[FieldTrip] ICA/PCA EOG artifact removal

[Raquel Bibi]

Dear Robert,
Over a year ago I tried to use the suggested method of the lower sample rate and project onto my original data. I had problems. Can you provide a little snippet of the projection?

Best,

Raquel
Sent from my iPhone

> On Oct 10, 2013, at 4:43 AM, Robert Oostenveld <r.oostenveld at donders.ru.nl> wrote:
> 
> Hi Craig,
> 
> Let me forward this to the email discussion list.
> 
> 
>> On 9 Oct 2013, at 23:27, CR wrote:
>> 
>> Hi Robert,
>> I wanted to see what your thoughts were on the merits of 2 different methods of removing blinks.  I have a 12 minute resting state segment of data, so it has required me to do some things a little differently.
>> 
>> Method 1:  ICA
>> 
>> I break the 12 minute segment into 2 second intervals, since doing ICA on the whole segment gave a poor result.  
> 
> why does it give you a poor result? Has the subject been moving? Is there something else that makes the data not compatible with the stationary mixing assumption? 
> 
> Or is it the difference in the preprocessing? 12 minutes of data represented in one segment can have drift, whereas 12 minutes of data represented in 2 second snippets will not have the drift  (assuming you use the default cfg.demean=yes). Doing a low-pass filter on the continuous data would have a similar effect as segmenting it and demeaning the 2 sec snippets.
> 
>> I apply the resulting unmixing matrix to the 12 minute segment and correlate each component with the EOG to find the most relevant components, and reject these based on a threshold.
> 
> so a bit like
> http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_eog_artifacts
> with the correlation method of
> http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_ecg_artifacts
> 
> 
>> Method 2: PCA
>> 
>> I do a timelock analysis based on the blink onset point returned by the eyelink system.  I then PCA the resulting blink ERF.  I then reject the component(s) that account for say 98% of the total variance.
>> 
>> Obviously option 2 is much faster.  What do you see as the relative merits/problems with the techniques?  Technique 1 is largely what the FT tutorials suggest, so what about method 2?
> 
> Option 2 makes the large variance component orthogonal to the remainder of the components, whereas in option 1 the eye component and frontal brain components are both estimated, not orthogonal, and removing of the eye component does not remove the frontal brain component.
> 
> Option 1 is better, as it is less aggressive in removing brain components.
> 
> If speed is a concern, you could do
> - ft_resampledata to e.g. 250 Hz or even less, estimate the components based on that and project them out of the original high Fsample data.
> - do ft_componentanalysis on a subset of the data (say every 4th data segment after cutting it in pieces), and project them out of the original segmented data
> - a combination of the two
> - try out anothe rica algorithm (fastica versus runica)
> - try out with the options of the ica algorithm, esp the stopping options
> - get a faster computer
> 
> best regards
> Robert
> 
> 
> 
> 
> 
> 
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