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ft_freqanalysis_mvar.m
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ft_freqanalysis_mvar.m
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function [freq] = ft_freqanalysis_mvar(cfg, data)
% FT_FREQANALYSIS_MVAR performs frequency analysis on
% mvar data, by fourier transformation of the coefficients. The output
% contains cross-spectral density, spectral transfer matrix, and the
% covariance of the innovation noise. The dimord = 'chan_chan(_freq)(_time)
%
% The function is stand-alone, but is typically called through
% FT_FREQANALYSIS, specifying cfg.method = 'mvar'.
%
% Use as
% [freq] = ft_freqanalysis(cfg, data), with cfg.method = 'mvar'
%
% or
%
% [freq] = ft_freqanalysis_mvar(cfg, data)
%
% The input data structure should be a data structure created by
% FT_MVARANALYSIS, i.e. a data-structure of type 'mvar'.
%
% The configuration can contain:
% cfg.foi = vector with the frequencies at which the spectral quantities
% are estimated (in Hz). Default: 0:1:Nyquist
% cfg.feedback = 'none', or any of the methods supported by FT_PROGRESS,
% for providing feedback to the user in the command
% window.
%
% To facilitate data-handling and distributed computing you can use
% cfg.inputfile = ...
% cfg.outputfile = ...
% If you specify one of these (or both) the input data will be read from a *.mat
% file on disk and/or the output data will be written to a *.mat file. These mat
% files should contain only a single variable, corresponding with the
% input/output structure.
%
% See also FT_MVARANALYSIS, FT_DATATYPE_MVAR, FT_PROGRESS
% Copyright (C) 2009, Jan-Mathijs Schoffelen
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
%
% $Id$
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar data
ft_preamble provenance data
ft_preamble trackconfig
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
cfg.foi = ft_getopt(cfg, 'foi', 'all');
cfg.feedback = ft_getopt(cfg, 'feedback', 'none');
%cfg.channel = ft_getopt(cfg, 'channel', 'all');
%cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'no');
%cfg.jackknife = ft_getopt(cfg, 'jackknife', 'no');
%cfg.keeptapers = ft_getopt(cfg, 'keeptapers', 'yes');
if strcmp(cfg.foi, 'all'),
cfg.foi = (0:1:data.fsampleorig/2);
end
dimtok = tokenize(data.dimord, '_');
isfull = isfield(data, 'label') && sum(strcmp(dimtok,'chan'))==2;
isuvar = isfield(data, 'label') && sum(strcmp(dimtok,'chan'))==1;
isbvar = isfield(data, 'labelcmb');
if (isfull||isuvar) && isbvar
error('data representaion is ambiguous');
end
if ~isfull && ~isbvar && ~isuvar
error('data representation is unsupported');
end
%keeprpt = strcmp(cfg.keeptrials, 'yes');
%keeptap = strcmp(cfg.keeptapers, 'yes');
%dojack = strcmp(cfg.jackknife, 'yes');
%dozscore = strcmp(cfg.zscore, 'yes');
%if ~keeptap, error('not keeping tapers is not possible yet'); end
%if dojack && keeprpt, error('you cannot simultaneously keep trials and do jackknifing'); end
nfoi = length(cfg.foi);
if isfield(data, 'time')
ntoi = numel(data.time);
else
ntoi = 1;
end
if isfull || isuvar
cfg.channel = ft_channelselection('all', data.label);
%cfg.channel = ft_channelselection(cfg.channel, data.label);
chanindx = match_str(data.label, cfg.channel);
nchan = length(chanindx);
label = data.label(chanindx);
nlag = size(data.coeffs,3); %change in due course
%---allocate memory
h = complex(zeros(nchan, nchan, nfoi, ntoi), zeros(nchan, nchan, nfoi, ntoi));
a = complex(zeros(nchan, nchan, nfoi, ntoi), zeros(nchan, nchan, nfoi, ntoi));
crsspctrm = complex(zeros(nchan, nchan, nfoi, ntoi), zeros(nchan, nchan, nfoi, ntoi));
elseif isbvar
ncmb = size(data.labelcmb,1)./4;
nlag = size(data.coeffs,2);
%---allocate memory
h = complex(zeros(ncmb*4, nfoi, ntoi), zeros(ncmb*4, nfoi, ntoi));
a = complex(zeros(ncmb*4, nfoi, ntoi), zeros(ncmb*4, nfoi, ntoi));
crsspctrm = complex(zeros(ncmb*4, nfoi, ntoi), zeros(ncmb*4, nfoi, ntoi));
end
%FIXME build in repetitions
%---loop over the tois
ft_progress('init', cfg.feedback, 'computing MAR-model based TFR');
for j = 1:ntoi
ft_progress(j/ntoi, 'processing timewindow %d from %d\n', j, ntoi);
if isfull
%---compute transfer function
ar = reshape(data.coeffs(:,:,:,j), [nchan nchan*nlag]);
[h(:,:,:,j), a(:,:,:,j)] = ar2h(ar, cfg.foi, data.fsampleorig);
%---compute cross-spectra
nc = data.noisecov(:,:,j);
for k = 1:nfoi
tmph = h(:,:,k,j);
crsspctrm(:,:,k,j) = tmph*nc*tmph';
end
elseif isuvar
%---compute transfer function
for m = 1:nchan
ar = reshape(data.coeffs(m,:,j), [1 nlag]);
[h(m,m,:,j), a(m,m,:,j)] = ar2h(ar, cfg.foi, data.fsampleorig);
%---compute cross-spectra
nc = data.noisecov(m,j);
for k = 1:nfoi
tmph = h(m,m,k,j);
crsspctrm(m,m,k,j) = tmph*nc*tmph';
end
end
elseif isbvar
for kk = 1:ncmb
%---compute transfer function
ar = reshape(data.coeffs((kk-1)*4+(1:4),:,:,j), [2 2*nlag]);
[tmph,tmpa] = ar2h(ar, cfg.foi, data.fsampleorig);
h((kk-1)*4+(1:4),:,:) = reshape(tmph, [4 nfoi ntoi]);
a((kk-1)*4+(1:4),:,:) = reshape(tmpa, [4 nfoi ntoi]);
%---compute cross-spectra
nc = reshape(data.noisecov((kk-1)*4+(1:4),j), [2 2]);
for k = 1:nfoi
crsspctrm((kk-1)*4+(1:4),k,j) = reshape(tmph(:,:,k)*nc*tmph(:,:,k)', [4 1]);
end
end
end
end
ft_progress('close');
%---create output-structure
freq = [];
freq.freq = cfg.foi;
%freq.cumtapcnt= ones(ntrl, 1)*ntap;
freq.transfer = h;
%freq.itransfer = a;
freq.noisecov = data.noisecov;
freq.crsspctrm = crsspctrm;
if isfield(data, 'dof'),
freq.dof = data.dof;
end
if isfull
freq.label = label;
if ntoi>1
freq.time = data.time;
freq.dimord = 'chan_chan_freq_time';
else
freq.dimord = 'chan_chan_freq';
end
elseif isbvar
freq.labelcmb = data.labelcmb;
if ntoi>1
freq.time = data.time;
freq.dimord = 'chancmb_freq_time';
else
freq.dimord = 'chancmb_freq';
end
end
% do the general cleanup and bookkeeping at the end of the function
ft_postamble debug
ft_postamble trackconfig
ft_postamble previous data
ft_postamble provenance freq
ft_postamble history freq
ft_postamble savevar freq
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SUBFUNCTION to compute transfer-function from ar-parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [h, zar] = ar2h(ar, foi, fsample)
nchan = size(ar,1);
ncmb = nchan*nchan;
nfoi = length(foi);
%---z-transform frequency axis
zfoi = exp(-2.*pi.*1i.*(foi./fsample));
%---reorganize the ar-parameters
ar = reshape(ar, [ncmb size(ar,2)./nchan]);
ar = fliplr([reshape(eye(nchan), [ncmb 1]) -ar]);
zar = complex(zeros(ncmb, nfoi), zeros(ncmb, nfoi));
for k = 1:ncmb
zar(k,:) = polyval(ar(k,:),zfoi);
end
zar = reshape(zar, [nchan nchan nfoi]);
h = zeros(size(zar));
for k = 1:nfoi
h(:,:,k) = inv(zar(:,:,k));
end
h = sqrt(2).*h; %account for the negative frequencies, normalization necessary for
%comparison with non-parametric (fft based) results in fieldtrip
%FIXME probably the normalization for the zero Hz bin is incorrect
zar = zar./sqrt(2);