<div dir="ltr"><div><div><div>Hi Claire,<br><br></div>Jaakko's solution is a possibility (e.g. this is what I used here <a href="http://dx.doi.org/10.1016/j.neuron.2016.10.051">http://dx.doi.org/10.1016/j.neuron.2016.10.051</a>), but this isn't optimal because the signal would be rectified at the sensor level. <br><br>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.<br><br></div>Best,<br><br></div>Jean-Rémi<br></div><div class="gmail_extra"><br><div class="gmail_quote">On 3 March 2017 at 03:41, Jaakko Leppakangas <span dir="ltr"><<a href="mailto:jaeilepp@gmail.com" target="_blank">jaeilepp@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Hi Claire,<span class=""><div><br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><span style="font-size:12.8px">tfr_cond_n = tfr_morlet(epochs[cond_n],</span><span style="font-size:12.8px">freq<wbr>s=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)</span></blockquote><div><br></div></span><div>This is the way I would do it. The events information is not stored to the EpochsTFR.</div><span class="HOEnZb"><font color="#888888"><div><br></div><div>-Jaakko</div></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Mar 2, 2017 at 9:44 PM, Claire Braboszcz <span dir="ltr"><<a href="mailto:claire@guakamole.org" target="_blank">claire@guakamole.org</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hello,<br>
<br>
I want to create single trials time-frequency epochs to later perform time-frequency decoding.<br>
I am wondering what is the correct way for creating my epochs. I have EEG data with 4 types of events.<br>
I have been using this code :<br>
<br>
tfr = tfr_morlet(epochs,freqs=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)<br>
<br>
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?<br>
<br>
Is it better then to create an EpochsTfr object for each of my 4 conditions using :<br>
<br>
tfr_cond_n = tfr_morlet(epochs[cond_n],freq<wbr>s=freqs, n_cycles=n_cycles, use_fft= True, return_itc= False, decim=decim, average= False)<br>
<br>
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?<br>
<br>
I then want to use:<br>
<br>
gat.fit(tfr_epochs, y=tfr_epochs_events)<br>
<br>
<br>
<br>
Thanks,<br>
Claire<br>
<br>
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