[Eeglablist] IClabel help

Bruzadin Nunes, Ugo ugob at siu.edu
Mon Jul 15 10:29:45 PDT 2019


Dear Makoto,

Thank you so much for the reply! I haven't use the STUDY function yet, I'll check it out and see about this cluster function!

Thanks again,

Ugo Bruzadin Nunes, M.A.
PSYC 312 Instructor - Sensation and Perception
Brain and Cognitive Sciences Ph.D Program || Department of Psychology

Southern Illinois University - Carbondale
________________________________
From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
Sent: Friday, July 12, 2019 10:09 PM
To: Bruzadin Nunes, Ugo
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] IClabel help

Dear Ugo,

I have never been the official anything of SCCN, but here is my personal opinion.

> 1.  At what percentile does the team considers good enough to remove? 95% and above? 99%?

I would use EEG probability of 0.7 for the accepting criterion.

> 2.  Do the team advises to use other data cleaning processes such as CORRMAP, or can I use IClabel only to clean the data?

I would use ICLable with 0.7 for EEG or my PSD-based semi-manual IC rejection.
https://sccn.ucsd.edu/wiki/Std_selectICsByCluster#As_a_group-level_filter_to_manually_exclude_non-EEG_ICs

But for the application, use this one. This is the updated one.
https://sccn.ucsd.edu/wiki/Std_clust2ch

> 3.  Could I theoretically use it in a loop until I find something above 95% to remove?

Oh are you talking about identifying artifacts?
Hmm, it is actually more fuzzy than identifying brain components. You would not achieve completely satisfactory results even if you try hard to find the magical threshold. So unfortunately it is up to you. One good thing is that in theory no one can blame you for the choice of the threshold. Just choose any number which you can take responsibility.

Makoto


On Fri, Jul 12, 2019 at 9:53 PM Bruzadin Nunes, Ugo <ugob at siu.edu<mailto:ugob at siu.edu>> wrote:
Hi,

I have three quick questions about the new IC label plugin.

  1.  At what percentile does the team considers good enough to remove? 95% and above? 99%?
  2.  Do the team advises to use other data cleaning processes such as CORRMAP, or can I use IClabel only to clean the data?
  3.  Could I theoretically use it in a loop until I find something above 95% to remove?

Thanks a lot,

Ugo Bruzadin Nunes, M.A.
PSYC 312 Instructor - Sensation and Perception
Brain and Cognitive Sciences Ph.D Program || Department of Psychology

Southern Illinois University - Carbondale
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--
Makoto Miyakoshi
Assistant Project Scientist, Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego


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