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

  FT_SPIKETRIGGEREDSPECTRUM_CONVOL computes the Fourier spectrum (amplitude and
  phase) of the LFP around the spikes using convolution of the complete LFP traces. A
  phase of zero corresponds to the spike being on the peak of the LFP oscillation. A
  phase of 180 degree corresponds to the spike being in the through of the oscillation.
  A phase of 45 degrees corresponds to the spike being just after the peak in the LFP.
  The difference to FT_SPIKETRIGGEREDSPECTRUM_FFT is that this function allows for
  multiple frequencies to be processed with different time-windows per frequency, and
  that FT_SPIKETRIGGEREDSPECTRUM_FFT is based on taking the FFT of a limited LFP
  segment around each spike.
  Use as
    [sts] = ft_spiketriggeredspectrum_convol(cfg,data,spike)
    [sts] = ft_spiketriggeredspectrum_convol(cfg,data)
  The spike data can either be contained in the data input or in the spike
  The input DATA should be organised as the raw datatype, obtained from
  The input SPIKE should be organised as the spike or the raw datatype, obtained from
  FT_SPIKE_MAKETRIALS or FT_PREPROCESSING, in which case the conversion is done
  within this function.
  Important is that data.time and spike.trialtime should be referenced
  relative to the same trial trigger times!
  Configurations (following largely FT_FREQNALYSIS with method mtmconvol)
      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')
      cfg.t_ftimwin       = vector 1 x numfoi, length of time window (in
      cfg.taperopt        =  parameter that goes in WINDOW function (only
                            applies to windows like KAISER).
      cfg.spikechannel    = cell-array with selection of channels (default = 'all')
                            see FT_CHANNELSELECTION for details         = Nx1 cell-array with selection of channels (default = 'all'),
                            see FT_CHANNELSELECTION for details
      cfg.borderspikes    = 'yes' (default) or 'no'. If 'yes', we process the spikes
                            falling at the border using an LFP that is not centered
                            on the spike. If 'no', we output NaNs for spikes
                            around which we could not center an LFP segment.
      cfg.rejectsaturation= 'yes' (default) or 'no'. If 'yes', we set
                            EEG segments where the maximum or minimum
                            voltage range is reached
                            with zero derivative (i.e., saturated signal) to
                            NaN, effectively setting all spikes phases that
                            use these parts of the EEG to NaN. An EEG that
                            saturates always returns the same phase at all
                            frequencies and should be ignored.
  Note: some adjustment of the frequencies can occur as the chosen time-window may not 
  be appropriate for the chosen frequency.
  For example, suppose that cfg.foi = 80, data.fsample = 1000, and
  cfg.t_ftimwin = 0.0625. The DFT frequencies in that case are 
  linspace(0,1000,63) such that cfg.foi --> 80.645. In practice, this error
  can only become large if the number of cycles per frequency is very
  small and the frequency is high. For example, suppose that cfg.foi = 80
  and cfg.t_ftimwin = 0.0125. In that case cfg.foi-->83.33.
  The error is smaller as data.fsample is larger.
    sts is a spike structure, containing new fields:
    sts.fourierspctrm = 1 x nUnits cell array with dimord spike_lfplabel_freq
    sts.lfplabel      = 1 x nChan cell array with EEG labels
    sts.freq          = 1 x nFreq frequencies. Note that per default, not
                        all frequencies can be used as we compute the DFT
                        around the spike based on an uneven number of
                        samples. This introduces a slight adjustment of the
                        selected frequencies.
    Note: sts.fourierspctrm can contain NaNs, for example if
    cfg.borderspikes = 'no', or if cfg.rejectsaturation = 'yes', or if the
    trial length was too short for the window desired.
  WHen using multitapering, the phase distortion is corrected for.
  The output STS data structure can be input to FT_SPIKETRIGGEREDSPECTRUM_STAT