[Eeglablist] Fwd: bad channel rejection - kurtosis - threshold limits default 5

Sun Feb 17 08:27:10 PST 2013

---------- Forwarded message ----------
From: ida miokovic <ida.miokovic at gmail.com>
Date: Sun, Feb 17, 2013 at 4:34 PM
Subject: Re: [Eeglablist] bad channel rejection - kurtosis - threshold
limits default 5
To: mmiyakoshi at ucsd.edu

Dear Makoto,

thank you very much for your response.

Actually, I performed Automatic rejection of bad channels on continuous
data. The steps that lead to ICA decomposition were:

1. Highpass 0.1 Hz FIR filter (the frequency of my interest is very low
breathing frequency 0.13 - 0. 35 Hz)

2. Notch short bandpass IIR 48 – 52 Hz filter for line noise removal

3.* Automatic rejection of bad channels (Kurtosis,5) --> *Is it a problem
if the automatic rejection is done on continuous data?

4. re-referencing data to the average reference

5. Epoching data around the markers of interest in segment [-3 2], baseline
removal -3000 -2000 (ms).

6. Run ICA for the first time, reject bad ICs (by rejecting componenets by
map), run ICA for the second time. --> I found your answer regarding the
same issue:

*If you want to obtain better ICA solution, what you should actually do*

>* is to click 'Tools - Reject data using ICA - Reject data (all*>* methods)' and not 'Reject components by map'. You may have run epoch*>* rejection before running ICA. Similarly, you can run the same epoch*>* rejection after ICA on IC activities. If you want to know why and how*>* epoch rejection on IC activity is effective compared to raw EEG data,*

What is wrong with rejecting componenets by map after first ICA?

> Start with 5 and see the results. If it catches too many epochs, then
> increase the value. I would use 8-10.
I performed this rejection (step 3) and among 121 channels, around 10 (+-2)
of them were rejected. Is it too much?

> Adjust them so that the sum of epochs suggested by your rejection
> methods ends up with around 10 % of the data.

Here, you mean to adjust the threshold value of the Kurtosis to end up with
around 10% of the data to be rejected by this automatic method? I obtained
that with Kurtosis threshold 5 (max 12 channels out of 121 were rejected).

> I would recommend that you simply threshold the data by amplitude
> first (+/- 150-200 microvolt, for example) to exclude undoubtedly
> wrong epochs due to loose channel etc (select up to 1% of data- but be
> careful not to catch eye blinks), then apply probability method
> (select up to 3-5% of data). You may think the data is not clean yet,
> but apply ICA anyway, and do rejections on IC activities to select
> another up to 5% of epochs if you want.

Could you please help me with what would be functions/commands for this in
EEGLAB and would this be acceptable on contiuous channel or only epoched?

> Delorme et al. 2007 NeuroImage for epoch rejection using EEGLAB tools.

Somehow I have troubles finding it...could you please mail me the link to
it?

> My general impression is that people spend too much time on data cleaning
(especially psychologists; I've seen this because I'm a psychologist).   >
ICA decomposition is in many cases more robust than you think. If you want
to prove it, try ICA with rejection rate of 5%, 10%, and 15%. I'll bet you
> don't see much (or even any) difference in IC topos and spectra as long
as you recorded the data in an ordinary laboratory environment. So don't be
 > too nervous.

Thank you for these encouraging words. If I understood you correctly, you
are suggesting me to try run ICA after this automatic bad channels (in your
case epochs) rejection but with thresholds of the Kurtosis 5, 10, 15? If
not, what do you mean by rejection rate of the data?

Thank you very much in advance...Your help means a lot.

All the best.

Ida
On Mon, Feb 11, 2013 at 7:54 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:

> Dear Ida,
>
> > I didn't pay much attention to it assuming
> > that the default settings are the most common ones.
>
> Not necessarily so. These parameters depend on your data quality.
>
> > - Why is 5 default value for the max threshold limits? I read
> explanation of
> > the function jointprob() where it says that the threshold is expressed in
> > standard deviation of the mean.
>
> Start with 5 and see the results. If it catches too many epochs, then
> increase the value. I would use 8-10.
>
> > - What would be the easiest way to calculate the standard deviation of
> the
> > mean of my dataset and would that result be the threshold that is
> > appropriate to my dataset?
>
> Adjust them so that the sum of epochs suggested by your rejection
> methods ends up with around 10 % of the data.
>
> > - Are there situation when it is better to use Kurtosis rather than the
> > probability measure and the other way around?
>
> You should see what type of artifacts are picked up by what methods.
> Generally, kurtosis is too sensitive to outliers. The probability
> measure sometimes picks up large alpha. Both have problems, so don't
> completely rely on them. Use them mildly.
>
> I would recommend that you simply threshold the data by amplitude
> first (+/- 150-200 microvolt, for example) to exclude undoubtedly
> wrong epochs due to loose channel etc (select up to 1% of data- but be
> careful not to catch eye blinks), then apply probability method
> (select up to 3-5% of data). You may think the data is not clean yet,
> but apply ICA anyway, and do rejections on IC activities to select
> another up to 5% of epochs if you want.
>
> See also Delorme et al. 2007 NeuroImage for epoch rejection using
> EEGLAB tools. This is an excellent guide for you.
>
> My general impression is that people spend too much time on data
> cleaning (especially psychologists; I've seen this because I'm a
> psychologist). ICA decomposition is in many cases more robust than you
> think. If you want to prove it, try ICA with rejection rate of 5%,
> 10%, and 15%. I'll bet you don't see much (or even any) difference in
> IC topos and spectra as long as you recorded the data in an ordinary
> laboratory environment. So don't be too nervous.
>
> Makoto
>
> 2013/2/9 ida miokovic <ida.miokovic at gmail.com>:
> > Dear eeglab list,
> >
> > after performing some analysis on the eeg dataset I have, I noticed that
> in
> > "Bad channels rejection" step I used Kurtosis measure, normalize measure
> > checked and the max threshold limits remained set on 5 (by the default).
> At
> > the time of performing this step, I didn't pay much attention to it
> assuming
> > that the default settings are the most common ones. Now, when explaining
> to
> > the detail each step of my analysis, I'm stuck here.
> >
> > - Why is 5 default value for the max threshold limits? I read
> explanation of
> > the function jointprob() where it says that the threshold is expressed in
> > standard deviation of the mean.
> >
> > - What would be the easiest way to calculate the standard deviation of
> the
> > mean of my dataset and would that result be the threshold that is
> > appropriate to my dataset?
> >
> > - Are there situation when it is better to use Kurtosis rather than the
> > probability measure and the other way around?
> >
> > Thank you very much for your help and I apologize in advance if you find
> > these questions too simple...
> >
> > Ida
> >
> >
> >
> >
> > _______________________________________________
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>
>
> --
> Makoto Miyakoshi
> JSPS Postdoctral Fellow for Research Abroad
> Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
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