pyeeg.connectivity.phase_transfer_entropy

pyeeg.connectivity.phase_transfer_entropy(data, delay=None, binsize='scott')

Compute Phase Transfer Entropy between each pair of channels in data.

Parameters:

  • data (ndarray, shape (nsamples, nchannels)) – Input data.

  • delay (int) – Delay between channels. If None, it is estimated from the data.

  • binsize (str) – Method to estimate binsize. Can be ‘scott’, ‘fd’ or ‘otnes’.

Returns:

  • dPTE (ndarray, shape (nchannels, nchannels)) – Directional PTE (x -> y)

  • PTE (ndarray, shape (nchannels, nchannels)) – Undirectional PTE (x <-> y)

References

Lobier, M., Siebenhühner, F., Palva, S., & Palva, J. M. (2014). Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. NeuroImage, 85, 853–872. https://doi.org/10.1016/j.neuroimage.2013.04.090

Notes

Original MATLAB code from Matteo Fraschini, Arjan Hillebrand (VERSION 2.5 /2017) accessed at: https://figshare.com/articles/Phase_Transfer_Entropy/3847086 Cite: Fraschini, Matteo; Hillebrand, Arjan (2017). Phase Transfer Entropy in Matlab. figshare. Dataset. https://doi.org/10.6084/m9.figshare.3847086.v12

Following description of PTE from: M Lobier, F Siebenhuhner, S Palva, JM Palva (2014) Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. Neuroimage 85, 853-872 with implemementation inspired by Java code by C.J. Stam (https://web.archive.org/web/20200711091249/https://home.kpn.nl/stam7883/brainwave.html) Note that implementations differ in normalisation, as well as choices for binning and delay