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')[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'.

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.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, epochs_data])

Request metadata passed to the fit method.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, epochs_data])

Request metadata passed to the transform method.

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=None)[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.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_fit_request(*, epochs_data: bool | None | str = '$UNCHANGED$') → PSDEstimator[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in v1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
epochs_datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for epochs_data parameter in fit.

Returns:
selfobject

The updated object.

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.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_transform_request(*, epochs_data: bool | None | str = '$UNCHANGED$') → PSDEstimator[source]#

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in v1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
epochs_datastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for epochs_data parameter in transform.

Returns:
selfobject

The updated object.

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.