| Parameters: |
- epoch_data : array of shape (n_epochs, n_channels, n_times)
The epochs.
- sfreq : float | int
Sampling frequency of the data.
- freqs : array-like of floats, shape (n_freqs)
The frequencies.
- n_cycles : float | array of float
Number of cycles in the Morlet wavelet. Fixed number or one per
frequency. Defaults to 7.0.
- zero_mean : bool
If True, make sure the wavelets have a mean of zero. Defaults to True.
- time_bandwidth : 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_fft : bool
Use the FFT for convolutions or not. Defaults to True.
- decim : 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.
- output : str, defaults to ‘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.
- n_jobs : int
The number of epochs to process at the same time. The parallelization
is implemented across channels. Defaults to 1.
- verbose : bool, str, int, or None, defaults to None
If not None, override default verbose level (see mne.verbose()
and Logging documentation for more).
|
|---|
| Returns: |
- out : 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
|
|---|
