[Mne_analysis] blog post on MNE by Clemens Brunner {Disarmed}

Sun Oct 22 13:17:36 EDT 2017

Yes and no. SSP zeros out an entire component. Regression subtracts the signal recorded at the EOG channels from en EEG.

The two are probably equivalent if the projector is computed using both the EOG and EEG simultaneously at all times. However, by default, MNE doesn't do this. 

The difference is best demonstrated on a dataset with only one EEG channel and one EOG channel. In this set, SSP using only the EEG channel doesn't make any sense, it would zero out the only component there is. However, regressing the EOG onto the EEG channel and subtracting it leaves a cleaned EEG channel as residual of the regression. 

On October 22, 2017 7:52:45 PM GMT+03:00, "Bharadwaj, Hari M" <hbharadw at purdue.edu> wrote:
>If you use samples around each blink event to construct an SSP
>projector that is orthogonal to the leading eigenvector of  the blink
>covariance, then the SSP is equivalent to performing a
>linear-regression based subtraction (with the PCA-component
>corresponding to the blink as the regressor). As you increase the
>number of blink events used to compute the blink covariance and the
>blink projector, the SSP will converge to some set of coefficients. In
>the same way, as you have longer and longer traces, the linear
>regression (whether you use the EOG or the PCA component corresponding
>to the blink) will converge to essentially the same set of
>coefficients.
>
>
>So there any reason to think this will be different from SSP?
>
>
> Thanks,
>
>Hari
>
>
>________________________________
>From: mne_analysis-bounces at nmr.mgh.harvard.edu
><mne_analysis-bounces at nmr.mgh.harvard.edu> on behalf of Denis-Alexander
>Engemann <denis.engemann at gmail.com>
>Sent: Sunday, October 22, 2017 12:35 PM
>To: Discussion and support forum for the users of MNE Software
>Subject: Re: [Mne_analysis] blog post on MNE by Clemens Brunner
>
>We already chatted a bit on twitter. I'd like to see an example or a
>simple API for this in MNE. Marjin has worked on this apparently in
>mne-sandbox.
>
>On Sun, Oct 22, 2017 at 6:33 PM Chris Holdgraf
><choldgraf at berkeley.edu<mailto:choldgraf at berkeley.edu>> wrote:
>wooo, that's great!
>
>--
>
>On Sun, Oct 22, 2017 at 8:12 AM Alexandre Gramfort
><alexandre.gramfort at inria.fr<mailto:alexandre.gramfort at inria.fr>>
>wrote:
>hi,
>
>let me share here a nice blog post by Clemens Brunner
>using MNE-Python to clean up EOG by linear regression:
>
>https://cbrnr.github.io/2017/10/20/removing-eog-regression/<https://secure-web.cisco.com/1ROZtzf_JVx9JVl2jR0vWkhG1jCqXMoWGqkX79zHqaPgh2sdV8qbKC1HqyeoNwSH9SE-7b9McwSkCHi4La_L8kV9X7XHgJGzmRbWR6AVlq9aprthTgN4DF19-oO-MDTpTUutmZJds_o50dIO1UGX8mjTnTZd50j4r5481o8NVKBwB_fz2KdSSrtISoEp15U7L5z8tUSydf1ExtAf7D3Rg7E9eDgCm9kdTDJ7Uauc2ocbUVM7pku5EZ2oOIICame-8HNFuCUxvkZK5MVmucYT2JKFy2f-ZyjQO_rj_ATvcjO7XOGQSJdsUu7a4AS0TyKfrmDcCrJh51LoghiFkvbPWkhkJj0A3HvW9S83INReaJUo/https%3A%2F%2Fcbrnr.github.io%2F2017%2F10%2F20%2Fremoving-eog-regression%2F>
>
>thanks Clemens
>
>Alex
>
>
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-- 
Marijn van Vliet 
Sent from my phone. Please excuse my brevity.
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