Hi all,
I’m working on the analysis of temporal frequencies (TF) in EEG, and I used the function tfr_array_morlet()
with n_cycles = 6
. My aim is to extract power from the 830 Hz band to analyze alpha and beta oscillations.

Reading the documentation for
tfr_morlet()
, I understand thatn_cycles
only affects the length of the Morlet wavelet (as well asfreqs
), is this correct or is there something else? 
I have a long period (up to 9s), and
tfr_array_morlet()
takes a long time to execute, even if I use a large number of n_jobs. If I only need one or two electrodes, is it possible to execute this function only with the electrode of interest, or is it necessary to send data from all the electrodes to obtain a better/equal result? 
I used to use the ShortTime Fourier Transform (STFT) because I better understood the mathematical procedure behind the concept. In this case, increasing the window length increases frequency resolution, but reduces temporal resolution. What are the advantages and disadvantages of STFT compared with Morlet? Is time resolution better with the Morlet transform?

Finally, how do you choose the method for calculating the timefrequency transform for the eeg signal, and which one do you think is the most reliable?

After TF, I used
mne.EvokedArray()
to transforme my array in MNE object (adapting frequency sample asnew_sf = (initial array of time sample  1) / length of epoch (seconds)
) and plot the topography with.plot_topomap()
, is correct ?
Thanks for your explanations and help!
Johan