[Eeglablist] Artifacts after removal of ICA component

[Tarik S Bel-Bahar]

Hello Eric, quick notes below for you regarding extracting n-1 components.
This may already be specified in makoto's recommendations page.

The flag/option within pop_runica and runica functions is simply 'pca',
'your N-1' number.

See the details on  options for the functions pop_runica and runica,
available by clicking help I believe in the eeglab gui.
The info on these functions is also available via googling or via help/doc
in matlab command line, as for all other matlab functions (usually).

Note you can also drop a channel first and then let ica run without
pca (which for some reason tends to work better for me).
You may enter the pca option and other options in the window for extra
commands in the pop_runica GUI. Alternatively,
if you haven't before, you can run ICA once, then use the eegh command to
get the command line version, and then add the pca option.




On Sun, Dec 25, 2016 at 10:50 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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