questions on ICA to remove EOG artifacts from MEG data

Robert Oostenveld r.oostenveld at FCDONDERS.RU.NL
Fri Mar 19 11:26:40 CET 2010

Dear Elisabeth,

please note that Saskia has just created a draft page on the wiki for  
using ICA to remove EOG artifacts from MEG data. Have a look here 
. The example there is using 151 channel CTF data avaoalble from the  
ftp server.

Feel free to improve the documentation on that page. Since the  
plotting of the topographies is tricky for the mixed planar/ 
magnetometer data that you are working on, I can imagine that  
especially using the topographies for identifying the components  
requires a neuromag specific approach.


On 19 Mar 2010, at 10:01, Elisabeth May wrote:

> Dear Masaki, dear Laurence,
> thank you a lot for your really quick and extensive replies to my  
> long list of questions!
> Masaki, you are right with your first point: so far I am not  
> completely sure that my plotting scripts correctly sepparates the  
> two gradiometer types. I do get the same results with my plotting  
> function and ft_topoplotIC. However, I use my own layout files for  
> horizontal and vertical gradiometers in both functions so there  
> might be something wrong with the layout files. I'll check those  
> again to be sure. I'll also try to scale gradiometers and  
> magnetometers and do ICA on all sensors together.
> Laurence, thank you for the plots, they will give me a better idea  
> of what to look for! I only want to use ICA to remove artifacts from  
> my data so I guess I might try the approach you suggested, too.
> I'll let you know as soon as I make any progress with my data!
> Thanks again and best wishes,
> Elisabeth
>> Dear Elisabeth,
>> I'm not very experienced with using ICA nor with using fieldtrip,  
>> but I do have some experience of obtaining reliable eyeblink  
>> spatial topographies from Neuromag 306 data (using SPM), so I  
>> thought I'd chip in with some advice...
>> On 18 Mar 2010, at 08:51, Elisabeth May wrote:
>>> Dear Fieldtrip users,
>>> I am trying to use independent component analysis  
>>> (ft_componentanalysis) to remove eye artifacts from my MEG data.  
>>> The dataset was recorded with the Neuromag 306 system and contains  
>>> two conditions with 40 trials each (one where a painful laser  
>>> stimulation was applied to the subject's right hand and one where  
>>> the same stimulation was applied to the left hand). So far, I am  
>>> trying to do the ICA just for the gradiometers on preprocessed  
>>> data (after removal of bad channels) and then plotting the  
>>> topographies of the components to identify and then reject  
>>> components representing the eye artifacts (as suggested in the  
>>> example script on the Fieldtrip webpage). I have used different  
>>> settings with the default option cfg.method = 'runica' (no PCA  
>>> before ICA, different numbers of principal components to be  
>>> retained (cfg.runica.pca), both conditions sepparately and  
>>> together...). However, I have never gotten component topographies  
>>> that I can clearly identify as components representing eye  
>>> artifacts (showing two sources right above the eyes), although  
>>> there are almost 200 eye artifacts in the 80 trials of this data  
>>> set (i.e. there was usually more than one EOG artifact in every  
>>> single trial). So I am still wondering about a few questions on  
>>> the exact settings for the ICA in my case and if I am missing  
>>> something really obvious:
>>>  * Is it correct to do the ICA on both horizontal and vertical
>>>    gradiometers together and then plot them sepparately to identify
>>>    artifacts?
>> Yes, this sounds sensible - there should be covariance across  
>> horizontal and vertical gradiometers during an eyeblink, which  
>> should help you in isolating a component, but of course they will  
>> have a very different spatial topography, which means you would  
>> want to plot them separately. I have attached a snapshot of an  
>> eyeblink component that I identified, in vertical and horizontal  
>> gradiometers (top left and top right), magnetometers (bottom left),  
>> and 60 EEG sensors (bottom right). Note that I didn't obtain this  
>> component with ICA, but with PCA of an 'average eyeblink' (see  
>> below).
>>>  * Should I do the PCA before the ICA and if so, how many principal
>>>    components should I retain?
>>>  * Should I do the ICA on both conditions together or sepparately
>>>    (since I expect different sources to be activated in both
>>>    conditions, e.g. one of the two primary somatsensory cortices
>>>    activated according to stimulation of the right or left hand)?
>>>  * Is it accurate enough to use complete trials for the component
>>>    analysis or do I have to extract and use just the parts  
>>> containing
>>>    EOG artifacts?
>> In my (limited) experience, I have had good success identifying  
>> eyeblink components with a slightly simpler method than ICA. I  
>> epoch the data with respect to well-identified eyeblinks (detected  
>> using EOG), then create an 'average eyeblink' using these epochs,  
>> so that I know what the timecourse of an eyeblink should look on  
>> average look like. I then just run a simple PCA on this 'average  
>> eyeblink', and normally the eyeblink is well captured by the first  
>> (and sometimes second) spatial component of this PCA. I then use  
>> this PCA map just as you might use an ICA map, i.e. regress it out  
>> of the raw (original) data. As a sanity check, you can correlate  
>> the timecourse of this component against the EOG channel, and this  
>> should correlate quite nicely. You can also try recomputing the  
>> average eyeblink after you have regressed the component out of the  
>> data, in order to see that it has been effective in removing most  
>> of the artefact.
>> I find this a bit simpler than ICA, and maybe comes with a few  
>> advantages. First, it doesn't use all the data to derive the  
>> components, only the data epoched around EOG-detected eyeblinks, so  
>> it is quite a bit faster on big datasets. Second, by creating an  
>> average eyeblink, it removes any variability in the data driven by  
>> other boring stuff (like the brain :) ), so it should pretty much  
>> always be the component which explains the most variance in the  
>> 'average' eyeblink (i.e. the first principal component). Finally,  
>> it is important to note that although the method requires you to  
>> detect /some/ eyeblinks using the EOG channel, it is still robust  
>> against you failing to detect /all/ eyeblinks, as the regression is  
>> done on the raw data, rather than just on the epochs where you have  
>> already detected the blinks.
>> However, there are some caveats; the first being that you assume  
>> that all eyeblinks look similar (in my experience they do), and  
>> perhaps more importantly that the component doesn't change  
>> (spatially) during the course of the experiment - with Neuromag  
>> data, you might be able to get around this to some extent by using  
>> Maxfilter for movement compensation. I think these caveats both  
>> also apply to ICA, but I'd be interested to hear other people's  
>> thoughts (particularly those who have more experience with ICA).  
>> Finally, I guess if you are interested in isolating interesting  
>> (brain) signals as well as boring (noise) signals simultaneously,  
>> then ICA is the way to go. I use this method just to remove  
>> eyeblinks before I do subsequent processing.
>>>  * And finally, are the component topographies usually sufficient to
>>>    identify eye artifacts?
>> I find with this method they are very easy to identify, and I  
>> imagine they would be reasonably straightforward in ICA also?  
>> Again, perhaps someone with more ICA experience can comment...
>>> Any help or advise on this would be greatly appreciated! Thanks in  
>>> advance,
>>> Elisabeth
>> Hope this helps,
>> Laurence
>> ===========================================
>> Laurence Hunt, DPhil Student
>> Centre for Functional MRI of the Brain (FMRIB), University of Oxford
>> lhunt at <mailto:lhunt at>
>> Phone: (+44)1865-(2)22738
>> ===========================================
>> ----------------------------------
>> The aim of this list is to facilitate the discussion between users  
>> of the FieldTrip toolbox, to share experiences and to discuss new  
>> ideas for MEG and EEG analysis.
> -- 
> Dipl.-Psych. Elisabeth May
> Universitätsklinikum Düsseldorf
> Institut für Klinische Neurowissenschaften und Medizinische  
> Psychologie
> Universitätsstr. 1
> 40225 Düsseldorf
> Tel: +49 211 81-18075
> ----------------------------------
> The aim of this list is to facilitate the discussion between users  
> of the FieldTrip  toolbox, to share experiences and to discuss new  
> ideas for MEG and EEG analysis. See also 
>  and

The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also and

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