[Mne_analysis] SSP vs. ICA for Artifact Correction

[Denis-Alexander Engemann]

I slightly disagree with Marijn,

They are not equally good because the methods are significantly different.
SSP is less appropriate for ECG/EOG on statistical grounds. It learns
spatial filters by extracting PCA components from the covariance computed
over artifact samples. PCA assumes Gaussian distributions. But in our case
these artifacts typically have skewed and/or peaky distributions, they are
non-Gaussian and ICA happens to be designed for learning spatial filters
from such signals. In my experience SSP can be good but often not good
enough. It tends to remove too much or not enough and is more difficult to
calibrate. This difference is less important for ERP/F analyses but they
can matter for single trial and connectivity estimation. If you do decoding
you don't have to bother either, your supervised model will learn the
important patterns. However, SSP can do an excellent job however at global
denoising when computed on empty room data.
My 2 cents.

Denis
On Fri, 9 Sep 2016 at 08:30, Marijn van Vliet <w.m.vanvliet at gmail.com>
wrote:

> Dear Matthew,
>
> in my current experience, the differences between SSP and ICA are
> negligible, *when the methods are applied correctly*. The paper Tuomas
> linked contains two nice example of how not to apply SSP correctly.
>
> (1) The authors didn't chance the default of removing 2 SSP components,
> where they should probably have used 1 in certain cases (EEG usually only
> has 1 EOG component). So it's no surprise they found that the signal was
> reduced as well as the noise. Take home message: always check that the
> number of components you remove is appropriate.
>
> (2) The authors never mention checking whether the extracted EOG epochs
> were correct. It is likely that they used the automated methods provided by
> MNE-Python blindly and just went with it. This could be acceptable in other
> studies (as long as the data is 'good enough' for the intended purpose),
> but certainly not in a study with the explicit focus on comparing noise
> reduction methods! Take home message: always check the EOG epochs found
> with the 'create_eog_epochs' function. Some things in the data (excessive
> movements by the subject, concatenating raw files that cause 'jumps' in the
> signal) can really mess up the automated algorithm.
>
>
> In conclusion, I recommend the ICA pipeline in MNE-Python because that
> method is easier to apply correctly :) Notably because it doesn't rely on
> EOG event detection and because it has an automated manner of selecting the
> number of components to remove (plus the components are not orthogonal, so
> removing a second component is safer). But even with the ICA pipeline:
> double check the results! At the very least, check whether the noise
> components marked by the automated method make sense.
>
> regards,
> Marijn.
> On 09/08/2016 11:21 PM, Tuomas Puoliväli wrote:
>
> Dear Matthew,
>
> There is a recent study by Haumann and co-authors that compares ICA with
> SSP for rejecting artefacts (http://www.ncbi.nlm.nih.gov/pubmed/27524998).
> They conclude that ICA performs better than SSP but one should be careful
> with ICA when dealing with low SNR data.
>
> Best regards,
> Tuomas
>
>
>
> On 8 September 2016 at 21:42, Boggess, Matthew Jozsef <
> MBOGGESS at mgh.harvard.edu> wrote:
>
>> Hello,
>>
>> MNE provides both SSP and ICA methods for correcting artifacts (eye
>> blinks, saccades, and heartbeat). However, I haven't ever seen these
>> methods compared and was curious if anyone has any reasons to prefer one
>> method over the other? I remember seeing on one of the MNE documentation
>> pages a while back that ICA was recommended over SSP, but I haven't been
>> able to find this statement anymore (perhaps it was removed?).  Was there a
>> reason behind that recommendation?
>>
>> Thanks!
>> Matt
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>> The information in this e-mail is intended only for the person to whom it
>> is
>> addressed. If you believe this e-mail was sent to you in error and the
>> e-mail
>> contains patient information, please contact the Partners Compliance
>> HelpLine at
>> http://www.partners.org/complianceline . If the e-mail was sent to you
>> in error
>> but does not contain patient information, please contact the sender and
>> properly
>> dispose of the e-mail.
>>
>>
>
>
> _______________________________________________
> Mne_analysis mailing listMne_analysis at nmr.mgh.harvard.eduhttps://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it is
> addressed. If you believe this e-mail was sent to you in error and the e-mail
> contains patient information, please contact the Partners Compliance HelpLine athttp://www.partners.org/complianceline . If the e-mail was sent to you in error
> but does not contain patient information, please contact the sender and properly
> dispose of the e-mail.
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>
>
> The information in this e-mail is intended only for the person to whom it
> is
> addressed. If you believe this e-mail was sent to you in error and the
> e-mail
> contains patient information, please contact the Partners Compliance
> HelpLine at
> http://www.partners.org/complianceline . If the e-mail was sent to you in
> error
> but does not contain patient information, please contact the sender and
> properly
> dispose of the e-mail.
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20160909/1f6fbdfd/attachment.html