FT_CONNECTIVITY_PPC computes pairwise phase consistency or weighted pairwise phase
 consistency from a data-matrix containing a cross-spectral density. This implements
 the method described in Vinck M, van Wingerden M, Womelsdorf T, Fries P, Pennartz
 CM. The pairwise phase consistency: a bias-free measure of rhythmic neuronal
 synchronization. Neuroimage. 2010 May 15;51(1):112-22.

 Use as
   [c, v, n] = ft_connectivity_ppc(input, ...)

 The input data input should be organized as:
   Repetitions x Channel x Channel (x Frequency) (x Time)
   Repetitions x Channelcombination (x Frequency) (x Time)

 The first dimension should contain repetitions and should not contain an average
 already. Also, it should not consist of leave-one-out averages.

 Additional optional input arguments come as key-value pairs:
   feedback  = 'none', 'text', 'textbar' type of feedback showing progress  of computation
   weighted  = 1 (or true) or 0 (or false), we compute unweighted ppc or
               weighted ppc, the weighting is according to the magnitude of
               the cross-spectrum

 The output c contains the ppc, v is a leave-one-out variance estimate which is only
 computed if dojack = 1,and n is the number of repetitions in the input data.