Package: connectivity

Connectivity estimation algorithms.

These are domain-specific nodes that estimate information-flow measures between multiple signal channels of a time series, with a focus on EEG. The main nodes to use here are the Group Lasso MVAR node, which is applied to a multivariate time series, followed by one of the dynamical measure nodes (e.g., Direct Directed Transfer Function, Partial Coherence, etc. or the generic Connectivity Measure node). Connectivity is typically represented by a tensor with two space axes (one with custom label 'source' and the other with label 'target') and a frequency axis. Additionally, there may be a time axis if the connectivity measure is time-varying and an instance axis if the connectivity measure is computed for multiple instances (e.g., trials).

Nodes in this package:

• Bayesian ARD MVAR
  Fit an adaptive multivariate autoregressive model (MVAR) using Bayesian regression with a grouped Automatic-Relevance-Determination (ARD) prior.

• Complex Coherence (Coh)
  Estimate the Complex Coherence (Coh) measure from a previously computed MVAR model.

• Complex Spectral Density (S)
  Estimate the Complex Spectral Density measure from a previously computed MVAR model.

• Connectivity Measure
  Estimate the specified connectivity measure from a previously computed MVAR model.

• Direct Directed Transfer Function (dDTF)
  Estimate the Direct Directed Transfer Function ( dDTF) or short-time dDTF (sdDTF) measure from a previously computed MVAR model.

• Directed Transfer Function (DTF)
  Estimate the Directed Transfer Function (DTF) or normalized DTF measure from a previously computed MVAR model.

• Efficiency
  Calculate the (global or local) efficiency parameters of a weighted-directed network.

• Generalized Partial Directed Coherence (GPDC)
  Estimate the Generalized Partial Directed Coherence (GPDC) measure from a previously computed MVAR model.

• Granger Geweke Causality (GGC)
  Estimate the Granger Geweke Causality (GGC) measure from a previously computed MVAR model.

• Group Lasso MVAR
  Fit an adaptive multivariate autoregressive model (MVAR) using a group LASSO regression approach.

• Imaginary Coherence (iCoh)
  Estimate the Imaginary Coherence (iCoh) measure from a previously computed MVAR model.

• Multiple Coherence (mCoh)
  Estimate the Multiple Coherence (mCoh) measure from a previously computed MVAR model.

• Partial Coherence (pCoh)
  Estimate the Partial Coherence (pCoh) measure from a previously computed MVAR model.

• Partial Directed Coherence (PDC)
  Estimate the Partial Directed Coherence (PDC) or normalized PDC measure from a previously computed MVAR model.

• Phase-Locking Value (PLV)
  Calculate the phase-locking value between all pairs of channels.

• Ridge MVAR
  Fit an adaptive multivariate autoregressive model (MVAR) using Tikhonov-regularized (ridge) regression.