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reference:ft_connectivity_corr [2018/08/23 14:43] (current)
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 +=====  FT_CONNECTIVITY_CORR =====
 +
 +Note that this reference documentation is identical to the help that is displayed in MATLAB when you type "help ft_connectivity_corr"​.
 +
 +<​html><​pre>​
 +  <a href=/​reference/​ft_connectivity_corr><​font color=green>​FT_CONNECTIVITY_CORR</​font></​a>​ computes correlation,​ coherence or a related quantity from a
 +  data-matrix containing a covariance or cross-spectral density. It implements the
 +  methods as described in the following papers:
 + 
 +  Coherence: Rosenberg et al, The Fourier approach to the identification of
 +  functional coupling between neuronal spike trains. Prog Biophys Molec
 +  Biol 1989; 53; 1-31
 + 
 +  Partial coherence: Rosenberg et al, Identification of patterns of
 +  neuronal connectivity - partial spectra, partial coherence, and neuronal
 +  interactions. J. Neurosci. Methods, 1998; 83; 57-72
 + 
 +  Phase locking value: Lachaux et al, Measuring phase sychrony in brain
 +  signals. Human Brain Mapping, 1999; 8; 194-208.
 + 
 +  Imaginary part of coherency: Nolte et al, Identifying true brain
 +  interaction from EEG data using the imaginary part of coherence. Clinical
 +  Neurophysiology,​ 2004; 115; 2292-2307
 + 
 +  Use as
 +    [c, v, n] = ft_connectivity_corr(input,​ ...)
 + 
 +  The input data should be an array organized as
 +    Repetitions x Channel x Channel (x Frequency) (x Time)
 +  or
 +    Repetitions x Channelcombination (x Frequency) (x Time)
 + 
 +  If the input already contains an average, the first dimension should be singleton.
 +  Furthermore,​ the input data can be complex-valued cross spectral densities, or
 +  real-valued covariance estimates. If the former is the case, the output will be
 +  coherence (or a derived metric), if the latter is the case, the output will be the
 +  correlation coefficient.
 + 
 +  Additional optional input arguments come as key-value pairs:
 +    hasjack ​  = 0 or 1 specifying whether the Repetitions represent
 +                leave-one-out samples
 +    complex ​  = '​abs',​ '​angle',​ '​real',​ '​imag',​ '​complex',​ '​logabs'​ for
 +                post-processing of coherency
 +    feedback ​ = '​none',​ '​text',​ '​textbar'​ type of feedback showing progress of
 +                computation
 +    dimord ​   = specifying how the input matrix should be interpreted
 +    powindx ​  = required if the input data contain linearly indexed
 +                channel pairs. should be an Nx2 matrix indexing on each
 +                row for the respective channel pair the indices of the
 +                corresponding auto-spectra
 +    pownorm ​  = flag that specifies whether normalisation with the
 +                product of the power should be performed (thus should
 +                be true when correlation/​coherence is requested, and
 +                false when covariance or cross-spectral density is
 +                requested).
 + 
 +  Partialisation can be performed when the input data is (chan x chan). The
 +  following options need to be specified:
 + 
 +    pchanindx ​  = index-vector to the channels that need to be
 +                  partialised
 +    allchanindx = index-vector to all channels that are used
 +                  (including the "​to-be-partialised"​ ones).
 + 
 +  The output c contains the correlation/​coherence,​ v is a variance estimate
 +  which only can be computed if the data contains leave-one-out samples,
 +  and n is the number of repetitions in the input data.
 + 
 +  See also <a href=/​reference/​ft_connectivityanalysis><​font color=green>​FT_CONNECTIVITYANALYSIS</​font></​a>​
 +</​pre></​html>​