[Eeglablist] PREP pipeline & boundary events

lxraplikelx at qq.com lxraplikelx at qq.com
Sat Sep 12 01:27:23 PDT 2015

Dear Nima & Makoto
Thanks for the kind help!
Yeah, I thought it would be no big problem if I use merged dataset before PREP pipeline.
But, for the third question, I am still confused that whether it is ok for ica using interpolated data. Could you please explain whether there is difference in ica results between interpolated and uninterpolated data?
Thank you for your time! 
Li Xiang

From: Nima Bigdely Shamlo
Date: 2015-09-12 06:21
To: Makoto Miyakoshi
CC: lxraplikelx at qq.com; eeglablist
Subject: Re: [Eeglablist] PREP pipeline & boundary events
Dear Li

(i) You could, especially if the data a few seconds before/after the boundary event is not that important.
(ii) No (at least major) problems.
(iii) No, since each dataset will be interpolated to have the original number of channels (bad channels replaced by interpolated ones). You will have the same number of channels in all blocks. You will only need to do a 'pca' to the rank of the merged data (in double precision) befoe doing the ICA. 


On Wed, Sep 9, 2015 at 6:34 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu> wrote:
Dear Nima,

Here is a question for you about the PREP pipeline. Would you mind helping him?


On Fri, Sep 4, 2015 at 5:20 AM, lxraplikelx at qq.com <lxraplikelx at qq.com> wrote:
Dear eeglablist

I get into an awkward situation using PREP pipeline. The raw datasets I am dealing with are stored separately based on blocks. Firstly, I merged them up for every subject. Then I used PREP pipeline without ignoring boundary event. Since PREP does respect boundary events which represent discontinuities in dataset, PREP skiped all procedures. 

SO, the situation is that 
(i)when using merged dataset, whether should I just ignore boundary events or not? 
(ii)If I truely ignoring boundary events and run PREP, is there any problem? 
(iii)If I do not merge dataset and use datasets of single block, it will very likely lead to inconsistent number of channels in dataset of subject level due to different number of bad channels, and that will not be legitimate in the downstream behavior like ICA. 

What should I do? 

Thank you!

Li Xiang

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Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego

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