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mne.minimum_norm.source_band_induced_power#

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.

Parameters:
epochsinstance of Epochs

The epochs.

inverse_operatorinstance of InverseOperator

The inverse operator.

bandsdict

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

labelLabel

Restricts the source estimates to a given label.

lambda2float

The regularization parameter of the minimum norm.

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

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

naveint

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.

dffloat

Delta frequency within bands.

use_fftbool

Do convolutions in time or frequency domain with FFT.

decimint

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’)

pcabool

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_backend() context manager that sets another value for n_jobs.

preparedbool

If True, do not call prepare_inverse_operator().

method_paramsdict | None

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

New in version 0.16.

use_cpsbool

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 version 0.20.

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.

Returns:
stcsdict of SourceEstimate (or VolSourceEstimate)

The estimated source space induced power estimates.

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

Compute induced power in the source space with dSPM