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

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).
normalizationstr: 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: 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.

See also

mne.time_frequency.psd_multitaper

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

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, shape (n_samples, n_features): Training set.
yarray, shape (n_samples,): Target values.
**fit_paramsdict: Additional fitting parameters passed to self.fit.

Returns

X_newarray, shape (n_samples, n_features_new): Transformed array.

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.

Examples using `mne.decoding.PSDEstimator`¶

mne.decoding.LinearModel

mne.decoding.Scaler

mne.decoding.PSDEstimator¶

Examples using mne.decoding.PSDEstimator¶

Examples using `mne.decoding.PSDEstimator`¶