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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>