mne.connectivity.
phase_slope_index
(data, indices=None, sfreq=6.283185307179586, mode='multitaper', fmin=None, fmax=inf, tmin=None, tmax=None, mt_bandwidth=None, mt_adaptive=False, mt_low_bias=True, cwt_frequencies=None, cwt_n_cycles=7, block_size=1000, n_jobs=1, verbose=None)¶Compute the Phase Slope Index (PSI) connectivity measure
The PSI is an effective connectivity measure, i.e., a measure which can give an indication of the direction of the information flow (causality). For two time series, and one computes the PSI between the first and the second time series as follows
indices = (np.array([0]), np.array([1])) psi = phase_slope_index(data, indices=indices, ...)
A positive value means that time series 0 is ahead of time series 1 and a negative value means the opposite.
The PSI is computed from the coherency (see spectral_connectivity), details can be found in [1].
Parameters:  data : arraylike, shape=(n_epochs, n_signals, n_times)
indices : tuple of arrays  None
sfreq : float
mode : str
fmin : float  tuple of floats
fmax : float  tuple of floats
tmin : float  None
tmax : float  None
mt_bandwidth : float  None
mt_adaptive : bool
mt_low_bias : bool
cwt_frequencies : array
cwt_n_cycles: float  array of float :
block_size : int
n_jobs : int
verbose : bool, str, int, or None


Returns:  psi : array
freqs : array
times : array
n_epochs : int
n_tapers : int

References
[1] Nolte et al. “Robustly Estimating the Flow Direction of Information in Complex Physical Systems”, Physical Review Letters, vol. 100, no. 23, pp. 14, Jun. 2008.