[Mne_analysis] events info on single trial time-frequency epochs

[Claire Braboszcz]

Hi,

Thanks Jaakko and Jean-Rémi for your responses.
I'll try to make my way through it and will be looking forward for a tutorial !

Best,
Claire

Le 03. 03. 17 à 09:57, JR KING a écrit :
> Hi Claire,
>
> Jaakko's solution is a possibility (e.g. this is what I used here http://dx.doi.org/10.1016/j.neuron.2016.10.051), but this isn't optimal because the signal would be rectified at the sensor level.
>
> You'd ideally need to work with covariance matrices based on signals filtered at particular frequencies. I'm hoping to have the time to add a tutorial and a set a functions in the next couple of weeks.
>
> Best,
>
> Jean-Rémi
>
> On 3 March 2017 at 03:41, Jaakko Leppakangas <jaeilepp at gmail.com <mailto:jaeilepp at gmail.com>> wrote:
>
>     Hi Claire,
>
>         tfr_cond_n = tfr_morlet(epochs[cond_n],freqs=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)
>
>
>     This is the way I would do it. The events information is not stored to the EpochsTFR.
>
>     -Jaakko
>
>     On Thu, Mar 2, 2017 at 9:44 PM, Claire Braboszcz <claire at guakamole.org <mailto:claire at guakamole.org>> wrote:
>
>         Hello,
>
>         I want to create single trials time-frequency epochs to later perform time-frequency decoding.
>         I am wondering what is the correct way for creating my epochs.  I have EEG data with 4 types of events.
>         I have been using this code :
>
>         tfr = tfr_morlet(epochs,freqs=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)
>
>         But as far as I understood the EpochsTfr object that is returned by tfr_morlet() does not contain information about the  events, is it right?
>
>         Is it better then to create an EpochsTfr object for each of my 4 conditions  using :
>
>         tfr_cond_n = tfr_morlet(epochs[cond_n],freqs=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)
>
>         and then create vectors coding for each trials in each condition - or is there a way to transfer the events information from the original epoch data to the time-frequency data?
>
>         I then want to use:
>
>         gat.fit(tfr_epochs, y=tfr_epochs_events)
>
>
>
>         Thanks,
>         Claire
>
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