Compute Time-Frequency Representation (TFR) using Morlet wavelets.
Same computation as tfr_morlet
, but operates on
NumPy arrays
instead of Epochs
objects.
array
of shape (n_epochs, n_channels, n_times)The epochs.
float
| int
Sampling frequency of the data.
float
, shape (n_freqs,)The frequencies.
float
| array
of float
, default 7.0Number of cycles in the Morlet wavelet. Fixed number or one per frequency.
False
If True, make sure the wavelets have a mean of zero. default False.
Use the FFT for convolutions or not. default True.
int
| slice
To reduce memory usage, decimation factor after time-frequency
decomposition. default 1
If int
, returns tfr[…, ::decim].
If slice
, returns tfr[…, decim].
Note
Decimation may create aliasing artifacts, yet decimation is done after the convolutions.
str
, default ‘complex’‘complex’ : single trial complex.
‘power’ : single trial power.
‘phase’ : single trial phase.
‘avg_power’ : average of single trial power.
‘itc’ : inter-trial coherence.
‘avg_power_itc’ : average of single trial power and inter-trial coherence across trials.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
The number of epochs to process at the same time. The parallelization
is implemented across channels. Default 1.
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.
array
Time frequency transform of epoch_data. If output is in [‘complex’, ‘phase’, ‘power’], then shape of out is (n_epochs, n_chans, n_freqs, n_times), else it is (n_chans, n_freqs, n_times). If output is ‘avg_power_itc’, the real values code for ‘avg_power’ and the imaginary values code for the ‘itc’: out = avg_power + i * itc.
See also
Notes
New in version 0.14.0.
mne.time_frequency.tfr_array_morlet
#Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)