mne.minimum_norm.source_band_induced_power(epochs, inverse_operator, bands, label=None, lambda2=0.1111111111111111, method='dSPM', nave=1, n_cycles=5, df=1, use_fft=False, decim=1, baseline=None, baseline_mode='logratio', pca=True, n_jobs=None, prepared=False, method_params=None, use_cps=True, *, verbose=None)[source]#

Compute source space induced power in given frequency bands.

epochsinstance of Epochs

The epochs.

inverse_operatorinstance of InverseOperator

The inverse operator.


Example : bands = dict(alpha=[8, 9]).

labelLabel | list of Label

Restricts the source estimates to a given label or list of labels. If labels are provided in a list, power will be averaged over vertices.


The regularization parameter of the minimum norm.

method“MNE” | “dSPM” | “sLORETA” | “eLORETA”

Use minimum norm, dSPM (default), sLORETA, or eLORETA.


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

n_cyclesfloat | array of float

Number of cycles. Fixed number or one per frequency.


Delta frequency within bands.


Do convolutions in time or frequency domain with FFT.


Temporal decimation factor.

baselineNone (default) or tuple, shape (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’)


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_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_config context manager that sets another value for n_jobs.


If True, do not call prepare_inverse_operator().

method_paramsdict | None

Additional options for eLORETA. See Notes of apply_inverse().

New in v0.16.


Whether to use cortical patch statistics to define normal orientations for surfaces (default True).

Only used when the inverse is free orientation (loose=1.), not in surface orientation, and pick_ori='normal'.

New in v0.20.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

stcsdict of SourceEstimate (or VolSourceEstimate)

The estimated source space induced power estimates in shape (n_vertices, n_frequencies, n_samples) if label=None or label=label. For lists of one or more labels, the induced power estimate has shape (n_labels, n_frequencies, n_samples).

Examples using mne.minimum_norm.source_band_induced_power#

Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM