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=1, 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.
- bands
dict
Example : bands = dict(alpha=[8, 9]).
- 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
offloat
Number of cycles. Fixed number or one per frequency.
- df
float
Delta frequency within bands.
- use_fftbool
Do convolutions in time or frequency domain with FFT.
- decim
int
Temporal decimation factor.
- baseline
None
(default) ortuple
, 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_jobs
int
The number of jobs to run in parallel (default 1). Requires the joblib package.
- preparedbool
If True, do not call
prepare_inverse_operator()
.- method_params
dict
|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, andpick_ori='normal'
.New in version 0.20.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- epochsinstance of
- Returns
- stcs
dict
ofSourceEstimate
(orVolSourceEstimate
) The estimated source space induced power estimates.
- stcs