Compute Time-Frequency Representation (TFR) using DPSS tapers.
Same computation as tfr_array_multitaper
, 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. The time-window length is thus T = n_cycles / freq.
float
, (optional), default 4.0 (n_tapers=3)Time x (Full) Bandwidth product. Should be >= 2.0. Choose this along with n_cycles to get desired frequency resolution. The number of good tapers (least leakage from far away frequencies) is chosen automatically based on this to floor(time_bandwidth - 1). E.g., With freq = 20 Hz and n_cycles = 10, we get time = 0.5 s. If time_bandwidth = 4., then frequency smoothing is (4 / time) = 8 Hz.
True
The fft based convolution or not.
True
Return inter-trial coherence (ITC) as well as averaged (or single-trial) power.
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
.
str
| list
| slice
| None
Channels to include. Slices and lists of integers will be interpreted as
channel indices. In lists, channel type strings (e.g., ['meg',
'eeg']
) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623']
will pick the given channels. Can also be the
string values “all” to pick all channels, or “data” to pick data
channels. None (default) will pick good data channels. Note that channels
in info['bads']
will be included if their names or indices are
explicitly provided.
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
| 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.
See also
Notes
New in version 0.9.0.
mne.time_frequency.tfr_multitaper
#Frequency and time-frequency sensor analysis
Compute and visualize ERDS maps
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)