This documentation is for development version 0.18.dev0.

mne.decoding.TimeFrequency

class mne.decoding.TimeFrequency(freqs, sfreq=1.0, method='morlet', n_cycles=7.0, time_bandwidth=None, use_fft=True, decim=1, output='complex', n_jobs=1, verbose=None)[source]

Time frequency transformer.

Time-frequency transform of times series along the last axis.

Parameters:
freqs : array-like of float, shape (n_freqs,)

The frequencies.

sfreq : float | int, default 1.0

Sampling frequency of the data.

method : ‘multitaper’ | ‘morlet’, default ‘morlet’

The time-frequency method. ‘morlet’ convolves a Morlet wavelet. ‘multitaper’ uses Morlet wavelets windowed with multiple DPSS multitapers.

n_cycles : float | array of float, default 7.0

Number of cycles in the Morlet wavelet. Fixed number or one per frequency.

time_bandwidth : float, default None

If None and method=multitaper, will be set to 4.0 (3 tapers). Time x (Full) Bandwidth product. Only applies if method == ‘multitaper’. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1).

use_fft : bool, default True

Use the FFT for convolutions or not.

decim : int | slice, default 1

To reduce memory usage, decimation factor after time-frequency decomposition. If int, returns tfr[…, ::decim]. If slice, returns tfr[…, decim].

Note

Decimation may create aliasing artifacts, yet decimation is done after the convolutions.

output : str, default ‘complex’
  • ‘complex’ : single trial complex.
  • ‘power’ : single trial power.
  • ‘phase’ : single trial phase.
n_jobs : int, default 1

The number of epochs to process at the same time. The parallelization is implemented across channels.

verbose : bool, str, int, or None

If not None, override default verbose level (see mne.verbose() and Logging documentation for more).

Methods

__hash__($self, /) Return hash(self).
fit(X[, y]) Do nothing (for scikit-learn compatibility purposes).
fit_transform(X[, y]) Time-frequency transform of times series along the last axis.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X) Time-frequency transform of times series along the last axis.
__hash__($self, /)

Return hash(self).

fit(X, y=None)[source]

Do nothing (for scikit-learn compatibility purposes).

Parameters:
X : array, shape (n_samples, n_channels, n_times)

The training data.

y : array | None

The target values.

Returns:
self : object

Return self.

fit_transform(X, y=None)[source]

Time-frequency transform of times series along the last axis.

Parameters:
X : array, shape (n_samples, n_channels, n_times)

The training data samples. The channel dimension can be zero- or 1-dimensional.

y : None

For scikit-learn compatibility purposes.

Returns:
Xt : array, shape (n_samples, n_channels, n_freqs, n_times)

The time-frequency transform of the data, where n_channels can be zero- or 1-dimensional.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters:
deep : boolean, optional

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

Returns:
params : mapping of string to any

Parameter names mapped to their values.

set_params(**params)[source]

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

transform(X)[source]

Time-frequency transform of times series along the last axis.

Parameters:
X : array, shape (n_samples, n_channels, n_times)

The training data samples. The channel dimension can be zero- or 1-dimensional.

Returns:
Xt : array, shape (n_samples, n_channels, n_freqs, n_times)

The time-frequency transform of the data, where n_channels can be zero- or 1-dimensional.