mne.decoding.PSDEstimator¶
- class mne.decoding.PSDEstimator(sfreq=6.283185307179586, fmin=0, fmax=inf, bandwidth=None, adaptive=False, low_bias=True, n_jobs=1, 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.
- adaptivebool
Use adaptive weights to combine the tapered spectra into PSD (slow, use n_jobs >> 1 to speed up computation).
- low_biasbool
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
str Either “full” or “length” (default). If “full”, the PSD will be normalized by the sampling rate as well as the length of the signal (as in nitime).
- verbosebool |
str|int|None 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.
- sfreq
See also
Methods
__hash__(/)Return hash(self).
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
