# mne.connectivity.phase_slope_index¶

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_freqs=None, cwt_n_cycles=7, block_size=1000, n_jobs=1, verbose=None)[source]

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
dataarray_like, shape=(n_epochs, n_signals, n_times)

Can also be a list/generator of array, shape =(n_signals, n_times); list/generator of SourceEstimate; or Epochs. The data from which to compute connectivity. Note that it is also possible to combine multiple signals by providing a list of tuples, e.g., data = [(arr_0, stc_0), (arr_1, stc_1), (arr_2, stc_2)], corresponds to 3 epochs, and arr_* could be an array with the same number of time points as stc_*.

indices

Two arrays with indices of connections for which to compute connectivity. If None, all connections are computed.

sfreqfloat

The sampling frequency.

modestr

Spectrum estimation mode can be either: ‘multitaper’, ‘fourier’, or ‘cwt_morlet’.

fmin

The lower frequency of interest. Multiple bands are defined using a tuple, e.g., (8., 20.) for two bands with 8Hz and 20Hz lower freq. If None the frequency corresponding to an epoch length of 5 cycles is used.

fmax

The upper frequency of interest. Multiple bands are dedined using a tuple, e.g. (13., 30.) for two band with 13Hz and 30Hz upper freq.

tmin

Time to start connectivity estimation.

tmax

Time to end connectivity estimation.

mt_bandwidth

The bandwidth of the multitaper windowing function in Hz. Only used in ‘multitaper’ mode.

Use adaptive weights to combine the tapered spectra into PSD. Only used in ‘multitaper’ mode.

mt_low_biasbool

Only use tapers with more than 90% spectral concentration within bandwidth. Only used in ‘multitaper’ mode.

cwt_freqsarray

Array of frequencies of interest. Only used in ‘cwt_morlet’ mode.

cwt_n_cycles: float | array of float

Number of cycles. Fixed number or one per frequency. Only used in ‘cwt_morlet’ mode.

block_sizeint

How many connections to compute at once (higher numbers are faster but require more memory).

n_jobsint

How many epochs to process in parallel.

verbose

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Returns
psiarray

Computed connectivity measure(s). The shape of each array is either (n_signals, n_signals, n_bands) mode: ‘multitaper’ or ‘fourier’ (n_signals, n_signals, n_bands, n_times) mode: ‘cwt_morlet’ when “indices” is None, or (n_con, n_bands) mode: ‘multitaper’ or ‘fourier’ (n_con, n_bands, n_times) mode: ‘cwt_morlet’ when “indices” is specified and “n_con = len(indices[0])”.

freqsarray

Frequency points at which the connectivity was computed.

timesarray

Time points for which the connectivity was computed.

n_epochsint

Number of epochs used for computation.

n_tapersint

The number of DPSS tapers used. Only defined in ‘multitaper’ mode. Otherwise None is returned.

References

[1] Nolte et al. “Robustly Estimating the Flow Direction of Information in Complex Physical Systems”, Physical Review Letters, vol. 100, no. 23, pp. 1-4, Jun. 2008.