I’ve started to look into wavelet-TFR, and MNE offers `tfr_morlet`

. But what about other wavelet transformations? After discussion with colleagues, I’d like to try to compute a TFR representation with morlet wavelet + other wavelets at once.

### Morlet wavelet case

In the case of `tfr_morlet`

, the function `_compute_tfr`

creates one wavelet per frequency of interest to account for variable time-window duration, decreasing with frequency (depending on the number of cycles set).

Next step, the wavelets are passed to `_time_frequency_loop`

as an array of shape `(n_tapers, n_wavelets, n_times`

) with `n_tapers=1`

for wavelet-based TFR.

Next step, the CWT is computed (as a generator used in `_time_frequency_loop`

):

With the wavelets `W`

(`Ws`

in `_cwt_gen`

) provided as a list of wavelets (1D array): one wavelet per frequency of interest. The CWT generator yields the TFR for each epoch `(n_freqs, n_times)`

.

### Multiple wavelets case

Now, what if I want to have both a morlet wavelet + a daubechies wavelet? (as an example only). I’m stuck at this stage:

- Should I combine both wavelets before CWT? And how?
- Should I compute 2 times the CWT, one with each wavelet, and combine the TFR? And how?

EDIT: Not entirely sure, but it looks like I’m referring to DWT.

### Addition to MNE-Python

I’m waiting for the literature that my colleague will forward me about wavelet representation of EEG data, if it checks out, I could add another TFR function in which you provide a list of wavelets to use (by name probably).