Compute Time-Frequency Representation (TFR) using DPSS tapers.
Same computation as tfr_multitaper
, 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
Number of cycles in the wavelet. Fixed number or one per frequency. Defaults to 7.0.
If True, make sure the wavelets have a mean of zero. Defaults to True.
float
If None, will be set to 4.0 (3 tapers). Time x (Full) Bandwidth product. The number of good tapers (low-bias) is chosen automatically based on this to equal floor(time_bandwidth - 1). Defaults to None.
Use the FFT for convolutions or not. Defaults to True.
int
| slice
To reduce memory usage, decimation factor after time-frequency
decomposition. Defaults to 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 per taper complex values.
‘power’ : single trial power.
‘phase’ : single trial per taper 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. Defaults to 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 in ['complex',
'phase']
, then the shape of out
is (n_epochs, n_chans,
n_tapers, n_freqs, n_times)
; if output is ‘power’, the 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 in out
contain the average power and the imaginary values
contain the ITC: out = avg_power + i * itc
.
See also
Notes
New in version 0.14.0.