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
- freqsarray_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
offloat
, default 7.0 Number of cycles in the Morlet wavelet. Fixed number or one per frequency.
- time_bandwidth
float
, defaultNone
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_fftbool, 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]. Ifslice
, 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
The number of jobs to run in parallel (default 1). Requires the joblib package. The number of epochs to process at the same time. The parallelization is implemented across channels.
- verbosebool,
str
,int
, orNone
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.
- freqsarray_like of
Methods
__hash__
(/)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.
- fit_transform(X, y=None)[source]¶
Time-frequency transform of times series along the last axis.
- Parameters
- 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.
- Xt
- 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.- Parameters
- **params
dict
Parameters.
- **params
- Returns
- instinstance
The object.