Note that this reference documentation is identical to the help that is displayed in Matlab when you type “help ft_connectivityanalysis”.
FT_CONNECTIVITYANALYSIS computes various measures of connectivity between MEG/EEG channels or between source-level signals. Use as stat = ft_connectivityanalysis(cfg, data) stat = ft_connectivityanalysis(cfg, timelock) stat = ft_connectivityanalysis(cfg, freq) stat = ft_connectivityanalysis(cfg, source) where the first input argument is a configuration structure (see below) and the second argument is the output of FT_PREPROCESSING, FT_TIMELOCKANLAYSIS, FT_FREQANALYSIS, FT_MVARANALYSIS or FT_SOURCEANALYSIS. The different connectivity metrics are supported only for specific datatypes (see below). The configuration structure has to contain cfg.method = string, can be 'amplcorr', amplitude correlation, support for freq and source data 'coh', coherence, support for freq, freqmvar and source data. For partial coherence also specify cfg.partchannel 'csd', cross-spectral density matrix, can also calculate partial csds - if cfg.partchannel is specified, support for freq and freqmvar data 'dtf', directed transfer function, support for freq and freqmvar data 'granger', granger causality, support for freq and freqmvar data 'pdc', partial directed coherence, support for freq and freqmvar data 'plv', phase-locking value, support for freq and freqmvar data 'powcorr', power correlation, support for freq and source data 'ppc' pairwise phase consistency 'psi', phaseslope index, support for freq and freqmvar data 'wpli', weighted phase lag index (signed one, still have to take absolute value to get indication of strength of interaction. Note: measure has positive bias. Use wpli_debiased to avoid this. 'wpli_debiased' debiased weighted phase lag index (estimates squared wpli) 'wppc' weighted pairwise phase consistency Additional configuration options are cfg.channel = Nx1 cell-array containing a list of channels which are used for the subsequent computations. This only has an effect when the input data is univariate. See FT_CHANNELSELECTION cfg.channelcmb = Nx2 cell-array containing the channel combinations on which to compute the connectivity. This only has an effect when the input data is univariate. See FT_CHANNELCOMBINATION cfg.trials = Nx1 vector specifying which trials to include for the computation. This only has an effect when the input data contains repetitions. cfg.feedback = string, specifying the feedback presented to the user. Default is 'none'. See FT_PROGRESS For specific methods the cfg can also contain cfg.partchannel = cell-array containing a list of channels that need to be partialized out, support for method 'coh', 'csd', 'plv' cfg.complex = 'abs' (default), 'angle', 'complex', 'imag', 'real', '-logabs', support for method 'coh', 'csd', 'plv' cfg.removemean = 'yes' (default), or 'no', support for method 'powcorr' and 'amplcorr'. To facilitate data-handling and distributed computing with the peer-to-peer module, this function has the following options: cfg.inputfile = ... cfg.outputfile = ... If you specify one of these (or both) the input data will be read from a *.mat file on disk and/or the output data will be written to a *.mat file. These mat files should contain only a single variable, corresponding with the input/output structure. See also FT_PREPROCESSING, FT_TIMELOCKANALYSIS, FT_FREQANALYSIS, FT_MVARANALYSIS, FT_SOURCEANALYSIS, FT_NETWORKANALYSIS