[FieldTrip] ICA/PCA EOG artifact removal

[Alik Widge]

Thank you for posting this, as I'm in the middle of this same processing
step, and I've been pondering methods as well while following the
tutorials. (In my case, 60-channel EEG plus one bipolar EOG in a diagonal
configuration.) Your response to Craig contains something that's been
bugging me a little bit:

When doing the ICA on a test recording, I find (and it sounds like he finds
as well) that there is not a single component that captures eyeblinks well
(and I have tried various adjustments, such as altering number of
components via pre-PCA, clipping or not clipping out epochs that look to me
to have substantial slow eye-roll, turning runica.extended on/off). I do
get components that have the classic "pair of eyeglasses" or "single
eyeball" look... but I get four or five of them on a 58-component
decomposition, and that's before we talk about the components that are
almost pure 60-cycle noise or temporal EMG.

You seem to be telling us, in your comment about orthogonality and
percentage of variance captured, that this is actually a *good* thing,
because it reduces the chance of removing activity from the frontal pole.
Can you help me understand that a bit better? I've felt very nervous about
the sheer number of components I'm removing; it feels as though I'm killing
a big chunk of the dataset, and doing so somewhat blindly.


Thanks,

Alik Widge, MD, PhD
Massachusetts General Hospital
Charlestown, MA, USA
alik.widge at gmail.com
(206) 866-5435



On Thu, 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
>
>
>
>
>
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.science.ru.nl/pipermail/fieldtrip/attachments/20131010/bb9e4a17/attachment-0002.html>