FT_CONNECTIVITY_PDC
Note that this reference documentation is identical to the help that is displayed in MATLAB when you type “help ft_connectivity_pdc”.
FT_CONNECTIVITY_PDC computes partial directed coherence. This function implements the metrices described in Baccala et al., Biological Cybernetics 2001, 84(6), 463-74. and in Baccala et al., 15th Int.Conf.on DSP 2007, 163-66. The implemented algorithm has been tested against the implementation in the SIFT-toolbox. It yields numerically identical results to what is known there as 'nPDC' (for PDC) and 'GPDC' for generalized pdc. Use as [p, v, n] = ft_connectivity_pdc(h, key1, value1, ...) The input argument H should be a spectral transfer matrix organized as Nrpt x Nchan x Nchan x Nfreq (x Ntime), where Nrpt can be 1. Additional optional input arguments come as key-value pairs: 'hasjack' = 0 (default) is a boolean specifying whether the input contains leave-one-outs, required for correct variance estimate 'feedback' = string, determining verbosity (default = 'none'), see FT_PROGRESS 'invfun' = 'inv' (default) or 'pinv', the function used to invert the transfer matrix to obtain the fourier transform of the MVAR coefficients. Use 'pinv' if the data are poorly-conditioned. 'noisecov' = matrix containing the covariance of the residuals of the MVAR model. If this matrix is defined, the function returns the generalized partial directed coherence. Output arguments: p = partial directed coherence matrix Nchan x Nchan x Nfreq (x Ntime). If multiple observations in the input, the average is returned. v = variance of p across observations. n = number of observations. Typically, nrpt should be 1 (where the spectral transfer matrix is computed across observations. When nrpt>1 and hasjack is true the input is assumed to contain the leave-one-out estimates of H, thus a more reliable estimate of the relevant quantities. See also FT_CONNECTIVITYANALYSIS