[FieldTrip] ICA/PCA EOG artifact removal

[Raquel Bibi]

Thanks Jim and Craig!


On Thu, Oct 10, 2013 at 11:35 AM, Herring, J.D. (Jim) <
j.herring at fcdonders.ru.nl> wrote:

> Dear Raquel,
>
> What you could do, for example, is:
>
>
>
> % Save your data to disk
> Save('data','data','-v7.3'); % Saves the variable data to the file
> data.mat. The '-v7.3' switch allows the file to be larger than 4gb, if
> necessary.
>
> % Resample your data
> cfg = [];
> cfg.resamplefs = 250; % Or less
>
> data_resampled = ft_resampledata(cfg, data);
>
> % Clear original data from memory
> Clear data
>
>
> % Perform ICA on resampled data
> cfg = [];
> cfg.method = 'runica'; % or 'fastica', for example.
>
> comp = ft_componentanalysis(cfg, data_resampled);
>
> % clear resampled data
> Clear data_resampled
>
> % load original data
> Load('data.mat');
>
> % Use previously calculated unmixing matrix on original data
> cfg = [];
> cfg.unmixing = comp.unmixing; % copy unmixing matrix to new configuration
> structure
> cfg.topolabel = comp.topolabel; % copy channel labels to new config
> structure.
>
> comp = ft_componentanalysis(cfg, data);
>
>
>
> All the saving and clearing in between might not be necessary depending on
> how much RAM you have and on the size of your data.
>
> Best,
>
> Jim
>
>
>
>
> -----Original Message-----
> From: fieldtrip-bounces at science.ru.nl
> [mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Raquel Bibi
> Sent: donderdag 10 oktober 2013 15:36
> To: FieldTrip discussion list
> Cc: FieldTrip discussion list; CR
> Subject: Re: [FieldTrip] ICA/PCA EOG artifact removal
>
> 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_i
> ca_to_remove_eog_artifacts
> > with the correlation method of
> >
> http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_i
> ca_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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> > fieldtrip at donders.ru.nl
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>
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