This documentation is for development version 0.18.dev0.

# mne.minimum_norm.source_induced_power¶

mne.minimum_norm.source_induced_power(epochs, inverse_operator, freqs, 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, method_params=None, verbose=None)[source]

Compute induced power and phase lock.

Computation can optionally be restricted in a label.

Parameters: epochs : instance of Epochs The epochs. inverse_operator : instance of InverseOperator The inverse operator. freqs : 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” | “eLORETA” Use minimum norm, dSPM (default), sLORETA, or eLORETA. 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 to (None, None) all the time interval is used. baseline_mode : ‘mean’ | ‘ratio’ | ‘logratio’ | ‘percent’ | ‘zscore’ | ‘zlogratio’ Perform baseline correction by subtracting the mean of baseline values (‘mean’) dividing by the mean of baseline values (‘ratio’) dividing by the mean of baseline values and taking the log (‘logratio’) subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’) subtracting the mean of baseline values and dividing by the standard deviation of baseline values (‘zscore’) dividing by the mean of baseline values, taking the log, and dividing by the standard deviation of log baseline values (‘zlogratio’) 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(). method_params : dict | None Additional options for eLORETA. See Notes of apply_inverse(). verbose : bool, str, int, or None If not None, override default verbose level (see mne.verbose() and Logging documentation for more).