[Eeglablist] re-concatenating epoched dataset

[Bernhardsson Jens]

Dear Makoto

It would be great if you could share your code. I am in the midst of moving rejection marked epochs from one dataset to another. The code I have done for this makes it very easy for mistakes, and it would be good to have a look at/use yours.

Best
Jens

-----Ursprungligt meddelande-----
Från: eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at sccn.ucsd.edu] För Makoto Miyakoshi
Skickat: den 28 januari 2013 17:44
Till: Pete Manza
Kopia: eeglablist at sccn.ucsd.edu
Ämne: Re: [Eeglablist] re-concatenating epoched dataset

Dear Pete,

I've been trying to find the best work around to do that, but haven't found it yet. What I did for the last time was that when epoching the data I epoched short (0 to 1000 ms) and long (-1000 to 2000), perform ICA and epoch rejection on the short-epoched data, copy the results to the long-epoched data, and create STUDY using the long-epoched one.
This is purposed to obtain good low-frequency resolution in ERSP/ITC as you mentioned (in this case, epochs could be overlapped).

You can copy ICA matrices via GUI, but you can't do so epoch and event information. You have to write your own code. I'd happy to share my code if you understand that it comes with no guarantee or support.

Makoto

2013/1/25 Pete Manza <pete.manza at gmail.com>:
> Hi all,
>
> I have fully processed .set files for a STUDY design that were epoched 
> around my stimulus -- but unfortunately, for ERSP calculation, the 
> epochs do not start early enough behind the stimulus (only -300ms, I'd 
> like to go back to -800ms or so).  I did not exclude any of the 
> continuous data in my original epoching procedure (i.e., my trials 
> were 7300ms long including the ITI, and I epoched from -300 to 7000ms).
>
> Is there a way to re-concatenate all of the trials of epoched data 
> into continuous data so I can re-epoch?
>
> Or better yet, is there a way to chop off the last 500ms of each epoch 
> and directly append them to the beginning of the following trial?  
> Either of these would be a much better option than going back and 
> re-doing most of my preprocessing :)
>
> I am not very experienced in matlab, so if anyone is willing to share 
> some code I'd greatly appreciate it.
>
> Thanks!
> Pete Manza
> Stony Brook University
> Integrative Neuroscience
>
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--
Makoto Miyakoshi
JSPS Postdoctral Fellow for Research Abroad Swartz Center for Computational Neuroscience Institute for Neural Computation, University of California San Diego _______________________________________________
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