mne.decoding.PSDEstimator#
- class mne.decoding.PSDEstimator(sfreq=6.283185307179586, fmin=0, fmax=inf, bandwidth=None, adaptive=False, low_bias=True, n_jobs=None, normalization='length', *, verbose=None)[source]#
Compute power spectral density (PSD) using a multi-taper method.
- Parameters:
- sfreq
float
The sampling frequency.
- fmin
float
The lower frequency of interest.
- fmax
float
The upper frequency of interest.
- bandwidth
float
The bandwidth of the multi taper windowing function in Hz.
- adaptive
bool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
- low_bias
bool
Only use tapers with more than 90% spectral concentration within bandwidth.
- n_jobs
int
Number of parallel jobs to use (only used if adaptive=True).
- normalization‘full’ | ‘length’
Normalization strategy. If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in Nitime). Default is
'length'
.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- sfreq
See also
Methods
fit
(epochs_data, y)Compute power spectral density (PSD) using a multi-taper method.
fit_transform
(X[, y])Fit to data, then transform it.
transform
(epochs_data)Compute power spectral density (PSD) using a multi-taper method.
- fit(epochs_data, y)[source]#
Compute power spectral density (PSD) using a multi-taper method.
- Parameters:
- Returns:
- selfinstance of
PSDEstimator
The modified instance.
- selfinstance of