Compute Time-Frequency Representation (TFR) using Morlet wavelets.
Same computation as tfr_array_morlet
, but
operates on Epochs
objects instead of
NumPy arrays
.
Epochs
| Evoked
The epochs or evoked object.
ndarray
, shape (n_freqs,)The frequencies in Hz.
float
| ndarray
, shape (n_freqs,)The number of cycles globally or for each frequency.
False
The fft based convolution or not.
True
Return inter-trial coherence (ITC) as well as averaged power.
Must be False
for evoked data.
int
| slice
, default 1To 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.
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
.
int
| None
, default None
The indices of the channels to decompose. If None, all available good data channels are decomposed.
True
Make sure the wavelet has a mean of zero.
New in version 0.13.0.
True
If False
return an EpochsTFR
containing separate TFRs for each
epoch. If True
return an AverageTFR
containing the average of all
TFRs across epochs.
Note
Using average=True
is functionally equivalent to using
average=False
followed by EpochsTFR.average()
, but is
more memory efficient.
New in version 0.13.0.
str
Can be “power” (default) or “complex”. If “complex”, then average must be False.
New in version 0.15.0.
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.
AverageTFR
| EpochsTFR
The averaged or single-trial power.
AverageTFR
| EpochsTFR
The inter-trial coherence (ITC). Only returned if return_itc is True.
mne.time_frequency.tfr_morlet
#Overview of MEG/EEG analysis with MNE-Python
Frequency and time-frequency sensor analysis
Non-parametric 1 sample cluster statistic on single trial power
Non-parametric between conditions cluster statistic on single trial power
Mass-univariate twoway repeated measures ANOVA on single trial power
Spatiotemporal permutation F-test on full sensor data
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)