[Eeglablist] Dealing with bad channels in merged datasets
ericksonb.eng at gmail.com
Sat Apr 19 21:47:28 PDT 2014
Makoto, thanks for this detailed response. Best, Brian
On Tue, Apr 15, 2014 at 1:02 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:
> Dear Brian,
> > Do you know if there has been a systematic investigation of this issue
> of combining data between sessions?
> There is no systematic investigation as far as I know, partly because it's
> more like theoretically evident issue. ICA produces a static spatial
> filter, and if you change the relative positions between electrodes and
> sources then you'll need another filter, which violates the stationarity
> > We are very precise in our electrode placement.
> But as I told you even re-gelling can screw it up... so we don't recommend
> > Our purpose in this study is to remove artifactual components and then
> return to the scalp map space - not to continue on to dipole modeling, etc.
> (though I intend to work on dipole modeling methods in the future).
> I see.
> > You mention the problem of probable creation of multiple artifact ICs in
> the concatenation method I described. We do see e.g. multiple blink
> components in some resting state files I have concatenated this way, but
> not for others. And in fact, I often see such duplicate artifactual ICs in
> longer datasets that are all from one session, so it seems there are other
> generators of this multiple IC problem?
> There are two issues. One is the subspace issue. For example, you may find
> multiple alpha, mu, EOG, and EMG components. These subspace are
> intradependent but inter-independent. This is a normal behavior of ICA, and
> actually applying ICA means applying independent subspace analysis. I'm not
> a specialist in this topic so if you need I can ask Jason Palmer, who wrote
> AMICA, to take over this query. The other issue is the cap/electrode shift
> by accident, lapse of time (since cap is elastic and tightened with rubber
> band etc...) which is a problem for ICA.
> > If the evidence really suggests that reapplication of the cap (multiple
>> sessions) makes concatenation impossible,
> Yes, as I said it is theoretically clear.
> > I see a few ways I could go forward. The first is to do PCA as you
> mentioned. This reduces the dimensionality of the data and thus the number
> of time points necessary for a good decomposition.
> Yes, although Scott Makeig seems to have changed his preference on it over
> this years, and now he is more reluctant to use PCA and I have never heard
> he recommend PCA before ICA even in seemingly appropriate situations (more
> optimism in minimum datapoint estimate for ICA).
> > Another method for reducing dimensionality would be to simply
> down-sample. I calculate that we would achieve the recommended 30
> datapoints per channel^2 necessary for good ICA decomposition if we
> down-sampled to 50 channels from our current 84. Could you comment on the
> merits of this method vs. PCA?
> If you downsample channels, you'll have problems in recovering the scalp
> topos with full channels, although just discarding channels is the most
> straightforward solution in a sense. I would say applying PCA is better.
> > Particularly, what is the nature of any distortion caused by running
> PCA? Since it drops variance.
> After PCA, when you recover channel signals from ICA activations you'll
> see different data (of course, since it's a lossy compression). Though you
> want to compare before and after PCA application though, since the
> difference should be fairly small.
> I appreciate you consider this process seriously and your carefulness.
> 2014-04-09 7:49 GMT-07:00 Erickson <ericksonb.eng at gmail.com>:
>> Thanks for your response. Do you know if there has been a systematic
>> investigation of this issue of combining data between sessions? We are very
>> precise in our electrode placement. Our purpose in this study is to remove
>> artifactual components and then return to the scalp map space - not to
>> continue on to dipole modeling, etc. (though I intend to work on dipole
>> modeling methods in the future).
>> You mention the problem of probable creation of multiple artifact ICs in
>> the concatenation method I described. We do see e.g. multiple blink
>> components in some resting state files I have concatenated this way, but
>> not for others. And in fact, I often see such duplicate artifactual ICs in
>> longer datasets that are all from one session, so it seems there are other
>> generators of this multiple IC problem?
>> Another option I see would be to split the file into two sets with 84/2 =
>> 42 channels each (evenly distributed across the scalp). I would run ICA on
>> both of these datasets independently, remove artifacts via ADJUST, and then
>> recombine the corrected waveforms. Does this violate the assumptions of
>> ICA? Again, I am only interested in removing artifacts, not dipole modeling
>> etc. at this time. I appreciate your comments!
>> - Brian
>> On Fri, Apr 4, 2014 at 10:36 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:
>>> Dear Erickson,
>>> > However, since the setup is identical between sessions, we are merging
>>> the datasets from each subject's 4 sessions into a large dataset,
>>> Sorry to point this out, but you can't do this because electrode cap
>>> applications were different for each of 4 recordings, right?
>>> Even a re-gelling can move a channel and create 'another' IC which shows
>>> 'blocked' ERPimages...
>>> Run ICA separately for each of 4 recording sessions. If you don't have
>>> enough datapoints, you can run pca to reduce dimensions. For infomax, after
>>> 'extended', 1, continue 'pca', 20 for example.
>>> 2014-04-03 13:02 GMT-07:00 Erickson <ericksonb.eng at gmail.com>:
>>>> I am creating a data pipeline to process resting state eeg with ADJUST,
>>>> and I've run into a conceptual problem with bad channels.
>>>> Our study involved the collection of resting state data across several
>>>> days. Individually these resting state files are not long enough to meet
>>>> the data requirements of ICA (datapoints/channels^2 > 30 or 40). However,
>>>> since the setup is identical between sessions, we are merging the datasets
>>>> from each subject's 4 sessions into a large dataset, running ICA on this
>>>> dataset, and then applying the ICA weights back to the 4 individual
>>>> datasets. We have no reason to believe that the EEG signature of a blink
>>>> would be any different between sessions, nor is the cognitive task
>>>> different (resting state) so this merge seems to be a nice way to take care
>>>> of the problem.
>>>> However, my issue is that if there is a bad channel in one of these 4
>>>> datasets, and I remove it, the dimensionality of the datasets is different
>>>> and they can't be merged, much less used for ICA. Normally I would
>>>> interpolate to get those channels back, but it's not correct to interpolate
>>>> before ICA.
>>>> Currently, my solution is just to accept the loss of data. If a channel
>>>> is bad in any of the 4 original datasets, I have to remove it from all 4
>>>> original datasets. Then I can merge them and run ICA on the merged file.
>>>> Then I apply those ICA weights to the 4 original datasets individually and
>>>> run ADJUST. However, I'm obviously throwing away a lot of data here so I
>>>> would like to know if there is a better way.
>>>> Can anyone suggest an option I am not thinking of to solve this issue?
>>>> Thanks for your time! - Brian
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>>> Makoto Miyakoshi
>>> Swartz Center for Computational Neuroscience
>>> Institute for Neural Computation, University of California San Diego
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
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