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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 | |
opts_bst = getfield(process_fooof('GetDescription'), 'options'); | |
% Fetch user settings, this is a chunk of code copied over from | |
% process_fooof, to bypass the whole database etc handling. | |
opt = ft_getopt(cfg, 'fooof', []); | |
opt.freq_range = ft_getopt(opt, 'freq_range', Freqs([1 end])); | |
opt.peak_width_limits = ft_getopt(opt, 'peak_width_limits', opts_bst.peakwidth.Value{1}); | |
opt.max_peaks = ft_getopt(opt, 'max_peaks', opts_bst.maxpeaks.Value{1}); | |
opt.min_peak_height = ft_getopt(opt, 'min_peak_height', opts_bst.minpeakheight.Value{1}/10); % convert from dB to B | |
opt.aperiodic_mode = ft_getopt(opt, 'aperiodic_mode', opts_bst.apermode.Value); | |
opt.peak_threshold = ft_getopt(opt, 'peak_threshold', 2); % 2 std dev: parameter for interface simplification | |
opt.return_spectrum = ft_getopt(opt, 'return_spectrum', 1); % SPM/FT: set to 1 | |
opt.border_threshold = ft_getopt(opt, 'border_threshold', 1); % 1 std dev: proximity to edge of spectrum, static in Python | |
% Matlab-only options | |
opt.power_line = ft_getopt(opt, 'power_line', '50'); % for some reason it should be a string, if you don't want a notch, use 'inf'. Brainstorm's default is '60' | |
opt.peak_type = ft_getopt(opt, 'peak_type', opts_bst.peaktype.Value); | |
opt.proximity_threshold = ft_getopt(opt, 'proximity_threshold', opts_bst.proxthresh.Value{1}); | |
opt.guess_weight = ft_getopt(opt, 'guess_weight', opts_bst.guessweight.Value); | |
opt.thresh_after = ft_getopt(opt, 'thresh_after', true); % Threshold after fitting always selected for Matlab (mirrors the Python FOOOF closest by removing peaks that do not satisfy a user's predetermined conditions) | |
% Output options | |
opt.sort_type = opts_bst.sorttype.Value; | |
opt.sort_param = opts_bst.sortparam.Value; | |
opt.sort_bands = opts_bst.sortbands.Value; | |
% Check input frequency bounds | |
if (any(opt.freq_range < 0) || opt.freq_range(1) >= opt.freq_range(2)) | |
bst_report('error','Invalid Frequency range'); | |
return | |
end | |
hasOptimTools = 0; | |
if exist('fmincon', 'file') | |
hasOptimTools = 1; | |
disp('Using constrained optimization, Guess Weight ignored.') | |
end | |
[fs, fg] = process_fooof('FOOOF_matlab', TF, freq.freq, opt, hasOptimTools); | |
% add the options back to the cfg | |
cfg.fooof = opt; | |
switch cfg.output | |
case 'fooof' | |
powspctrm_f = cat(1, fg.fooofed_spectrum); | |
case 'fooof_peaks' | |
powspctrm_f = cat(1, fg.peak_fit); | |
case 'fooof_aperiodic' | |
powspctrm_f = cat(1, fg.ap_fit); | |
end | |
fg = removefields(fg, {'fooofed_spectrum', 'peak_fit', 'ap_fit'}); | |
for k = 1:size(powspctrm_f,1) | |
powspctrm(k,:) = interp1(fs, powspctrm_f(k,:), freq.freq, 'linear', nan); | |
fg(k).label = freq.label{k}; | |
end | |
freq.powspctrm = powspctrm; | |
freq.fooofparams = fg(:); | |
else | |
freq.powspctrm = powspctrm; | |
end | |
end | |
if fftflg | |
% 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 | |
fourierspctrm(:,:,hasdc_nyq,:) = fourierspctrm(:,:,hasdc_nyq,:)./sqrt(2); | |
else | |
fourierspctrm(:,hasdc_nyq,:) = fourierspctrm(:,hasdc_nyq,:)./sqrt(2); | |
end | |
end | |
freq.fourierspctrm = fourierspctrm; | |
end | |
if csdflg | |
% 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 | |
crsspctrm(:,:,hasdc_nyq,:) = crsspctrm(:,:,hasdc_nyq,:)./2; | |
else | |
crsspctrm(:,hasdc_nyq,:) = crsspctrm(:,hasdc_nyq,:)./2; | |
end | |
end | |
freq.labelcmb = cfg.channelcmb; | |
freq.crsspctrm = crsspctrm; | |
end | |
if strcmp(cfg.calcdof, 'yes') | |
freq.dof = 2 .* dof; | |
end | |
if strcmp(cfg.method, 'mtmfft') && (keeprpt == 2 || keeprpt == 4) | |
freq.cumsumcnt = trllength'; | |
end | |
if exist('cumtapcnt', 'var') && (keeprpt == 2 || keeprpt == 4) | |
freq.cumtapcnt = cumtapcnt; | |
end | |
% backwards compatability of foilim | |
if ~isempty(cfg.foilim) | |
cfg = rmfield(cfg, 'foi'); | |
else | |
cfg = rmfield(cfg, 'foilim'); | |
end | |
% some fields from the input should always be copied over in the output | |
freq = copyfields(data, freq, {'grad', 'elec', 'opto', 'topo', 'topolabel', 'unmixing'}); | |
if isfield(data, 'trialinfo') && strcmp(cfg.keeptrials, 'yes') | |
% copy the trialinfo into the output, but not the sampleinfo | |
freq.trialinfo = data.trialinfo; | |
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 |