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

[Makoto Miyakoshi]

Dear Ida,

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

OK, I noticed that filter is very important for you. Make sure that you
used pop_eegfiltnew() or firfilt(). See also
http://sccn.ucsd.edu/wiki/Firfilt_FAQ. Please let me know if the latest
version of EEGLAB DOES NOT have pop_eegfiltnew().

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

Do not use notch since we have much better solution. Check this out.

http://www.nitrc.org/projects/cleanline/

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

It depends on how clean the data need to be. In general, kurtosis is easily
affected by outliers. If no data rejection is done, kurtosis may not be the
best choise. I would start with simple code such as

stdData = std(EEG.data,0,2);

figure; bar(stdData)

If you find bad channel in this bargraph you may wan to check them visually
to make decision. If you have too many data to eyeball, you may need to
write a code to loop this.

> 4. re-referencing data to the average reference

This seems fine.

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

If your freq of intrest is 0.13-0.35, then you should have at least 7.7 sec
long (longer is better) for a epoch to obtain spectra at 0.13 Hz (I could
be wrong in guessing what you want to do).

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

http://sccn.ucsd.edu/eeglab/eeglabfaq.html

*Re-running ICA*

*Question:* While trying within EEGLAB to remove artifacts using ICA, I had
trouble in recalculating an ICA decomposition after removing components. I
tried to follow the guidelines in the tutorial, thinking that with fuzzy
components it might work better to remove some clear artifact components
first and the run a new ICA. When I tried to do that, the second ICA always
took much longer and I got also some error message in the end telling me,
that there was something wrong with the result.

*Answer:* The standard procedure we advise is first to perform ICA on the
data and to remove bad trials using the ICA component activities. If you
remove ICA components, the rank of the data will decrease (to <nchans). If
the data have n channels, the rank of the data is (most probably) n. If you
remove one component it will become n-1, and ICA will not be able to find n
components in the pruned data). Thus, as a first step, you should only
remove bad trials. This procedure will not alter the dimensionality of the
data. As a second step, recompute ICA and remove bad components (the second
run of ICA should result in clearer artifact components (for instance
muscle at high frequencies), not contaminated by strong outlier trials. If
you remove ICA components and want to re-run ICA, you must decompose the
data with the 'pca' option to reduce the dimensionality of the
decomposition to match the data rank (see below).

> I performed this rejection (step 3) and among 121 channels, around 10
(+-2) of them were rejected. Is it too much?
I think that' fine.

> 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?

No. I meant in epoch rejection around 10% would be ok, not the number of
channels.

> 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?

In the current EEGLAB continuous data can be rejected only by eyes. If you
don't mind chopping up your data into epochs, check out eeg_regepochs().

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

Delorme, A., Sejnowski, T., Makeig, S. (2007) Improved rejection of
artifacts from EEG data using high-order statistics and independent
component analysis. *Neuroimage*, 34, 1443-1449.

> 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?

Again I mean the number of epochs. In the guideline paper (Picton et al.,
2000) it is stated that rejecting more than 30% of epochs may be a bad idea
if the data were recorded from healthy adults.

Makoto

2013/2/17 ida miokovic <ida.miokovic at gmail.com>

> 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
>>
>
>


-- 
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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