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:

sfreqfloat: The sampling frequency.
fminfloat: The lower frequency of interest.
fmaxfloat: The upper frequency of interest.
bandwidthfloat: 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_jobsint: 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'.
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.

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.
`set_output`(*[, transform])	Set output container.
`transform`(epochs_data)	Compute power spectral density (PSD) using a multi-taper method.

See also

mne.time_frequency.psd_array_multitaper
mne.io.Raw.compute_psd
mne.Epochs.compute_psd
mne.Evoked.compute_psd

fit(epochs_data, y)[source]#

Compute power spectral density (PSD) using a multi-taper method.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times): The data.
yarray, shape (n_epochs,): The label for each epoch.

Returns:

selfinstance of PSDEstimator: The modified instance.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray_like of shape (n_samples, n_features): Input samples.
yarray_like of shape (n_samples,) or (n_samples, n_outputs), default=None: Target values (None for unsupervised transformations).
**fit_paramsdict: Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

set_output(*, transform=None)[source]#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

“default”: Default output format of a transformer
“pandas”: DataFrame output
“polars”: Polars output
None: Transform configuration is unchanged

New in v1.4: “polars” option was added.

Returns:

selfestimator instance: Estimator instance.

transform(epochs_data)[source]#

Compute power spectral density (PSD) using a multi-taper method.

Parameters:

epochs_dataarray, shape (n_epochs, n_channels, n_times): The data.

Returns:

psdarray, shape (n_signals, n_freqs) or (n_freqs,): The computed PSD.