[Eeglablist] Question on EEGLAB tutorial
Makoto Miyakoshi
mmiyakoshi at ucsd.edu
Fri Jun 7 13:38:59 PDT 2013
Dear Venkatesh,
Check out Delorme et al., 2007 NeuroImage.
What I would suggest is
1. Before ICA, threshold data with +/- 300-500 uV. See to it that it does
NOT catch eye blinks.
2. Run ICA.
3. If you want, apply improbability test with SD 6-8 for each channel and
2-3 for all channels.
Through these steps, reject 1-5% of data; if you think you have to discard
more than 10%, you may got an unfortunate data (but still ok). If you have
to discard more than 30% of the data, that is no good according to the
guideline paper (Picton et al., 2000 Psychophysiology)... but that's still
not the law though.
Makoto
2013/6/6 Venkatesh Rajagopalan <VRajagopalan at kesslerfoundation.org>
> Hi All,
>
>
>
> I am new to EEG. I am going through your EEGLAB tutorial. I working on one
> of the sample data sets provided with the EEGLAB. The tutorial is good, but
> I am unable to understand under what circumstances one has to perform a
> certain set of steps and how to choose the thresholds etc. In specific
>
>
>
> I am unable to find clear instructions for how to clean the data. For
> instance it will be helpful for me to have a data with an artifact and if
> the tutorial could show how their routine for example what will be the
> result when you do the step "Rejecting improbable data" on that artifact or
> how the step is used to reject an artifacts and what kinds of artifacts can
> be rejected and what cannot be rejected. Similarly how to choose a
> threshold for "Rejecting abnormal trends" and how different thresholds
> affect the data etc.
>
>
>
> To me for a beginner the tutorial is very vague. Any suggestions to me
> regarding how I can first understand/determine what kind of preocessing
> steps is necessary for my data set or the sample data set that you have
> supplied with the software.
>
>
>
> Any help will be greatly appreciated.
>
>
>
> Thanks
>
> Venkat
>
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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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