[Eeglablist] ECG Channel Inclusion in ICA and ICLabel Steps

Mon Aug 5 07:59:41 PDT 2024

Hello John and Cedric,

This double negative makes it difficult to understand what you are trying
to say, Cedric.

> I do not believe including auxiliary channels does not violate ICA
assumptions.

You are saying that including ECG violates ICA's assumption, but your
claim seems opposite.

ICA's assumptions are, off the top of the head,

   - The sources are non-Gaussian
   - The sources are linearly independent
   - The sources are linearly and instantly mixed
   - Tony Bell is a legend

So ECG channels may be included in ICA with no problem in theory. However,
if empirical observations recommend otherwise, there may be some reason
that is specific to each application which is worth investigating. For
example, concatenating millivolt-range signals with microvolt-range signals
for decomposition may cause unexpected trouble.

> It does not inherently assume that all sources must be of the same nature
or originate from EPSP/IPSPs from pyramidal neurons in cortical gyri, does
it?

Then ICA would not identify non-brain artifacts, right?

I'm rather curious to know if heartbeat-evoked potential is a real thing.
For example, if I decompose a heartbeat IC which typically has a broad and
dipolar projection from the depth. Is there any neuronal contribution
there? If yes, then the heartbeat IC is contributed by both the heart and
the brain. If no, it is generated only by the heart. Which is the case?

Makoto

On Mon, Aug 5, 2024 at 12:58 AM Cedric Cannard via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

> Dear John,
>
> This is the same debate as including EOG for separating and extracting
> ocular components (isn't that common practice?). The only reason why you
> can get away without EOG channels is that ocular activity is so large
> compared to EEG, so easy to identify as an independent source. However,
> cardiac artifacts are difficult to identify without an explicit reference
> signal (except occasionally in some individuals where you can see them
> visually in the raw time series).
>
> Someone will correct me if I'm wrong, but I do not believe including
> auxiliary channels does not violate ICA assumptions. ICA is an algorithm
> designed to separate a mixture of signals into statistically independent
> components. It does not inherently assume that all sources must be of the
> same nature or originate from EPSP/IPSPs from pyramidal neurons in cortical
> gyri, does it?
>
> By providing a clear reference for ECG artifacts, ECG channels can enhance
> the accuracy of this separation in my experience, in line with Sofia's
> observation. In fact, this is a method supported by my new BrainBeats
> plugin (along with HEP/HEO and HRV analysis which implements validated
> algorithms and guidelines via command line or GUI). See here for a
> step-by-step tutorial:
> https://urldefense.com/v3/__https://www.jove.com/t/65829/author-spotlight-advancing-study-brain-heart-interplay-with__;!!Mih3wA!CWCJFZKu2zYD1n7sKo1ur_bO9g_iWhAy9vSTS18Bf-jfmH6errEXOVCDaXANh_5ukcj1Wajfh_MIn3CPFGGRV09qjA$
> And Github repo:
> https://urldefense.com/v3/__https://github.com/amisepa/BrainBeats__;!!Mih3wA!CWCJFZKu2zYD1n7sKo1ur_bO9g_iWhAy9vSTS18Bf-jfmH6errEXOVCDaXANh_5ukcj1Wajfh_MIn3CPFGGKS9yf5A$
>
> Sofia, my question is: Why are you using ICA in the context of HEP? You
> generally want to keep ECG-related activity for HEP. Do you suspect the HEP
> is a CFA? I have wondered about that but haven't found a work-around.
>
> Curious to hear more on this!
>
> Cedric Cannard
>
>
>
> On Thursday, August 1st, 2024 at 7:08 AM, Richards, John via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Don't include ECG in the EEG signal for ICA. For various technical
> reasons (amplitude, frequency, signal spikes, location) and theoretical
> ones (its NOT a brain signal; you want to correlate ECG and EEG).
> >
> > See poster by Wanze Xie at ICIS, or contact him. He has synchronized
> cardiac cycles and ERPs. His techniques might be useful and he has some
> very interesting VEP/cardiac cycle data.
> >
> > John
> >
> >
> > ***********************************************
> > John E. Richards
> > Carolina Distinguished Professor
> > Department of Psychology
> > University of South Carolina
> > Columbia, SC 29208
> > Dept Phone: 803 777 2079
> > Fax: 803 777 9558
> > Email: richards-john at sc.edu
> >
> https://urldefense.com/v3/__https://jerlab.sc.edu/__;!!Mih3wA!B4rXaMGVhHYr_2oHmmQc94PJcfhiYYPWsZNqJtpm0CRKhOTeHaXWrkUCGI-mcxaW1b2qR1YWJv1lJrPrNV29FIao1z4gCQ$
> > ***********************************************
> >
> > -----Original Message-----
> > From: eeglablist eeglablist-bounces at sccn.ucsd.edu On Behalf Of Sofia
> Amaoui via eeglablist
> >
> > Sent: Thursday, August 1, 2024 8:54 AM
> > To: eeglablist at sccn.ucsd.edu
> > Subject: [Eeglablist] ECG Channel Inclusion in ICA and ICLabel Steps
> >
> > Dear all,
> >
> > We are conducting a Heart-Evoked Potential (HEP) study using EEG signals
> that are time-locked to R-peak via ECG. Our preprocessing pipeline is being
> implemented with EEGLAB, including the ERPLAB and HEPLAB plugins. As part
> of this process, we employ Independent Component Analysis (ICA) and ICLabel
> to identify ocular and cardiac field artefacts (CFA).
> >
> > A key question has arisen regarding including the ECG channel in the ICA
> and ICLabel steps. Our preliminary findings show that when we include it,
> CFA classification is much higher.
> >
> > We are seeking your insights on whether the inclusion of the ECG channel
> is aiding in the identification of CFA components or if it might be
> compromising EEG information of interest by focusing on identifying and
> removing CFA.
> >
> > Thank you for your time and assistance.
> >
> > Best regards,
> >
> > Sofia Amaoui,
> > _______________________________________________
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