[Eeglablist] R: ICA and dipfit: high residual variance

[Makoto Miyakoshi]

Dear Giovanni,

> 1) Should I avoid to use fs=500Hz when working with SIFT? From the
tutorial I understood that there could be problems with this analysis with
fs>500 but at this point I assume that also having fs=500Hz is risky.

Yes, avoid it if you are not sure.

> 2) When working with fs=250Hz, can I rely on the goodness of the model
validation results (whiteness, consistency, stability) even if the model
has been estimated without respecting the ratio parameters/datapoints?

I thought that Tim used to recommend users to use something like 100Hz.
This will lower the recommended model order accordingly, so you would not
have parameter/datapoint ratio issue.

Makoto


On Thu, Oct 13, 2016 at 8:48 AM, Giovanni Vecchiato <
giovanni.vecchiato at gmail.com> wrote:

> Dear Tarik and John,
>
> Thanks a lot for your replies.
> Your notes helped to obtain more plausible ICA results from the
> localization point of view.
>
> Actually, I was using that processing (HP @ 0.1Hz) to match the suggestion
> given by Tim Mullen to perform the connectivity analysis via SIFT, which is
> my final goal.
> He provided a sample data collected at 256Hz and just high pass filtered
> at 0.1Hz before performing the ICA, DIPFIT and the following connectivity
> analysis.
> That's why I initially opted for the same approach.
>
> However, following your suggestion, I found benefits from a high pass
> filtering at 1Hz and a spatial downsampling (by eliminating the two more
> outer circumferences of electrodes), and a low pass at 70Hz which actually
> helped to obtain lower residual variance from the DIPFIT analysis. I used
> the PCA before computing ICA because I don't have enough trials to do it on
> all components; anyway, by computing ICA on the whole session (and not on
> trials) I did not find any improvement of residual variance.
>
> After that, I start working on the connectivity analysis via SIFT and
> would like to share with the following observations.
> My data are recorded with fs=500Hz and, according to the rule
> (M^2p)/(NtW)<0.1, I collected Nt=40 trials to estimate a Vieira-Morf model
> with W=0.3s, M=6 independent components and assuming a model order of 16.
>
> By following the standard preprocessing of the tutorial, from the model
> order selection procedure I get the following values for the related
> criteria:
>
> - hq=13
> - aic=23
>
> I set p=16 (which is the limit order I could apply according to the above
> rule) but the whiteness tests fail. Then, I tried to "force" the model
> fitting by using a p=23 but again the results of the following whiteness
> tests are negative.
>
> Then, I repeated the analysis on the downsampled dataset at fs=250Hz.
> In this way, using the same parameters for the analysis, I get the
> following values for the model order selection criteria:
>
> - hq=10
> - aic=15
>
> which are too high to satisfy the ratio parameters/datapoints. However, in
> this case when I put p=15 the whiteness tests are positive for all the
> windows, the model is stable for all the windows and the average
> consistency is above the 80%. So I assume the following connectivity
> patterns are valid, although the model has been estimated without
> respecting the parameters/datapoints ratio.
>
> Therefore, my issues are the following:
>
> 1) Should I avoid to use fs=500Hz when working with SIFT? From the
> tutorial I understood that there could be problems with this analysis with
> fs>500 but at this point I assume that also having fs=500Hz is risky.
>
> 2) When working with fs=250Hz, can I rely on the goodness of the model
> validation results (whiteness, consistency, stability) even if the model
> has been estimated without respecting the ratio parameters/datapoints?
>
> Sorry for this long email, but hope to have been clear enough.
> Thanks again for your help.
>
> Best,
> Giovanni
>
>
> -----Messaggio originale-----
> Da: Tarik S Bel-Bahar [mailto:tarikbelbahar at gmail.com]
> Inviato: mercoledì 21 settembre 2016 20:05
> A: Giovanni Vecchiato
> Cc: eeglablist
> Oggetto: Re: [Eeglablist] ICA and dipfit: high residual variance
>
> Hello Giovanni, some notes below for you that should help a little.
> Let the list know of your future success for dipfit-residual-variance-
> reduction!
>
>
>
>
>
> ************************************************************
> ******************************************
> Your main question should probably be "are my ICs and the dipfit results"
> computed correctly and accurate ? After you're sure about that, then  think
> about "ways" to "decrease" residual variance of dipfits. If you haven't
> yet, please be sure to walk through the online eeglab tutorial doing the
> same steps with the eeglab tutorial data.
> This is useful for understanding how things should look and what to expect.
>
> Review published articles, and poll current researchers, about what is the
> usual, acceptable, and OK levels of residual variance.
>
> You should be careful about using both PCA and ICA. My understanding is
> the recommendation from eeglab is to just use ICA. This might be impacting
> your ICA-results quality.
>
> Make sure you are not trying to get "lower" residual variance of dipole
> fits with "bad ICs".
>
> Review the normal residual variance of dipfit for the ICs in the online
> eeglab tutorial datasets. Compare to your expectations. Compare to
> published dipfit variance results.
>
> To get lower residual variance per IC, your IC scalp maps need to be
> "better" in terms of being more bipolar, etc..
>
> You have a low amount of time in your recording, so you may want to
> downsample in channels.
>
> I would say review your continuous pre-ICA and post-ICA data to make sure
> there is not noise or artifacts that can be removed that are being
> included, possibly influencing your IC quality (and thus dipfit).
>
> I believe that Makoto's pipeline recommends a 1hz high-pass for ICA.
> Also, it's often recommended to filter at 1-50 or 1-40 hz before doing ICA.
>
> Probably best to test multiple settings on a few files, rather than a few
> settings on many kinds of files.
> ************************************************************
> *******************************************
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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