# mne.minimum_norm.source_induced_power¶

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.

Parameters: 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).