Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_freqanalysis”.

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. cfg.output = 'pow' return the power-spectra 'powandcsd' return the power and the cross-spectra 'fourier' return the complex Fourier-spectra 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 or 'maxperlen', length in seconds to which the data can be padded out (default = 'maxperlen') 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. Note that this will run very slow if you specify cfg.pad as maxperlen AND the number of samples turns out to have a large prime factor sum. This is because the FFTs will then be computed very inefficiently. 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 MTMFFT performs frequency analysis on any time series trial data using the 'multitaper method' (MTM) based on discrete prolate spheroidal sequences (Slepian sequences) as tapers. Alternatively, you can use conventional tapers (e.g. Hanning). 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. 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. MTMCONVOL 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.foilim = [begin end], frequency band of interest OR 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) WAVELET 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 choosen >= 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) TFR 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

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