[Eeglablist] IClabel help

[Arnaud Delorme]

Also, the new version of IClabel 1.2 (released today) will allow you to flag components for a whole study.

Arno

> On Jul 15, 2019, at 10:29 AM, Bruzadin Nunes, Ugo <ugob at siu.edu> wrote:
> 
> 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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