[Eeglablist] marking bad channels and interpolating at different stages of preprocessing & ICA

[Raquel London]

Hi Makoto,

Thank you very much for these extra comments. Yes, rank is a funny thing!
Thanks also for having your pre-processing pipeline up, it was a very
useful resource for me.

cheers,
Raquel

--
Dragón de la Patagonia
Ramon Barros Luco 688
Puerto Natales - Patagonia - Chile
Oficina: +56-9-94022038
Skype: raquel.london


On Tue, Oct 18, 2016 at 4:16 AM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
wrote:

> Dear Raquel,
>
> > 1.-
> I want to mark channels as bad, and thereby exclude them from the average
> reference first. Then run ICA, remove components, and only then interpolate
> the removed channel(s). Does this make sense? In eeglab, once I remove a
> channel with edit > select data > channel range (remove), I cannot find a
> way to interpolate it anymore. Is there a way around this issue?
>
> I updated channel rejection and average reference parts in this wiki page.
> https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline
>
>    % Keep original EEG.
>    originalEEG = EEG;
>
>    % Interpolate channels.
>    EEG = pop_interp(EEG, originalEEG.chanlocs, 'spherical');
>
> > 2.A
> I have 64 channels + 4 externals, so I compute the average reference over
> the 64 channels.
> Because I have an average reference, the rank of the data goes down by 1,
> so I use EEG = pop_runica(EEG , 'extended',1,'interupt','on','*pca',67*);
> to reduce the number of ICs to match the rank. Is this right?
>
> Correct.
>
> > 2.B
> Now, if I eliminate, say, 1 channel, I would have to reduce the rank by
> one more, right? But, what if I *did *interpolate before running the ICA,
> I would still have to reduce the number of IC's to 66, is that correct?
>
> No, if you remove a channel when your data rank is numberOfChannels-1,
> your data are full rank again and no longer need to perform pca dimension
> reduction.
>
> Data rank is a funny thing because it is not explicitly visible, right?
> See also a snippet from that wiki page. Basically, you can ask what data
> rank is by running Matlab function rank() IF your data is 'double' (you can
> change it from EEGLAB option, default is 'single' so be careful).
>
>    % Discard channels to make the data full ranked.
>    dataRank = rank(EEG.data');
>    channelSubset = loc_subsets(EEG.chanlocs, dataRank);
>    EEG = pop_select( EEG,'channel', channelSubset{1});
>    EEG = pop_chanedit(EEG, 'eval','chans = pop_chancenter( chans, [],[]);');
>
>
> Makoto
>
>
>
> On Wed, Sep 7, 2016 at 10:41 AM, Raquel London <
> raquel at dragondelapatagonia.com> wrote:
>
>> Hi all,
>>
>> I have a few questions about how to handle bad channels & ICA in eeglab.
>>
>> 1.-
>> I want to mark channels as bad, and thereby exclude them from the average
>> reference first. Then run ICA, remove components, and only then interpolate
>> the removed channel(s). Does this make sense?
>> In eeglab, once I remove a channel with edit > select data > channel
>> range (remove), I cannot find a way to interpolate it anymore. Is there a
>> way around this issue?
>>
>> 2.A
>> I have 64 channels + 4 externals, so I compute the average reference over
>> the 64 channels.
>> Because I have an average reference, the rank of the data goes down by 1,
>> so I use EEG = pop_runica(EEG , 'extended',1,'interupt','on','*pca',67*
>> );
>> to reduce the number of ICs to match the rank. Is this right?
>>
>> 2.B
>> Now, if I eliminate, say, 1 channel, I would have to reduce the rank by
>> one more, right? But, what if I *did *interpolate before running the
>> ICA, I would still have to reduce the number of IC's to 66, is that correct?
>>
>> Thanks so much in advance for your comments!
>>
>> Raquel
>>
>>
>>
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>
>
>
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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