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

[JR KING]

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> 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>
> 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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