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”.
FT_CONNECTIVITY_CORR 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 FT_CONNECTIVITYANALYSIS