[Eeglablist] Artifacts after removal of ICA component

[Makoto Miyakoshi]

Dear Eric,

> How would you go about extracting N-1 components?

You should identify it by using envelope-topography analysis by envtopo().
You can perform it from GUI, but the GUI menu item name is very confusing.
Probably it's faster to use Google to search for 'envtopo'.

Makoto

On Sun, Dec 25, 2016 at 7:50 AM, Eric HG <erichg2013 at gmail.com> wrote:

> Thanks a lot for the responses!
>
> How would you go about extracting N-1 components? I can't see it under
> pop_runica.
>
> Best,
>
> Eric
>
> On Sat, Dec 10, 2016 at 8:49 AM, Iman Mohammad-Rezazadeh <
> irezazadeh at ucdavis.edu> wrote:
>
>> You may want to see https://sccn.ucsd.edu/pipermai
>> l/eeglablist/2014/007810.html
>>
>> -Iman
>>
>>
>>
>> *-------------------------------------------------------------*
>>
>> *Iman M.Rezazadeh, Ph.D*
>>
>> Semel Intitute, UCLA , Los Angeles
>>
>> & Center for Mind and Brain, UC DAVIS, Davis
>>
>>
>>
>>
>>
>>
>>
>> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at scc
>> n.ucsd.edu] *On Behalf Of *Ahmad, Jumana
>> *Sent:* Friday, December 9, 2016 10:02 AM
>> *To:* Eric HG <erichg2013 at gmail.com>; eeglablist <
>> eeglablist at sccn.ucsd.edu>
>> *Subject:* Re: [Eeglablist] Artifacts after removal of ICA component
>>
>>
>>
>> Hi Eric,
>>
>> Did you reduce the rank of your data by 1 before running ICA? I would do
>> Matlab rank(EEG.data(:,:)) and check it is not 1 less than the number of
>> your channels in ICA. If it is the same and you are not rank deficient. If
>> you are then you can perform channel rejection to match EEG.nbchan with
>> rank(EEG.data(:,:)).
>>
>> Average referencing reduces the rank of the data by 1. When you
>> rereference to average,  the sum of all your channels is 0 at each time
>> point.  One channel is therefore a linear combination of the others, which
>> implies statistical dependence, therefore the dimensionality of your data
>> is reduced  by one after averaging. I always average reference after ICA.
>>
>> Average referencing before means you can only extract N-1 independent
>> components after this.  This is fine as long as you remember to extract 1
>> component less than you would have before average reference.
>>
>> Best wishes,
>>
>> Jumana
>>
>>
>>
>>
>>
>> *From:* eeglablist-bounces at sccn.ucsd.edu [mailto:eeglablist-bounces at scc
>> n.ucsd.edu <eeglablist-bounces at sccn.ucsd.edu>] *On Behalf Of *Eric HG
>> *Sent:* 08 December 2016 00:39
>> *To:* eeglablist <eeglablist at sccn.ucsd.edu>
>> *Subject:* [Eeglablist] Artifacts after removal of ICA component
>>
>>
>>
>> Hi everybody,
>>
>>
>>
>> I've had some trouble with removal of components using ICA (I'm currently
>> using ICA infomax). After I remove a component there seems to be induced
>> high frequency noise in the dataset.
>>
>>
>>
>> The data is referenced to average before applying ICA.
>>
>>
>>
>> Does anyone know why that is happening?
>>
>>
>>
>> Best,
>>
>>
>>
>> Eric
>>
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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