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ft_freqanalysis.m
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function [freq] = ft_freqanalysis(cfg, data)
% FT_FREQANALYSIS performs frequency and time-frequency analysis
% on time series data over multiple trials
%
% Use as
% [freq] = ft_freqanalysis(cfg, data)
%
% The input data should be organised in a structure as obtained from
% the FT_PREPROCESSING or the FT_MVARANALYSIS function. The configuration
% depends on the type of computation that you want to perform.
%
% The configuration should contain:
% cfg.method = different methods of calculating the spectra
% 'mtmfft', analyses an entire spectrum for the entire data
% length, implements multitaper frequency transformation.
% 'mtmconvol', implements multitaper time-frequency
% transformation based on multiplication in the
% frequency domain.
% 'wavelet', implements wavelet time frequency
% transformation (using Morlet wavelets) based on
% multiplication in the frequency domain.
% 'tfr', implements wavelet time frequency
% transformation (using Morlet wavelets) based on
% convolution in the time domain.
% 'mvar', does a fourier transform on the coefficients
% of an estimated multivariate autoregressive model,
% obtained with FT_MVARANALYSIS. In this case, the
% output will contain a spectral transfer matrix,
% the cross-spectral density matrix, and the
% covariance matrix of the innovatio noise.
% 'superlet', combines Morlet-wavelet based
% decompositions, see below.
% 'irasa', implements Irregular-Resampling Auto-Spectral
% Analysis (IRASA), to separate the fractal components
% from the periodicities in the signal.
% cfg.output = 'pow' return the power-spectra
% 'powandcsd' return the power and the cross-spectra
% 'fourier' return the complex Fourier-spectra
% 'fractal' (when cfg.method = 'irasa'), return the
% fractal component of the spectrum (1/f)
% 'original' (when cfg.method = 'irasa'), return the
% full power spectrum
% 'fooof' returns a smooth power-spectrum,
% based on a parametrization of a mixture of aperiodic and periodic
% components (only works with cfg.method = 'mtmfft')
% 'fooof_aperiodic' returns a power-spectrum with the
% fooof based estimate of the aperiodic part of the signal.
% 'fooof_peaks' returns a power-spectrum with the fooof
% based estimate of the aperiodic signal removed,
% it's expressed as
% 10^(log10(fooof)-log10(fooof_aperiodic))
% cfg.channel = Nx1 cell-array with selection of channels (default = 'all'),
% see FT_CHANNELSELECTION for details
% cfg.channelcmb = Mx2 cell-array with selection of channel pairs (default = {'all' 'all'}),
% see FT_CHANNELCOMBINATION for details
% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
% cfg.keeptrials = 'yes' or 'no', return individual trials or average (default = 'no')
% cfg.keeptapers = 'yes' or 'no', return individual tapers or average (default = 'no')
% cfg.pad = number, 'nextpow2', or 'maxperlen' (default), length
% in seconds to which the data can be padded out. The
% padding will determine your spectral resolution. If you
% want to compare spectra from data pieces of different
% lengths, you should use the same cfg.pad for both, in
% order to spectrally interpolate them to the same
% spectral resolution. The new option 'nextpow2' rounds
% the maximum trial length up to the next power of 2. By
% using that amount of padding, the FFT can be computed
% more efficiently in case 'maxperlen' has a large prime
% factor sum.
% cfg.padtype = string, type of padding (default 'zero', see
% ft_preproc_padding)
% cfg.polyremoval = number (default = 0), specifying the order of the
% polynome which is fitted and subtracted from the time
% domain data prior to the spectral analysis. For
% example, a value of 1 corresponds to a linear trend.
% The default is a mean subtraction, thus a value of 0.
% If no removal is requested, specify -1.
% see FT_PREPROC_POLYREMOVAL for details
%
%
% METHOD SPECIFIC OPTIONS AND DESCRIPTIONS
%
% MTMFFT performs frequency analysis on any time series trial data using a
% conventional single taper (e.g. Hanning) or using the multiple tapers based on
% discrete prolate spheroidal sequences (DPSS), also known as the Slepian
% sequence.
% cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'dpss')
% For cfg.output='powandcsd', you should specify the channel combinations
% between which to compute the cross-spectra as cfg.channelcmb. Otherwise
% you should specify only the channels in cfg.channel.
% cfg.foilim = [begin end], frequency band of interest
% OR
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.tapsmofrq = number, the amount of spectral smoothing through
% multi-tapering. Note that 4 Hz smoothing means
% plus-minus 4 Hz, i.e. a 8 Hz smoothing box.
%
% MTMCONVOL performs time-frequency analysis on any time series trial data using
% the 'multitaper method' (MTM) based on Slepian sequences as tapers.
% Alternatively, you can use conventional tapers (e.g. Hanning).
% cfg.tapsmofrq = vector 1 x numfoi, the amount of spectral smoothing
% through multi-tapering. Note that 4 Hz smoothing means
% plus-minus 4 Hz, i.e. a 8 Hz smoothing box.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'dpss')
% For cfg.output='powandcsd', you should specify the channel combinations
% between which to compute the cross-spectra as cfg.channelcmb. Otherwise
% you should specify only the channels in cfg.channel.
% cfg.t_ftimwin = vector 1 x numfoi, length of time window (in seconds)
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds), or a string
% such as '50%' or 'all' (default). Both string options
% use all timepoints available in the data, but 'all'
% centers a spectral estimate on each sample, whereas
% the percentage specifies the degree of overlap between
% the shortest time windows from cfg.t_ftimwin.
%
% WAVELET performs time-frequency analysis on any time series trial data using the
% 'wavelet method' based on Morlet wavelets. Using mulitplication in the frequency
% domain instead of convolution in the time domain.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds)
% cfg.width = 'width', or number of cycles, of the wavelet (default = 7)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be chosen >= 3; (default = 3)
%
% The standard deviation in the frequency domain (sf) at frequency f0 is
% defined as: sf = f0/width
% The standard deviation in the temporal domain (st) at frequency f0 is
% defined as: st = 1/(2*pi*sf)
%
% SUPERLET performs time-frequency analysis on any time series trial data using the
% 'superlet method' based on a frequency-wise combination of Morlet wavelets of varying cycle
% widths (see Moca et al. 2019, https://doi.org/10.1101/583732).
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds)
% cfg.width = 'width', or number of cycles, of the base wavelet (default = 3)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be chosen >= 3; (default = 3)
% cfg.combine = 'additive', 'multiplicative' (default = 'additive')
% determines if cycle numbers of wavelets comprising a superlet
% are chosen additively or multiplicatively
% cfg.order = vector 1 x numfoi, superlet order, i.e. number of combined
% wavelets, for individual frequencies of interest.
%
% The standard deviation in the frequency domain (sf) at frequency f0 is
% defined as: sf = f0/width
% The standard deviation in the temporal domain (st) at frequency f0 is
% defined as: st = 1/(2*pi*sf)
%
% HILBERT performs time-frequency analysis on any time series data using a frequency specific
% bandpass filter, followed by the Hilbert transform.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.toi = vector 1 x numtoi, the time points for which the estimates will be returned (in seconds)
% cfg.width = scalar, or vector (default: 1), specifying the half bandwidth of the filter;
% cfg.edgartnan = 'no' (default) or 'yes', replace filter edges with nans, works only for finite impulse response (FIR) filters, and
% requires a user specification of the filter order
%
% For the bandpass filtering the following options can be specified, the default values are as in FT_PREPROC_BANDPASSFILTER, for more
% information see the help of FT_PREPROCESSING
% cfg.bpfilttype
% cfg.bpfiltord = (optional) scalar, or vector 1 x numfoi;
% cfg.bpfiltdir
% cfg.bpinstabilityfix
% cfg.bpfiltdf
% cfg.bpfiltwintype
% cfg.bpfiltdev
%
% TFR performs time-frequency analysis on any time series trial data using the
% 'wavelet method' based on Morlet wavelets. Using convolution in the time domain
% instead of multiplication in the frequency domain.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.width = 'width', or number of cycles, of the wavelet (default = 7)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be choosen >= 3; (default = 3)
%
%
% 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_FREQSTATISTICS, FT_FREQDESCRIPTIVES, FT_CONNECTIVITYANALYSIS
% Guidelines for use in an analysis pipeline:
% after FT_FREQANALYSIS you will have frequency or time-frequency
% representations (TFRs) of the data, represented as power-spectra,
% power and cross-spectra, or complex fourier-spectra, either for individual
% trials or an average over trials.
% This usually serves as input for one of the following functions:
% * FT_FREQDESCRIPTIVES to compute descriptive univariate statistics
% * FT_FREQSTATISTICS to perform parametric or non-parametric statistical tests
% * FT_FREQBASELINE to perform baseline normalization of the spectra
% * FT_FREQGRANDAVERAGE to compute the average spectra over multiple subjects or datasets
% * FT_CONNECTIVITYANALYSIS to compute various measures of connectivity
% Furthermore, the data can be visualised using the various plotting
% functions, including:
% * FT_SINGLEPLOTTFR to plot the TFR of a single channel or the average over multiple channels
% * FT_TOPOPLOTTFR to plot the topographic distribution over the head
% * FT_MULTIPLOTTFR to plot TFRs in a topographical layout
% Undocumented local options:
% cfg.method = 'hilbert'. Keeping this as undocumented as it does not make
% sense to use in ft_freqanalysis unless the user is doing his
% own filter-padding to remove edge-artifacts
% cfg.correctt_ftimwin (set to yes to try to determine new t_ftimwins based
% on correct cfg.foi)
% Copyright (C) 2003-2006, F.C. Donders Centre, Pascal Fries
% Copyright (C) 2004-2006, F.C. Donders Centre, Markus Siegel
% Copyright (C) 2007-2012, DCCN, The FieldTrip team
%
% 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/>.
% 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
% check if the input data is valid for this function
data = ft_checkdata(data, 'datatype', {'raw', 'raw+comp', 'mvar'}, 'feedback', 'yes', 'hassampleinfo', 'yes');
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'forbidden', {'channels', 'trial'}); % prevent accidental typos, see issue 1729
cfg = ft_checkconfig(cfg, 'renamed', {'label', 'channel'});
cfg = ft_checkconfig(cfg, 'renamed', {'sgn', 'channel'});
cfg = ft_checkconfig(cfg, 'renamed', {'labelcmb', 'channelcmb'});
cfg = ft_checkconfig(cfg, 'renamed', {'sgncmb', 'channelcmb'});
cfg = ft_checkconfig(cfg, 'required', {'method'});
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'fft', 'mtmfft'});
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'convol', 'mtmconvol'});
cfg = ft_checkconfig(cfg, 'forbidden', {'latency'}); % see bug 1376 and 1076
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'wltconvol', 'wavelet'});
% set the defaults
cfg.feedback = ft_getopt(cfg, 'feedback', 'text');
cfg.inputlock = ft_getopt(cfg, 'inputlock', []); % this can be used as mutex when doing distributed computation
cfg.outputlock = ft_getopt(cfg, 'outputlock', []); % this can be used as mutex when doing distributed computation
cfg.trials = ft_getopt(cfg, 'trials', 'all', 1);
cfg.channel = ft_getopt(cfg, 'channel', 'all');
% select channels and trials of interest, by default this will select all channels and trials
tmpcfg = keepfields(cfg, {'trials', 'channel', 'tolerance', 'showcallinfo', 'trackcallinfo', 'trackconfig', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo'});
data = ft_selectdata(tmpcfg, data);
% restore the provenance information
[cfg, data] = rollback_provenance(cfg, data);
% some proper error handling
if isfield(data, 'trial') && numel(data.trial)==0
ft_error('no trials were selected'); % this does not apply for MVAR data
end
if numel(data.label)==0
ft_error('no channels were selected');
end
% switch over method and do some of the method specfic checks and defaulting
switch cfg.method
case 'mtmconvol'
cfg.taper = ft_getopt(cfg, 'taper', 'dpss');
if isequal(cfg.taper, 'dpss') && ~isfield(cfg, 'tapsmofrq')
ft_error('you must specify a smoothing parameter with taper = dpss');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample+100*eps(data.fsample))/2)
% add a small number to allow for numeric tolerance issues
ft_error('frequencies in cfg.foi are above Nyquist')
end
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
end
cfg = ft_checkconfig(cfg, 'required', {'toi', 't_ftimwin'});
if ischar(cfg.toi)
begtim = min(cellfun(@min,data.time));
endtim = max(cellfun(@max,data.time));
if strcmp(cfg.toi, 'all') % each data sample gets a time window
cfg.toi = linspace(begtim, endtim, round((endtim-begtim) ./ ...
mean(diff(data.time{1})))+1);
elseif strcmp(cfg.toi(end), '%') % percent overlap between smallest time windows
overlap = str2double(cfg.toi(1:(end-1)))/100;
cfg.toi = linspace(begtim, endtim, round((endtim-begtim) ./ ...
(overlap * min(cfg.t_ftimwin))) + 1);
else
ft_error('cfg.toi should be either a numeric vector or a string: can be ''all'' or a percentage (e.g., ''50%'')');
end
end
case 'mtmfft'
cfg.taper = ft_getopt(cfg, 'taper', 'dpss');
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample/2))
ft_error('frequencies in cfg.foi are above Nyquist')
end
end
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
case 'irasa'
cfg.taper = ft_getopt(cfg, 'taper', 'hanning');
cfg.output = ft_getopt(cfg, 'output', 'fractal');
cfg.pad = ft_getopt(cfg, 'pad', 'nextpow2');
if ~isequal(cfg.taper, 'hanning')
ft_error('the irasa method supports hanning tapers only');
end
if ~isequal(cfg.pad, 'nextpow2')
ft_warning('consider using cfg.pad=''nextpow2'' for the irasa method');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample/2))
ft_error('frequencies in cfg.foi are above Nyquist')
end
end
case 'wavelet'
cfg.width = ft_getopt(cfg, 'width', 7);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
case 'superlet'
% reorganize the cfg, a nested cfg is not consistent with the othe methods
cfg = ft_checkconfig(cfg, 'createtopcfg', 'superlet');
cfg = removefields(cfg, 'superlet');
cfg = ft_checkconfig(cfg, 'renamed', {'basewidth', 'width'});
cfg.width = ft_getopt(cfg, 'width', 3);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
cfg.combine = ft_getopt(cfg, 'combine', 'additive');
cfg.order = ft_getopt(cfg, 'order', ones(1, numel(cfg.foi)));
if numel(cfg.order) == 1
cfg.order = cfg.order.*length(cfg.foi);
elseif numel(cfg.order) ~= numel(cfg.foi)
ft_error('cfg.foi must have the same number of elements as cfg.foi, or must be a scalar');
end
case 'tfr'
cfg = ft_checkconfig(cfg, 'renamed', {'waveletwidth', 'width'});
cfg = ft_checkconfig(cfg, 'unused', {'downsample'});
cfg.width = ft_getopt(cfg, 'width', 7);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
case 'hilbert'
ft_warning('method = hilbert may require user action to deal with filtering-artifacts')
cfg = ft_checkconfig(cfg, 'renamed', {'filttype', 'bpfilttype'});
cfg = ft_checkconfig(cfg, 'renamed', {'filtorder', 'bpfiltord'});
cfg = ft_checkconfig(cfg, 'renamed', {'filtdir', 'bpfiltdir'});
cfg.bpfilttype = ft_getopt(cfg, 'bpfilttype');
cfg.bpfiltord = ft_getopt(cfg, 'bpfiltord');
cfg.bpfiltdir = ft_getopt(cfg, 'bpfiltdir');
cfg.bpinstabilityfix = ft_getopt(cfg, 'bpinstabilityfix');
cfg.bpfiltdf = ft_getopt(cfg, 'bpfiltdf');
cfg.bpfiltwintype = ft_getopt(cfg, 'bpfiltwintype');
cfg.bpfiltdev = ft_getopt(cfg, 'bpfiltdev');
cfg.width = ft_getopt(cfg, 'width', 1);
cfg.edgeartnan = ft_getopt(cfg, 'edgeartnan', 'no');
fn = fieldnames(cfg);
bpfiltoptions = ft_cfg2keyval(keepfields(cfg, fn(startsWith(fn, 'bp'))));
case 'mvar'
if isfield(cfg, 'inputfile')
freq = ft_freqanalysis_mvar(cfg);
else
freq = ft_freqanalysis_mvar(cfg, data);
end
return
case 'neuvar'
cfg.order = ft_getopt(cfg, 'order', 1); % order of differentiation
otherwise
ft_error('specified cfg.method is not supported')
end
% set all the defaults
cfg.pad = ft_getopt(cfg, 'pad', []);
if isempty(cfg.pad)
ft_notice('Default cfg.pad=''maxperlen'' can run slowly. Consider using cfg.pad=''nextpow2'' for more efficient FFT computation.')
cfg.pad = 'maxperlen';
end
cfg.padtype = ft_getopt(cfg, 'padtype', 'zero');
cfg.output = ft_getopt(cfg, 'output', 'pow'); % the default for irasa is set earlier
cfg.calcdof = ft_getopt(cfg, 'calcdof', 'no');
cfg.channel = ft_getopt(cfg, 'channel', 'all');
cfg.precision = ft_getopt(cfg, 'precision', 'double');
cfg.foi = ft_getopt(cfg, 'foi', []);
cfg.foilim = ft_getopt(cfg, 'foilim', []);
cfg.correctt_ftimwin = ft_getopt(cfg, 'correctt_ftimwin', 'no');
cfg.polyremoval = ft_getopt(cfg, 'polyremoval', 0);
% keeptrials and keeptapers should be conditional on cfg.output,
% cfg.output = 'fourier' should always output tapers
if strcmp(cfg.output, 'fourier')
cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'yes');
cfg.keeptapers = ft_getopt(cfg, 'keeptapers', 'yes');
if strcmp(cfg.keeptrials, 'no') || strcmp(cfg.keeptapers, 'no')
ft_error('cfg.output = ''fourier'' requires cfg.keeptrials = ''yes'' and cfg.keeptapers = ''yes''');
end
else
cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'no');
cfg.keeptapers = ft_getopt(cfg, 'keeptapers', 'no');
end
% set flags for keeping trials and/or tapers
if strcmp(cfg.keeptrials, 'no') && strcmp(cfg.keeptapers, 'no')
keeprpt = 1;
elseif strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'no')
keeprpt = 2;
elseif strcmp(cfg.keeptrials, 'no') && strcmp(cfg.keeptapers, 'yes')
ft_error('There is currently no support for keeping tapers WITHOUT KEEPING TRIALS.');
elseif strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'yes')
keeprpt = 4;
end
if strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'yes')
if ~strcmp(cfg.output, 'fourier')
ft_error('Keeping trials AND tapers is only possible with fourier as the output.');
end
end
% Set flags for output
if ismember(cfg.output, {'pow','fractal','original','fooof','fooof_peaks','fooof_aperiodic'})
powflg = 1;
csdflg = 0;
fftflg = 0;
elseif strcmp(cfg.output, 'powandcsd')
powflg = 1;
csdflg = 1;
fftflg = 0;
elseif strcmp(cfg.output, 'fourier')
powflg = 0;
csdflg = 0;
fftflg = 1;
else
ft_error('Unrecognized output required');
end
% Check whether the keeptrials is correct for fooof
if startsWith(cfg.output, 'fooof')
% ensure that Brainstorm is on the path: if the user uses their own
% version of the code, assume that the paths are correctly set
if keeprpt~=1
ft_error('Keeping trials and/or tapers is not allowed when using fooof');
end
if ~isequal(cfg.method, 'mtmfft')
ft_error('Fooof is only supported with cfg.method = ''mtmfft''');
end
end
% prepare channel(cmb)
if ~isfield(cfg, 'channelcmb') && csdflg
%set the default for the channelcombination
cfg.channelcmb = {'all' 'all'};
elseif isfield(cfg, 'channelcmb') && ~csdflg
% no cross-spectrum needs to be computed, hence remove the combinations from cfg
cfg = rmfield(cfg, 'channelcmb');
end
if isfield(cfg, 'channelcmb')
% the channels in the data are already the subset according to cfg.channel
cfg.channelcmb = ft_channelcombination(cfg.channelcmb, data.label);
end
% determine the corresponding indices of all channels
chanind = match_str(data.label, cfg.channel);
nchan = numel(chanind);
if csdflg
assert(nchan>1, 'CSD output requires multiple channels');
% determine the corresponding indices of all channel combinations
[dummy,chancmbind(:,1)] = match_str(cfg.channelcmb(:,1), data.label);
[dummy,chancmbind(:,2)] = match_str(cfg.channelcmb(:,2), data.label);
nchancmb = size(chancmbind,1);
chanind = unique([chanind(:); chancmbind(:)]);
nchan = length(chanind);
cutdatindcmb = zeros(size(chancmbind));
for ichan = 1:nchan
cutdatindcmb(chancmbind == chanind(ichan)) = ichan;
end
end
% determine trial characteristics
ntrials = numel(data.trial);
trllength = zeros(1, ntrials);
for itrial = 1:ntrials
trllength(itrial) = size(data.trial{itrial}, 2);
end
if strcmp(cfg.pad, 'maxperlen')
padding = max(trllength);
cfg.pad = padding/data.fsample;
elseif strcmp(cfg.pad, 'nextpow2')
padding = 2^nextpow2(max(trllength));
cfg.pad = padding/data.fsample;
else
padding = cfg.pad*data.fsample;
if padding<max(trllength)
ft_error('the specified padding is too short');
end
end
% correct foi and implement foilim 'backwards compatibility'
if ~isempty(cfg.foi) && ~isempty(cfg.foilim)
ft_error('use either cfg.foi or cfg.foilim')
elseif ~isempty(cfg.foilim)
% get the full foi in the current foilim and set it too be used as foilim
fboilim = round(cfg.foilim .* cfg.pad) + 1;
fboi = fboilim(1):1:fboilim(2);
cfg.foi = (fboi-1) ./ cfg.pad;
else
% correct foi if foilim was empty and try to correct t_ftimwin (by detecting whether there is a constant factor between foi and t_ftimwin: cyclenum)
oldfoi = cfg.foi;
fboi = round(cfg.foi .* cfg.pad) + 1;
cfg.foi = (fboi-1) ./ cfg.pad; % boi - 1 because 0 Hz is included in fourier output
if strcmp(cfg.correctt_ftimwin, 'yes')
cyclenum = oldfoi .* cfg.t_ftimwin;
cfg.t_ftimwin = cyclenum ./ cfg.foi;
end
end
% tapsmofrq compatibility between functions (make it into a vector if it's not)
if isfield(cfg, 'tapsmofrq')
if strcmp(cfg.method, 'mtmconvol') && length(cfg.tapsmofrq) == 1 && length(cfg.foi) ~= 1
cfg.tapsmofrq = ones(length(cfg.foi),1) * cfg.tapsmofrq;
elseif strcmp(cfg.method, 'mtmfft') && length(cfg.tapsmofrq) ~= 1
ft_warning('cfg.tapsmofrq should be a single number when cfg.method = mtmfft, now using only the first element')
cfg.tapsmofrq = cfg.tapsmofrq(1);
end
end
% options that don't change over trials
if isfield(cfg, 'tapsmofrq')
options = {'pad', cfg.pad, 'padtype', cfg.padtype, 'freqoi', cfg.foi, 'tapsmofrq', cfg.tapsmofrq, 'polyorder', cfg.polyremoval, 'output', cfg.output};
else
options = {'pad', cfg.pad, 'padtype', cfg.padtype, 'freqoi', cfg.foi, 'polyorder', cfg.polyremoval, 'output', cfg.output};
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Main loop over trials, inside fourierspectra are obtained and transformed into the appropriate outputs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this is done on trial basis to save memory
ft_progress('init', cfg.feedback, 'processing trials');
trlcnt = []; % only some methods need this variable, but it needs to be defined outside the trial loop
for itrial = 1:ntrials
fbopt.i = itrial;
fbopt.n = ntrials;
dat = data.trial{itrial}; % chansel has already been performed
time = data.time{itrial};
clear spectrum % in case of very large trials, this lowers peak mem usage a bit
% Perform specest call and set some specifics
switch cfg.method
case 'mtmconvol'
[spectrum_mtmconvol,ntaper,foi,toi] = ft_specest_mtmconvol(dat, time, 'timeoi', cfg.toi, 'timwin', cfg.t_ftimwin, 'taper', ...
cfg.taper, options{:}, 'dimord', 'chan_time_freqtap', 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% error for different number of tapers per trial
if (keeprpt == 4) && any(ntaper(:) ~= ntaper(1))
ft_error('currently you can only keep trials AND tapers, when using the number of tapers per frequency is equal across frequency')
end
% create tapfreqind for later indexing
freqtapind = [];
tempntaper = [0; cumsum(ntaper(:))];
for iindfoi = 1:numel(foi)
freqtapind{iindfoi} = tempntaper(iindfoi)+1:tempntaper(iindfoi+1);
end
case 'mtmfft'
[spectrum,ntaper,foi] = ft_specest_mtmfft(dat, time, 'taper', cfg.taper, options{:}, 'feedback', fbopt);
hastime = false;
case 'irasa'
[spectrum,ntaper,foi] = ft_specest_irasa(dat, time, options{:}, 'feedback', fbopt);
hastime = false;
case 'wavelet'
[spectrum,foi,toi] = ft_specest_wavelet(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, 'gwidth', cfg.gwidth, options{:}, 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'superlet'
% calculate number of wavelets and respective cycle width dependent on superlet order
% equivalent one-liners:
% multiplicative: cycles = arrayfun(@(order) arrayfun(@(wl_num) cfg.width*wl_num, 1:order), cfg.order,'uni',0)
% additive: cycles = arrayfun(@(order) arrayfun(@(wl_num) cfg.width+wl_num-1, 1:order), cfg.order,'uni',0)
order_int = ceil(cfg.order);
cycles = cell(length(cfg.foi), 1);
for i_f = 1:length(cfg.foi)
frq_cyc = NaN(1, order_int(i_f));
if strcmp(cfg.combine, 'multiplicative')
for i_wl = 1:order_int(i_f)
frq_cyc(i_wl) = cfg.width * i_wl;
end
elseif strcmp(cfg.combine, 'additive')
for i_wl = 1:order_int(i_f)
frq_cyc(i_wl) = cfg.width + i_wl - 1;
end
end
cycles{i_f} = frq_cyc;
end
% compute superlets
spectrum = NaN(nchan, length(cfg.foi), length(cfg.toi));
% index of 'freqoi' value in 'options'
idx_freqoi = find(ismember(options(1:2:end), 'freqoi'))*2;
foi = options{idx_freqoi};
for i_f = 1:length(cfg.foi)
% collext individual wavelets' responses per frequency
spec_f = NaN(order_int(i_f), nchan, length(cfg.toi));
opt = options;
opt{idx_freqoi} = cfg.foi(i_f);
% compute responses for individual wavelets
for i_wl = 1:order_int(i_f)
[spec_f(i_wl, :, :), dum, toi] = ft_specest_wavelet(dat, time, 'timeoi', cfg.toi, 'width', cycles{i_f}(i_wl), 'gwidth', cfg.gwidth, opt{:}, 'feedback', fbopt);
end
if floor(cfg.order(i_f)) ~= order_int(i_f)
spec_f(i_wl, :, :) = spec_f(i_wl, :, :) .^ rem(cfg.order(i_f), 1);
end
% geometric mean across individual wavelets
spectrum(:, i_f, :) = prod(spec_f, 1) .^ (1 / cfg.order(i_f));
end
clear spec_f
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'tfr'
[spectrum,foi,toi] = ft_specest_tfr(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, 'gwidth', cfg.gwidth,options{:}, 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'hilbert'
[spectrum,foi,toi] = ft_specest_hilbert(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, bpfiltoptions{:}, options{:}, 'feedback', fbopt, 'edgeartnan', cfg.edgeartnan);
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'neuvar'
[spectrum,foi] = ft_specest_neuvar(dat, time, options{:}, 'feedback', fbopt);
hastime = false;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
end % switch
% Set n's
maxtap = max(ntaper);
nfoi = numel(foi);
if hastime
ntoi = numel(toi);
else
ntoi = 1; % this makes the same code compatible for hastime = false, as time is always the last dimension, and if singleton will disappear
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Memory allocation
if strcmp(cfg.method, 'mtmfft') && strcmp(cfg.taper, 'dpss')
% memory allocation for mtmfft is slightly different because of the possiblity of
% variable number of tapers over trials (when using dpss), the below exception is
% made so memory can still be allocated fully (see bug #1025
trllength = cellfun(@numel,data.time);
% determine number of tapers per trial
ntaptrl = sum(floor((2 .* (trllength./data.fsample) .* cfg.tapsmofrq) - 1)); % I floored it for now, because I don't know whether this formula is accurate in all cases, by flooring the memory allocated
% will most likely be less than it should be, but this would still have the same effect of 'not-crashing-matlabs'.
% I do have the feeling a round would be 100% accurate, but atm I cannot check this in Percival and Walden
% - roevdmei
else
ntaptrl = ntrials .* maxtap; % the way it used to be in all cases (before bug #1025)
end
% by default, everything is has the time dimension, if not, some specifics are performed
if itrial == 1
% allocate memory to output variables
if keeprpt == 1 % cfg.keeptrials, 'no' && cfg.keeptapers, 'no'
if powflg, powspctrm = zeros(nchan,nfoi,ntoi,cfg.precision); end
if csdflg, crsspctrm = complex(zeros(nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(zeros(nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'chan_freq_time';
elseif keeprpt == 2 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'no'
if powflg, powspctrm = nan(ntrials,nchan,nfoi,ntoi,cfg.precision); end
if csdflg, crsspctrm = complex(nan(ntrials,nchancmb,nfoi,ntoi,cfg.precision),nan(ntrials,nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(nan(ntrials,nchan,nfoi,ntoi,cfg.precision),nan(ntrials,nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'rpt_chan_freq_time';
elseif keeprpt == 4 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'yes'
if powflg, powspctrm = zeros(ntaptrl,nchan,nfoi,ntoi,cfg.precision); end %
if csdflg, crsspctrm = complex(zeros(ntaptrl,nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(zeros(ntaptrl,nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'rpttap_chan_freq_time';
end
if ~hastime
dimord = dimord(1:end-5); % cut _time
end
% prepare calcdof
if strcmp(cfg.calcdof, 'yes')
if hastime
dof = zeros(nfoi,ntoi);
%dof = zeros(ntrials,nfoi,ntoi);
else
dof = zeros(nfoi,1);
%dof = zeros(ntrials,nfoi);
end
end
% prepare cumtapcnt
switch cfg.method %% IMPORTANT, SHOULD WE KEEP THIS SPLIT UP PER METHOD OR GO FOR A GENERAL SOLUTION NOW THAT WE HAVE SPECEST
case 'mtmconvol'
cumtapcnt = zeros(ntrials,nfoi);
case 'mtmfft'
cumtapcnt = zeros(ntrials,1);
end
end % itrial==1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Create output
if keeprpt~=4
% mtmconvol is a special case and needs special processing
if strcmp(cfg.method, 'mtmconvol')
foiind = ones(1,nfoi);
else
% by using this vector below for indexing, the below code does not need to be duplicated for mtmconvol
foiind = 1:nfoi;
end
for ifoi = 1:nfoi
if strcmp(cfg.method, 'mtmconvol')
spectrum = reshape(permute(spectrum_mtmconvol(:,:,freqtapind{ifoi}),[3 1 2]),[ntaper(ifoi) nchan 1 ntoi]);
end
% set ingredients for below
if ~hastime
acttboi = 1;
nacttboi = 1;
else
acttboi = ~all(isnan(spectrum(1,:,foiind(ifoi),:)), 2); % check over all channels, some channels might contain a NaN
acttboi = reshape(acttboi, [1 ntoi]); % size(spectrum) = [? nchan nfoi ntoi]
nacttboi = sum(acttboi);
end
acttap = logical([ones(ntaper(ifoi),1);zeros(size(spectrum,1)-ntaper(ifoi),1)]);
if powflg
if strcmp(cfg.method, 'irasa') % ft_specest_irasa outputs power and not amplitude
powdum = spectrum(acttap,:,foiind(ifoi),acttboi);
else
powdum = abs(spectrum(acttap,:,foiind(ifoi),acttboi)) .^2;
end
% sinetaper scaling is disabled, because it is not consistent with the other
% tapers. if scaling is required, please specify cfg.taper =
% 'sine_old'
% if isfield(cfg, 'taper') && strcmp(cfg.taper, 'sine')
% %sinetapscale = zeros(ntaper(ifoi),nfoi); % assumes fixed number of tapers
% sinetapscale = zeros(ntaper(ifoi),1); % assumes fixed number of tapers
% for isinetap = 1:ntaper(ifoi) % assumes fixed number of tapers
% sinetapscale(isinetap,:) = (1 - (((isinetap - 1) ./ ntaper(ifoi)) .^ 2));
% end
% sinetapscale = reshape(repmat(sinetapscale,[1 1 nchan ntoi]),[ntaper(ifoi) nchan 1 ntoi]);
% powdum = powdum .* sinetapscale;
% end
end
if fftflg
fourierdum = spectrum(acttap,:,foiind(ifoi),acttboi);
end
if csdflg
csddum = spectrum(acttap,cutdatindcmb(:,1),foiind(ifoi),acttboi) .* conj(spectrum(acttap,cutdatindcmb(:,2),foiind(ifoi),acttboi));
end
% switch between keep's
switch keeprpt
case 1 % cfg.keeptrials, 'no' && cfg.keeptapers, 'no'
if ~isempty(trlcnt)
trlcnt(1, ifoi, :) = trlcnt(1, ifoi, :) + shiftdim(double(acttboi(:)'),-1);
end
if powflg
powspctrm(:,ifoi,acttboi) = powspctrm(:,ifoi,acttboi) + (reshape(mean(powdum,1),[nchan 1 nacttboi]) ./ ntrials);
%powspctrm(:,ifoi,~acttboi) = NaN;
end
if fftflg
fourierspctrm(:,ifoi,acttboi) = fourierspctrm(:,ifoi,acttboi) + (reshape(mean(fourierdum,1),[nchan 1 nacttboi]) ./ ntrials);
%fourierspctrm(:,ifoi,~acttboi) = NaN;
end
if csdflg
crsspctrm(:,ifoi,acttboi) = crsspctrm(:,ifoi,acttboi) + (reshape(mean(csddum,1),[nchancmb 1 nacttboi]) ./ ntrials);
%crsspctrm(:,ifoi,~acttboi) = NaN;
end
case 2 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'no'
if powflg
powspctrm(itrial,:,ifoi,acttboi) = reshape(mean(powdum,1),[nchan 1 nacttboi]);
powspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
if fftflg
fourierspctrm(itrial,:,ifoi,acttboi) = reshape(mean(fourierdum,1), [nchan 1 nacttboi]);
fourierspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
if csdflg
crsspctrm(itrial,:,ifoi,acttboi) = reshape(mean(csddum,1), [nchancmb 1 nacttboi]);
crsspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
end % switch keeprpt
% do calcdof dof = zeros(numper,numfoi,numtoi);
if strcmp(cfg.calcdof, 'yes')
if hastime
acttimboiind = ~all(isnan(spectrum(1,:,foiind(ifoi),:)), 2); % check over all channels, some channels might contain a NaN
acttimboiind = reshape(acttimboiind, [1 ntoi]);
dof(ifoi,acttimboiind) = ntaper(ifoi) + dof(ifoi,acttimboiind);
else % hastime = false
dof(ifoi) = ntaper(ifoi) + dof(ifoi);
end
end
end %ifoi
else
% keep tapers
if ~exist('tapcounter', 'var')
tapcounter = 0;
end
if strcmp(cfg.method, 'mtmconvol')
spectrum = permute(reshape(spectrum_mtmconvol,[nchan ntoi ntaper(1) nfoi]),[3 1 4 2]);
end
currrptind = tapcounter + (1:maxtap);
tapcounter = currrptind(end);
%rptind = reshape(1:ntrials .* maxtap,[maxtap ntrials]);
%currrptind = rptind(:,itrial);
if powflg
if strcmp(cfg.method, 'irasa') % ft_specest_irasa outputs power and not amplitude
powspctrm(currrptind,:,:) = spectrum;
else
powspctrm(currrptind,:,:) = abs(spectrum).^2;
end
end
if fftflg
fourierspctrm(currrptind,:,:,:) = spectrum;
end
if csdflg
crsspctrm(currrptind,:,:,:) = spectrum(cutdatindcmb(:,1),:,:) .* ...
conj(spectrum(cutdatindcmb(:,2),:,:));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set cumptapcnt
switch cfg.method %% IMPORTANT, SHOULD WE KEEP THIS SPLIT UP PER METHOD OR GO FOR A GENERAL SOLUTION NOW THAT WE HAVE SPECEST
case {'mtmconvol' 'wavelet'}
cumtapcnt(itrial,:) = ntaper;
case 'mtmfft'
cumtapcnt(itrial,1) = ntaper(1); % fixed number of tapers? for the moment, yes, as specest_mtmfft computes only one set of tapers
end
end % for ntrials
ft_progress('close');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% END: Main loop over trials
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% re-normalise the TFRs if keeprpt==1
if (strcmp(cfg.method, 'mtmconvol') || strcmp(cfg.method, 'wavelet')) && keeprpt==1
nanmask = trlcnt==0;
if powflg
powspctrm = powspctrm.*ntrials;
powspctrm = powspctrm./trlcnt(ones(size(powspctrm,1),1),:,:);
powspctrm(nanmask(ones(size(powspctrm,1),1),:,:)) = nan;
end
if fftflg
fourierspctrm = fourierspctrm.*ntrials;
fourierspctrm = fourierspctrm./trlcnt(ones(size(fourierspctrm,1),1),:,:);
fourierspctrm(nanmask(ones(size(fourierspctrm,1),1),:,:)) = nan;
end
if csdflg
crsspctrm = crsspctrm.*ntrials;
crsspctrm = crsspctrm./trlcnt(ones(size(crsspctrm,1),1),:,:);
crsspctrm(nanmask(ones(size(crsspctrm,1),1),:,:)) = nan;
end
end
% set output variables
freq = [];
freq.label = data.label;
freq.dimord = dimord;
freq.freq = foi;
hasdc = find(foi==0);
hasnyq = find(foi==data.fsample./2);
hasdc_nyq = [hasdc hasnyq];
if exist('toi', 'var')
freq.time = toi;
end
if powflg
% correct the 0 Hz or Nyqist bin if present, scaling with a factor of 2 is only appropriate for ~0 Hz
if ~isempty(hasdc_nyq)
if keeprpt>1
powspctrm(:,:,hasdc_nyq,:) = powspctrm(:,:,hasdc_nyq,:)./2;
else
powspctrm(:,hasdc_nyq,:) = powspctrm(:,hasdc_nyq,:)./2;
end
end
if startsWith(cfg.output, 'fooof')
% check for brainstorm functions on the path, and add if needed
ft_hastoolbox('brainstorm', 1);
TF(:,1,:) = powspctrm;
Freqs = freq.freq;
Freqs(Freqs==0) = [];
% This grabs the defaults from the brainstorm code