frites.conn.conn_te#

frites.conn.conn_te(data, times=None, roi=None, min_delay=0, max_delay=30, step_delay=1, return_delays=False, gcrn=True, sfreq=None, n_jobs=1, verbose=None, **kw_links)[source]#

Compute the across-trials transfer entropy (TE).

Parameters
datanumpy:array_like

Electrophysiological data. Several input types are supported :

  • Standard NumPy arrays of shape (n_epochs, n_roi, n_times)

  • mne.Epochs

  • xarray.DataArray of shape (n_epochs, n_roi, n_times)

timesnumpy:array_like | python:None

Time vector array of shape (n_times,). If the input is an xarray, the name of the time dimension can be provided

roinumpy:array_like | python:None

ROI names of a single subject. If the input is an xarray, the name of the ROI dimension can be provided

max_delaypython:int | 30

Number of time points defining where to stop looking at in the past. Increasing this maximum delay input can lead to slower computations

step_delaypython:int | 1

Step between delays to test. By default, test every delays

return_delaysbool | python:False

Specify whether the returned TE should be average across delays (False) or not (True).

sfreqpython:float | python:None

Sampling frequency

gcrnbool | python:True

Specify if the Gaussian Copula Rank Normalization should be applied. Default is True.

n_jobspython:int | 1

Number of jobs to use for parallel computing (use -1 to use all jobs). The parallel loop is set at the pair level.

kw_linkspython:dict | {}

Additional arguments for selecting links to compute are passed to the function frites.conn.conn_links()

Returns
tenumpy:array_like

The TE array of shape (n_pairs, max_delay, time - max_delay) if return_delays is True or (n_pairs, time - max_delay) if False.

See also

conn_links