[Eeglablist] Using a laplacian filter with ICA

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Tue Jan 24 16:08:12 PST 2017

Dear Sean,

It's a great honor to have the comment from the first author of the paper

Is it true that an assumption of using Laplacian is that all EEG sources be
radial and cannot be tangential? That assumption sounds generally
reasonable since tangential (i.e. sulcus) sources would suffer a lot from
cancellation by the facing cortex. However, when we see ICA results, there
are quite a bit of seemingly tangential sources. Actually, the tangential
(sulcus) sources may be able to survive if the facing cortical patches are
less active and/or activated independently. Otherwise, what are these
results we see in ICA results? Too beautiful to be noise.


On Thu, Jan 12, 2017 at 11:43 PM, Sean Fitzgibbon <
sean.fitzgibbon at ndcn.ox.ac.uk> wrote:

> 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
> 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
> _______________________________________________
> *Dr Sean Fitzgibbon * BSc PhD
> Postdoctoral Researcher
> FMRIB <http://www.fmrib.ox.ac.uk/> Centre, University of Oxford
> Oxford, United Kingdom
> On 13 Jan 2017, at 06:55, Makoto Miyakoshi <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> 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), 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
>> ------------------------------
>> UCLA HEALTH SCIENCES IMPORTANT WARNING: This email (and any attachments)
>> is only intended for the use of the person or entity to which it is
>> addressed, and may contain information that is privileged and confidential.
>> You, the recipient, are obligated to maintain it in a safe, secure and
>> confidential manner. Unauthorized redisclosure or failure to maintain
>> confidentiality may subject you to federal and state penalties. If you are
>> not the intended recipient, please immediately notify us by return email,
>> and delete this message from your computer.
>> _______________________________________________
>> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
>> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.uc
>> sd.edu
>> For digest mode, send an email with the subject "set digest mime" to
>> eeglablist-request at sccn.ucsd.edu
> --
> Makoto Miyakoshi
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
> _______________________________________________
> Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html
> To unsubscribe, send an empty email to eeglablist-unsubscribe at sccn.
> ucsd.edu
> For digest mode, send an email with the subject "set digest mime" to
> eeglablist-request at sccn.ucsd.edu

Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://sccn.ucsd.edu/pipermail/eeglablist/attachments/20170124/76128098/attachment.html>

More information about the eeglablist mailing list