frites.conn.conn_fit#

frites.conn.conn_fit(data, y, roi=None, times=None, mi_type='cc', gcrn=True, max_delay=0.3, avg_delay=False, net=False, sfreq=None, verbose=None, **kw_links)[source]#

Feature-specific information transfer.

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)

ynumpy:array_like

The feature of shape (n_trials,). This feature vector can either be categorical and in that case, the mutual information type has to ‘cd’ or y can also be a continuous regressor and in that case the mutual information type has to be ‘cc’

roinumpy:array_like | python:None

Array of region of interest name of shape (n_roi,)

timesnumpy:array_like | python:None

Array of time points of shape (n_times,)

mi_type{‘cc’, ‘cd’}
Mutual information type. Switch between :

  • ‘cc’ : if the y input is a continuous regressor

  • ‘cd’ : if the y input is a discret vector with categorical integers inside

gcrnbool | python:True

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

max_delaypython:float | .3

Maximum delay for past conditioning

avg_delaybool | python:False

If False (default) the returned FIT is aggregated across delays. If True, the returned FIT is going to contained the additional dimension corresponding to the number of delays used.

sfreqpython:float | python:None

The sampling frequency.

kw_linkspython:dict | {}

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

Returns
fitnumpy:array_like

The feature specific information transfer of shape (n_pairs, n_times) if avg_delay is False or (n_pairs, n_delays, n_times) if avg_delay is True.

See also

conn_links

References

Celotto M, et al. [3]

Examples using frites.conn.conn_fit#

FIT: Feature specific information transfer

FIT: Feature specific information transfer