[Eeglablist] Using a laplacian filter with ICA

Thu Jan 19 17:01:05 PST 2017

Abby,

As we chatted eariler,  I think the CSD method works well in high-density EEG; so, your proposed method sounds good.

-Iman

________________________________
From: eeglablist-bounces at sccn.ucsd.edu <eeglablist-bounces at sccn.ucsd.edu> on behalf of Dickinson, Abigail <ADickinson at mednet.ucla.edu>
Sent: Thursday, January 19, 2017 10:16 AM
To: Sean Fitzgibbon; mmiyakoshi at ucsd.edu
Cc: eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Using a laplacian filter with ICA

Thanks for your helpful responses Makoto & Sean.

I've been comparing using laplacian and ICA together, and in different orders on both my high density data, and then the downsampled montage.

So far (I'm going to try a few more datasets) - the results don't look strikingly different. But I do find that applying laplacian to 128 channels, downsampling to the 25 channel montage, and then running ICA (in order to remove EOG/EMG components) works really nicely. From what I udnerstand this is probably because the laplacian is helping with the persistent muscle contamination, and the ICA with the more stereotyped artifacts.

Thanks again for your help.

Best,

Abby

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From: Sean Fitzgibbon <sean.fitzgibbon at ndcn.ox.ac.uk>
Sent: Thursday, January 12, 2017 11:43 PM
To: mmiyakoshi at ucsd.edu
Cc: Dickinson, Abigail; eeglablist at sccn.ucsd.edu
Subject: Re: [Eeglablist] Using a laplacian filter with ICA

Dear Abby

The first thing to note is that this method is not designed to deal with transient muscle activity defined as “transient contractions of facial, cranial, and neck muscles [that] generate high amplitude electrical signals... Typically [transient] EMG contamination is of sufficient amplitude that it can be easily identified (visually or algorithmically) and it is common practice to excise and discard contaminated periods of EEG”.  We would still advocate excising these segments.  Rather, the method seeks to rectify persistent muscle contamination defined as "sustained non-forceful contractions (e.g. tone) of these muscles [that] generate persistent low amplitude contamination” that is very difficult to identify in the scalp EEG.  The motivation for our interest in persistent EMG is “our experiments in paralysed conscious humans have demonstrated that persistent EMG strength exceeds EEG strength from frequencies as low as 10–20 Hz, more so peripherally than centrally, and that by 100 Hz, EMG can be more than 200 times larger than EEG”.

The method, in brief, is to decompose the EEG using ICA, identify and remove EMG components, project back to scalp space, and then apply the laplacian.  The average reference is not applied.  We have shown in earlier papers, consistent with Makato’s comment below, that ICA cannot completely decompose EMG, although it can improve SNR.  However, using paralysed (muscle-free) EEG as the gold standard, we have demonstrated empirically that “the combination of the ICA procedure and the surface Laplacian results in a strong suppression of [persistent] EMG contamination at all scalp sites and frequencies” and "does not impair the detection of well-known, cerebral responses”.

Anyway, to actually respond to your question, it is commonly stated that high spatial density EEG (~128 channel) is required for Lacplacian.  However this is challenged by Kayser and Tenke (2006; full ref below), at least for ERPs.  Our own experience is that we get reasonable approximations at lower densities, consistent with Kayser and Tenke.  The 25-channels you have is quite low, but might still be worth trying.  I cannot comment on whether is is useful to reverse the order and do Laplacian before ICA as we have not ever tried that.  However, "Tenke and Kayser (2005) proposed a method called fPCA, which … demonstrate[s] compelling removal of EMG contamination … [and] requires application of the surface Laplacian to the EEG to estimate CSD, calculation of the amplitude spectra from the CSD, and then application of principal component analysis (PCA) to decompose the spectra” which sounds similar’ish to what you are proposing and may be a good starting point for you…

Cheers, Sean

Kayser, J., & Tenke, C. E. (2006). Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: II. Adequacy of low-density estimates. Clinical Neurophysiology, 117(2), 369–380. http://doi.org/10.1016/j.clinph.2005.08.033<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fdoi.org%2F10.1016%2Fj.clinph.2005.08.033&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=N4Bsi%2BAGWhgKlxKsslrlZ7l%2BmoZj4o6Kq%2FHBAowASXk%3D&reserved=0>

Tenke, C. E., & KAYSER, J. (2005). Reference-free quantification of EEG spectra: Combining current source density (CSD) and frequency principal components analysis (fPCA). Clinical Neurophysiology, 116(12), 2826–2846. http://doi.org/10.1016/j.clinph.2005.08.007<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fdoi.org%2F10.1016%2Fj.clinph.2005.08.007&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=WM4gZturJKtE9zFoUZ3Y2m4TWY25OPumDfS7t2k06cw%3D&reserved=0>

_______________________________________________

Dr Sean Fitzgibbon  BSc PhD
Postdoctoral Researcher
FMRIB<https://na01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.fmrib.ox.ac.uk%2F&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=kW2pSDQA7z%2B%2BmPeYy2B2i2iJ8JsGoy47ZIhhX8ckkj8%3D&reserved=0> Centre, University of Oxford
Oxford, United Kingdom

On 13 Jan 2017, at 06:55, Makoto Miyakoshi <mmiyakoshi at ucsd.edu<mailto:mmiyakoshi at ucsd.edu>> wrote:

Dear Abby,

That's a new method for me and I don't know how it works. From the abstract, it seems you apply ICA, reject muscle components, then Laplacian? If so, the question is when you want to perform the average reference. Probably you want to average reference after Laplacian so that the computed average reference is EMG free (not exactly free, but very much removed?)

By the way, ICA cannot decompose EMG becaues EMG source spreads along with muscle fibers, so my former colleague told me. Empirically, I also experienced that muscle is harder to decomposed compared with other typical artifacts such as eyes, which makes sense.

>From Lufthansa455 to Frankfurt,

Makoto

On Tue, Jan 10, 2017 at 10:28 AM, Dickinson, Abigail <ADickinson at mednet.ucla.edu<mailto:ADickinson at mednet.ucla.edu>> wrote:

Hi everyone,

I jwondered if anyone could help me with a question I had regarding using a laplacian filter, and at which stage I should implement the  filter. I currently have a processing pipeline which effectively cleans spontaneous data collected from low-functioning children (EGI 128 channel, 500Hz). Due to the nature of our sample I am working with a few constraints, including a high amount of EMG and relatively short recording lengths (1-2 minutes).

The processing pipeline I currently use involves:

-FIR filter (high pass:1Hz, low pass:100Hz)

-remove bad channels

-down-sampling to the 10-20 system 25 channel montage (in order to have an adequate k-factor to run ICA)

-remove bad segments of data

-Run ICA

-Remove artifactual components

-Re-reference to average

I recently saw a paper which advocated the use of a laplacian filter along with ICA to remove EMG (https://www.ncbi.nlm.nih.gov/pubmed/25455426<https://na01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F25455426&data=01%7C01%7CADickinson%40mednet.ucla.edu%7Ca5dae3461d4145d0999a08d43b87ea2d%7C39c3716b64714fd5ac04a7dbaa32782b%7C0&sdata=BSvWDr41xp9VU0z2u3z7kaIdDi634EfFQDv5xrEvQ1U%3D&reserved=0>), and also I would like to be able to run cohernece analyses at a channel level, so wanted to re-process this data with a laplacian filter.

However, I cannot apply the laplacian after ICA, as at that point I only have 25 channels. I wondered if anyone could comment on whether it would be appropriate to apply the laplacian filter either on all 128 channels, or after removing bad channels, and then continuing with the rest of the processing pipeline detailed above?

Thanks in advance!

Best wishes,

Abby

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--
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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