split_conditions.m
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function D = spm_eeg_split_conditions(S)
% Splits a file into different conditions in order to facilitate TF
% processing. The idea is to create several smaller files, run TF, then
% aveage within the condition files using spm_eeg_average_tf and lastly,
% merge again.
% FORMAT D = spm_eeg_split_conditions(S)
%
% S - optional input struct
% (optional) fields of S:
% D - MEEG object or filename of M/EEG mat-file with epoched data
%
% Output:
% D - MEEG object (also written on disk)
%
% The function also physically removes bad trials.
%
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
% Dominik R Bach
% based on spm_eeg_remove_bad_trials
SVNrev = '$Rev: 6374 $';
%-Startup
%--------------------------------------------------------------------------
spm('FnBanner', mfilename, SVNrev);
spm('FigName','Remove bad trials'); spm('Pointer','Watch');
%-Get MEEG object
%--------------------------------------------------------------------------
try
D = S.D;
catch
[D, sts] = spm_select(1, 'mat', 'Select M/EEG mat file');
if ~sts, D = []; return; end
S.D = D;
end
D = spm_eeg_load(D);
%-Check that there is any good data available
%--------------------------------------------------------------------------
if ntrials(D)==0 || all(badtrials(D, ':'))
warning('No good trials were found. Nothing to do.');
return;
end
%-select trials
%--------------------------------------------------------------------------
cl = D.condlist;
goodtrials = [];
for i = 1:numel(cl)
goodtrials{i} = indtrial(D, cl{i}, 'GOOD');
end
%-Generate new files & copy data
%--------------------------------------------------------------------------
for i = 1:length(goodtrials)
spm_progress_bar('Init', length(goodtrials{i}), 'Trials copied');
if length(goodtrials{i}) > 100, Ibar = floor(linspace(1, length(goodtrials{i}), 100));
else Ibar = [1:length(goodtrials{i})]; end
fn = fname(D);
fn = ['r', fn(1:(end-4)), sprintf('_%02.0f.dat', i)];
if strncmpi(D.transformtype,'TF',2) % TF and TFphase
Dnew = clone(D, fn, [D.nchannels D.nfrequencies D.nsamples numel(goodtrials{i})]);
else
Dnew = clone(D, fn, [D.nchannels D.nsamples numel(goodtrials{i})]);
end
for trl = 1:numel(goodtrials{i})
if strncmpi(D.transformtype,'TF',2) % TF and TFphase
Dnew(:, :, :, trl) = D(:, :, :, goodtrials{i}(trl));
else
Dnew(:, :, trl) = D(:, :, goodtrials{i}(trl));
end
end;
if ismember(i, Ibar), spm_progress_bar('Set', i); end
%-Copy trial-specific data.
%--------------------------------------------------------------------------
Dnew = conditions(Dnew, ':', conditions(D, goodtrials{i}));
Dnew = repl(Dnew, ':', repl(D, goodtrials{i}));
Dnew = events(Dnew, ':', events(D, goodtrials{i}));
Dnew = trialonset(Dnew, ':', trialonset(D, goodtrials{i}));
%-Save the new M/EEG dataset
%--------------------------------------------------------------------------
Dnew = Dnew.history(mfilename, S);
save(Dnew);
DD{i} = Dnew;
end %
spm_progress_bar('Clear');
D = DD;
%-Cleanup
%--------------------------------------------------------------------------
spm('FigName','Split conditions: done'); spm('Pointer','Arrow');
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