[Eeglablist] ICs removal

Andreas Widmann widmann at uni-leipzig.de
Fri Oct 25 09:01:02 PDT 2019


Hi,

one more suggestion: To identify eye movement activity related ICs (presaccadic spike potentials, movements of the corneo-retinal dipoles, blink/eyelid) it helps a lot to include the recorded (monopolar) EOG channels into the topographies. In case locations were not co-registered and you use standard EOG channel locations it is sufficient to rename them to the corresponding labels as used in the EEGLAB provided lookup files and then lookup channel locations with the GUI. Typically the labels are LO1 and LO2 for HEOG and SO1 and IO1 for VEOG (at left eye; SO2 and IO2 for right eye).

IC2 activity as shown in the ERP-image plot might be compatible with PSP activity.

Hope this helps! Best,
Andreas

> Am 23.10.2019 um 20:47 schrieb Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:
> 
> Hi Paolo...
> 
> Some quick suggestions re your question....
> 1) if you can't remove the noise otherwise at collection or via a cleaning
> procedure
> and 2) if you have isolated the noise into mainly one IC,
> and 3) if the component is mainly noise
> and 4) if you have major clear alpha-dominated components that cover more
> variance and have good neural-IC properties..
> and 5) if the spectral component does not vary with your experimental
> conditions or groups of interest
> ...THEN it is probably safe to remove it, as long as you use the same rules
> for other subjects/files.
> 
> also, if you haven't, try your analysis with and without clean_rawdata
> plugin/function before ICA, and see what happens to the noise and the
> component.
> 
> Extra thoughts...
> As one often finds, ICs are mixed, as ICA is not a perfect/pure carver of
> spatial regularities in the surface potentials.
> Some groups keep all ICs except ones that are clearly artifactual. Some
> groups remove all ICs except those that are clearly neural.
> Some groups take a more cautious approach and keep mixed ICs, or as many as
> possible, to "preserve" potential loss of (neural) information. not many
> published comparisons exist.
> Please look into reliability procedures for ICA (e.g., relica, eegift), and
> comparisons of ICA and PCA approaches.
> Overall, one good strategy to try would be to remove any ICs that IClabel
> plugin says is under 90% on the neural classifier, and/or over ~15% on any
> artifact classifier.
> One can run SASICA for complementary ICA feature diagnostics.
> If you are beginning with ICA, one good strategy is to just follow the best
> 2 or 3 procedures from high-quality research groups that use ICA for
> purposes similar to yours (e.g., using our friend Google Scholar).
> .
> if you haven't had a chance to check out the IClabel classification site
> which has really good examples, tutorials, and training regarding ICs.
> 
> A useful resource to remember that helps me sometimes is the  googlable
> eeglablist archives, where one can find many interesting past thread
> related to one's topic of choice.
> 
> 
> 
> 
> On Tue, Oct 22, 2019 at 4:18 AM Paolo Antonino Grasso <
> paolo.grasso at iusspavia.it> wrote:
> 
>> Hi all,
>> I have a question about IC removal
>> In some cases I have found situations like the one described in the
>> attached figure where the signal is very noisy (although a PSD analysis do
>> not reveal the presence of electrical noise around 50 Hz) and ICA finds a
>> component that, if removed, eliminates most of the noise. However, this
>> component seems to retain some neural signal (as evident from the peak
>> around 12 Hz and from the ERP) and I don't know if it is appropriate to
>> remove it.
>> Do you have any idea of what is the origin of this component/noise and how
>> should I deal with it?
>> Thanks a lot for your replies
>> Paolo
>> --
>> *Paolo A. Grasso, Ph.D.*
>> *Postdoctoral Research Fellow*
>> *Scuola Universitaria Superiore IUSS Pavia*
>> 
>> Palazzo del Broletto
>> Piazza della Vittoria,15 - 27100 Pavia
>> www.iusspavia.it
>> 
>> Personal Web Page
>> <
>> http://www.iusspavia.it/people-research-fellows/-/asset_publisher/GFd4VCR7Erp7/content/paolo-a-grasso?redirect=%2Fpeople-research-fellows
>>> 
>> skype: paolograsso86
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to
>> eeglablist-unsubscribe at sccn.ucsd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
>> 
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to eeglablist-request at sccn.ucsd.edu
> 



More information about the eeglablist mailing list