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reference:ft_spiketriggeredspectrum_stat [2018/08/23 14:43] (current)
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 +=====  FT_SPIKETRIGGEREDSPECTRUM_STAT =====
 +
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_spiketriggeredspectrum_stat"​.
 +
 +<​html><​pre>​
 +  <a href=/​reference/​ft_spiketriggeredspectrum_stat><​font color=green>​FT_SPIKETRIGGEREDSPECTRUM_STAT</​font></​a>​ computes phase-locking statistics for spike-LFP
 +  phases. These contain the PPC statistics according to Vinck et al. 2010 (Neuroimage)
 +  and Vinck et al. 2011 (Journal of Computational Neuroscience).
 + 
 +  Use as:
 +    [stat] = ft_spiketriggeredspectrum_stat(cfg,​ spike)
 + 
 +  The input SPIKE should be a structure as obtained from the <a href=/​reference/​ft_spiketriggeredspectrum><​font color=green>​FT_SPIKETRIGGEREDSPECTRUM</​font></​a>​ function.
 + 
 +  Configurations (cfg) 
 + 
 +  cfg.method ​ = string, indicating which statistic to compute. Can be:
 +      -'​plv'​ : phase-locking value, computes the resultant length over spike
 +               ​phases. More spikes -&gt; lower value (bias).
 +      -'​ang'​ : computes the angular mean of the spike phases.
 +      -'​ral'​ : computes the rayleigh p-value.
 +      -'​ppc0':​ computes the pairwise-phase consistency across all available
 +               spike pairs (Vinck et al., 2010).
 +      -'​ppc1':​ computes the pairwise-phase consistency across all available
 +               spike pairs with exclusion of spike pairs in the same trial.
 +               This avoids history effects within spike-trains to influence
 +               phase lock statistics.
 +      -'​ppc2':​ computes the PPC across all spike pairs with exclusion of
 +               spike pairs in the same trial, but applies a normalization
 +               for every set of trials. This estimator has more variance but
 +               is more robust against dependencies between spike phase and
 +               spike count.
 +          ​
 +  cfg.timwin ​ = double or '​all'​ (default)
 +    - double: indicates we compute statistic with a
 +             ​sliding window of cfg.timwin, i.e. time-resolved analysis.
 +    - '​all':​ we compute statistic over all time-points,​
 +             i.e. in non-time resolved fashion.
 + 
 +  cfg.winstepsize ​ = double, stepsize of sliding window in seconds. For
 +    example if cfg.winstepsize = 0.1, we compute stat every other 100 ms.
 + 
 +  cfg.channel ​     = Nx1 cell-array or numerical array with selection of
 +    channels (default = '​all'​),​See CHANNELSELECTION for details
 + 
 +  cfg.spikechannel = label of ONE unit, according to <a href=/​reference/​ft_channelselection><​font color=green>​FT_CHANNELSELECTION</​font></​a>​
 + 
 +  cfg.spikesel ​    = '​all'​ (default) or numerical or logical selection of spikes.
 + 
 +  cfg.foi ​         = '​all'​ or numerical vector that specifies a subset of
 +    frequencies in Hz, e.g. cfg.foi = spike.freq(1:​10); ​                                   ​
 + 
 +  cfg.latency ​     = [beg end] in sec, or '​maxperiod', ​ '​poststim'​ or
 +   '​prestim'​. ​ This determines the start and end of analysis window.
 + 
 +  cfg.avgoverchan ​ = '​weighted',​ '​unweighted'​ (default) or '​no'​.
 +                   This regulates averaging of fourierspectra prior to
 +                   ​computing the statistic.
 +   - '​weighted' ​ : we average across channels by weighting by the LFP power.
 +                   This is identical to adding the raw LFP signals in time 
 +                   and then taking their FFT.
 +   - '​unweighted':​ we average across channels after normalizing for LFP power. ​
 +                   This is identical to normalizing LFP signals for 
 +                   their power, averaging them, and then taking their FFT.
 +   - '​no' ​       : no weighting is performed, statistic is computed for
 +                   every LFP channel.
 +  cfg.trials ​      = vector of indices (e.g., 1:2:10),
 +                     ​logical selection of trials (e.g., [1010101010]),​ or
 +                    '​all'​ (default)
 + 
 +  Main outputs:
 +    stat.nspikes ​                   =  nChancmb-by-nFreqs-nTimepoints number
 +                                       of spikes used to compute stat
 +    stat.dimord ​                    = '​chan_freq_time'​
 +    stat.labelcmb ​                  ​= ​ nChancmbs cell array with spike vs
 +                                       LFP labels
 +    stat.(cfg.method) ​              ​= ​ nChancmb-by-nFreqs-nTimepoints ​ statistic
 +    stat.freq ​                      ​= ​ 1xnFreqs array of frequencies
 +    stat.nspikes ​                   =  number of spikes used to compute
 + 
 +  The output stat structure can be plotted using ft_singleplotTFR or ft_multiplotTFR.
 +</​pre></​html>​