[Eeglablist] MATLAB for EEGLAB

Dr Devvarta Kumar devvarta.k at nimhans.ac.in
Wed Feb 8 17:43:27 PST 2017


 Hi 

 I am buying MATLAB for running EEGLAB. Please let me know whether only basic MATLAB is to be procured or any toolbox is also required to run EEGLAB?

 Best wishes

 Devvarta Kumar


 ----- Original Message -----
 From: Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
 Date: Sunday, January 22, 2017 1:21 pm
 Subject: Re: [Eeglablist] ICA question
 To: "Ahmad, Jumana" <jumana.ahmad at kcl.ac.uk>
 Cc: "eeglablist at sccn.ucsd.edu" <eeglablist at sccn.ucsd.edu>



> Dear Jumana,
> 
> > For data rank reasons, I interpolated and average referenced after ICA, using runica. When re-referencing I deleted the ICA activation matrix and the ICA weights, because of course I now have interpolated channels which do not have ICA weights associated with them.
> 
> Average referencing is just to subtract a fixed value from all channels/ICs, so I thought it is harmless. You don't need to delete ICA-calculated items.
> 
> > I only wanted to do ICA for eye blink rejection, and it worked perfectly and I did not have any ICA corruption (trade offs or rank deficiency). I compared the data before and after ICA and the level of noise was the same. 
> 
> Do you mean that if you don't reject ICA-calculated items, it destroys data? I'm interested in testing it. It's more likely it does not happen. If you say you reject channels after ICA, it's more likely to result in more complicated situation, depending on the path you follow using EEGLAB.
> 
> By the way what do you mean trade-off?
> 
> > I am working with an extremely large dataset and do not ideally want to re-do these steps, especially because I am mainly working with raw channel ERP data. 
> 
> It's a good opportunity for you to start using batch code. If you follow my instructions in my wiki pages, 100-200 datasets are nothing. I created a STUDY with nearly 1,000 datasets with no problem, so I can guarantee up to 1,000. If you eventually want to go back to channels, then after ICA selections using std_selectICsByCluster(), perform channel statistics. In this case, I think interpolation will definitely help you because it'll eliminate missing value problems reasonably.
> 
> > this time on the Chanel data where ICA blink components have been removed
> 
> Even if you run ICA on IC-rejected clean data, you won't obtain cleaner data. What happens is that you'll get exactly the same decomposition. Try it with one subject to see it.
> 
> > Identify noisy channels
> > Run ICA on clean channels
> > Interpolate electrodes to give the same 60 electrodes per person
> > Average reference to the 60 electrodes
> > Compute ERPs on channel data
> > Then re-run ICA, and do any further analysis on component data.


> 


> Yeah it works. Most likely, the last ICA will produce the same results except post average-reference components show zero-mean scalp topos.

> 


> Makoto

> 


> 


> 


> 

> On Fri, Jan 13, 2017 at 6:20 AM, Ahmad, Jumana <jumana.ahmad at kcl.ac.uk> wrote:




> Dear EEGlab. 

> For data rank reasons, I interpolated and average referenced after ICA, using runica. When re-referencing I deleted the ICA activation matrix and the ICA weights, because of course I now have interpolated channels which do not have ICA weights associated with them.

> 


> I only wanted to do ICA for eye blink rejection, and it worked perfectly and I did not have any ICA corruption (trade offs or rank deficiency). I compared the data before and after ICA and the level of noise was the same. 

> 


> I am working with an extremely large dataset and do not ideally want to re-do these steps, especially because I am mainly working with raw channel ERP data. 

> 


> However, for future analysis, if I did want to work with component data, could I re-run ICA for a second time, this time on the Chanel data where ICA blink components have been removed, and where the interpolated electrodes and the new reference implemented (this data should already be clean from blinks etc).

> 


> Overal this would look like:

> 


> Identify noisy channels

> Run ICA on clean channels 

> Interpolate electrodes to give the same 60 electrodes per person

> Average reference to the 60 electrodes

> Compute ERPs on channel data

> Then re-run ICA, and do any further analysis on component data. 

> 


> 


> Please let me know if you can see any issues with this. I very much appreciate any advice. 

> Best wishes,

> Jumana 

> 






> ------------------------------> ------------

> Jumana Ahmad

> Post-Doctoral Research Worker in Cognitive Neuroscience 

> EU-AIMS Longitudinal European Autism Project (LEAP) & SynaG Study

> Room M1.26.Department of Forensic and Neurodevelopmental Sciences (PO 23) | Institute of Psychiatry, Psychology & Neuroscience | King’s College London | 16 De Crespigny Park | London SE5 8AF

>  

> Phone: 0207 848 5359| Email: jumana.ahmad at kcl.> ac.uk | Website: www.eu-aims.> eu | Facebook: www.facebook.> com/euaims
> 



> 
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> 

> -- 


> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego

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 =============================================
 Devvarta Kumar, Ph.D.

 Additional Professor
 Department of Clinical Psychology
 M. V. Govindaswamy Building
 National Institute of Mental Health and Neurosciences,
 Hosur Road,
 Bangalore,
 Karnataka-560029
 India

 Ph: +91-80-26995188
 http://www.nimhans.ac.in/users/dr-devvarta-kumar
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