- 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).