mne.minimum_norm.source_induced_power(epochs, inverse_operator, frequencies, label=None, lambda2=0.1111111111111111, method='dSPM', nave=1, n_cycles=5, decim=1, use_fft=False, pick_ori=None, baseline=None, baseline_mode='logratio', pca=True, n_jobs=1, zero_mean=False, prepared=False, verbose=None)

Compute induced power and phase lock

Computation can optionaly be restricted in a label.


epochs : instance of Epochs

The epochs.

inverse_operator : instance of InverseOperator

The inverse operator.

frequencies : array

Array of frequencies of interest.

label : Label

Restricts the source estimates to a given label.

lambda2 : float

The regularization parameter of the minimum norm.

method : “MNE” | “dSPM” | “sLORETA”

Use mininum norm, dSPM or sLORETA.

nave : int

The number of averages used to scale the noise covariance matrix.

n_cycles : float | array of float

Number of cycles. Fixed number or one per frequency.

decim : int

Temporal decimation factor.

use_fft : bool

Do convolutions in time or frequency domain with FFT.

pick_ori : None | “normal”

If “normal”, rather than pooling the orientations by taking the norm, only the radial component is kept. This is only implemented when working with loose orientations.

baseline : None (default) or tuple of length 2

The time interval to apply baseline correction. If None do not apply it. If baseline is (a, b) the interval is between “a (s)” and “b (s)”. If a is None the beginning of the data is used and if b is None then b is set to the end of the interval. If baseline is equal ot (None, None) all the time interval is used.

baseline_mode : None | ‘logratio’ | ‘zscore’

Do baseline correction with ratio (power is divided by mean power during baseline) or zscore (power is divided by standard deviation of power during baseline after subtracting the mean, power = [power - mean(power_baseline)] / std(power_baseline)).

pca : bool

If True, the true dimension of data is estimated before running the time-frequency transforms. It reduces the computation times e.g. with a dataset that was maxfiltered (true dim is 64).

n_jobs : int

Number of jobs to run in parallel.

zero_mean : bool

Make sure the wavelets are zero mean.

prepared : bool

If True, do not call prepare_inverse_operator.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose).

Examples using mne.minimum_norm.source_induced_power