[Eeglablist] Time-Freq Decomp. and Pop_epoch - avoid edge artifacts

[Makoto Miyakoshi]

Dear Luis,

I can think of two things you want to be careful about.

1. Loosing data of half width of the window from both ends of your TF
data (assuming that you apply it to the continuous data)
2. Difference in time-resolution between TF data and your EEG sampling rate.

To make it simple, I recommend you simply discard the first and last
epochs of TF data to address the issue 1 above. Also, for the sake of
simplicity you should choose time-resolution of TF data as an integral
multiple of your EEG sampling rate. For example, if your sampling rate
is 250 Hz, which is 4 ms, then your TF data should have 4, 8, or 12 ms
resolution. With these two preparations, it would be easier to copy
the temporal structure of your EEG data to your TF data.

I'm not sure if you can use pop_epoch for epoching TF data. Probably
you should write your own code to epoch your TF data, which should not
be too difficult. In any case, the important thing is to have
corresponding event markers in your TF data.

If you have further question let us know.

Makoto



2012/7/10 Luis R. Piloto <lpiloto at princeton.edu>:
> Hello,
>
> I was wondering how I can perform time-frequency decomposition on my entire dataset and *then* split the resulting data into different epoch types based on port codes.  I know how to do each of these steps individually i.e. time-freq. decomp with newtimef() and splitting the data with pop_epoch(), but I have no clue how to use the output of newtimef with pop_epoch.  I'd like to split the data AFTER performing the time-frequency decomposition because the literature says that you get edge-artifacts from time-freq decomposition and performing it on the entire segment is obviously preferable to introducing edge-artifacts to each split segment.  Thanks in advance.
>
> Regards,
> Luis Piloto
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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