Homec4science
Diffusion Slepians (master) Browse

Slepi/Code/Utilities/spm12/spm_eeg_tf_rescale.m
8a186cd82567master

Last Change
Raw File

spm_eeg_tf_rescale.m
View Options

function D = spm_eeg_tf_rescale(S)
% Rescale (avg) spectrogram with nonlinear and/or difference operator
% FORMAT [D] = spm_eeg_tf_rescale(S)
%
% S - input structure (optional)
% fields of S:
% S.D - MEEG object or filename of M/EEG mat-file
% S.method - 'LogR', 'Diff', 'Rel', 'Log', 'Sqrt', 'None'
% S.timewin - 2-element vector: start and stop of baseline (ms)
% (need to specify this for LogR and Diff)
% S.Db - MEEG object or filename of M/EEG mat-file to use
% for the baseline (if different from the input dataset).
% prefix - prefix for the output file (default - 'r')
%
% Output:
% D - MEEG object with rescaled power data (also
% written to disk with prefix r)
%
% For 'Log' and 'Sqrt', these functions are applied to spectrogram
% For 'LogR', 'Rel' and 'Diff' this function computes power in the baseline
% p_b and outputs (i) p-p_b for 'Diff' (ii) 100*(p-p_b)/p_b for 'Rel'
% (iii) log (p/p_b) for 'LogR'
%__________________________________________________________________________
% Copyright (C) 2009-2012 Wellcome Trust Centre for Neuroimaging
% Will Penny
% $Id: spm_eeg_tf_rescale.m 5626 2013-08-30 14:03:16Z vladimir $
SVNrev = '$Rev: 5626 $';
%-Startup
%--------------------------------------------------------------------------
spm('FnBanner', mfilename, SVNrev);
spm('FigName','M/EEG Time-frequency rescale'); spm('Pointer','Watch');
if ~isfield(S, 'prefix'), S.prefix = 'r'; end
if ~isfield(S, 'timewin'), S.timewin = [-Inf 0]; end
D = spm_eeg_load(S.D);
Dnew = clone(D, [S.prefix fname(D)]);
needbaseline = ismember(lower(S.method), {'logr','diff', 'rel', 'zscore'});
if needbaseline
if isfield(S, 'Db') && ~isempty(S.Db)
Db = spm_eeg_load(S.Db);
else
Db = D;
end
if any(abs(D.frequencies - Db.frequencies) > 0.1) || ~isequal(Db.chanlabels, D.chanlabels) ||...
(Db.ntrials > 1 && (Db.ntrials ~= D.ntrials))
error('The input dataset and the baseline dataset should have the same frequencies, channels and trial numbers');
end
timeind = Db.indsample(1e-3*(min(S.timewin))):Db.indsample(1e-3*(max(S.timewin)));
if isempty(timeind) || any(isnan(timeind))
error('Selected baseline time window is invalid.');
end
end
spm_progress_bar('Init', D.ntrials, 'Trials processed');
if D.ntrials > 100, Ibar = floor(linspace(1, D.ntrials, 100));
else Ibar = 1:D.ntrials; end
for i = 1:D.ntrials
if needbaseline
x = spm_squeeze(D(:,:,:,i), 4);
if Db.ntrials > 1
xbase = spm_squeeze(Db(:,:,:,i), 4);
else
xbase = spm_squeeze(Db(:,:,:,1), 4);
end
end
switch lower(S.method)
case 'logr'
xbase = mean(log10(xbase(:,:,timeind)),3);
Dnew(:,:,:,i) = 10*(log10(x) - repmat(xbase,[1 1 Dnew.nsamples 1]));
case 'diff'
xbase = mean(xbase(:, :, timeind),3);
Dnew(:,:,:,i) = (x - repmat(xbase,[1 1 Dnew.nsamples 1]));
case 'zscore'
stdev = std(xbase(:, :, timeind), [], 3);
xbase = mean(xbase(:,:, timeind),3);
Dnew(:, :, :, i) = (x - repmat(xbase,[1 1 Dnew.nsamples 1]))./repmat(stdev,[1 1 Dnew.nsamples 1]);
case 'rel'
xbase = mean(xbase(:, :, timeind), 3);
Dnew(:, :, :, i) = 100*((x./repmat(xbase,[1 1 Dnew.nsamples 1]) - 1));
case 'log'
Dnew(:, :, :, i) = log(D(:, :, :, i));
case 'logeps'
Dnew(:, :, : ,i) = log(D(:, :, :, i)+(1/64)*mean(spm_vec(D(:, :, :, i))));
case 'sqrt'
Dnew(:, :, :, i) = sqrt(D(:, :, :, i));
case 'none'
Dnew(:, :, :, i) = D(:, :, :, i);
otherwise
error('Unknown rescaling method');
end
if ismember(i, Ibar), spm_progress_bar('Set', i); end
end
spm_progress_bar('Clear');
switch lower(S.method)
case 'logr'
Dnew = units(Dnew, ':', 'dB');
case 'rel'
Dnew = units(Dnew, ':', '%');
end
D = Dnew;
% Save
D = D.history(mfilename, S);
save(D);
%-Cleanup
%--------------------------------------------------------------------------
spm('FigName','M/EEG Time Frequency Rescale: done');
spm('Pointer','Arrow');
c4science · Help