[Eeglablist] Reject data (all methods)

Thu Apr 4 00:13:51 PDT 2013

Dear Tarik,

thank you for sharing your opinion very much. I'll respond to each point
you pointed out =)

>> 1. I would ask how was the data truly continuous if you did artifact
rejection on it ?
I had data set recorded in periods when subject is performing task and then
relaxing, performing tast, relaxing...I highpassed the raw data, remove
line noise using Cleanline (which works great) and after that I removed all
the portions where subject is relaxing. So you are correct - it is not 100%
continuous data, since I have those Boundary markers (and I also kept
markers determing the end of task performance). But also - it is not
epoched in a way EEGLAB sees epoching. Those were my preprocessing steps
before first ICA. I planned to do re-referencing to the average reference
after artifactual ICs removal (it would be done on EEG pruned with ICA).

>>2.Epoching the data won't hurt the components. You should eventually end
up with a (potentially) clean(er) ICA decomposition, and then apply that
back to a full continuous data set, or a continuous (or epoched) dataset
free of artifactual time periods.
I thought it wouldn't hurt components and as I said in previous paragraph -
removal of the artifactual ICs would be applied to this "semi-continuous"
data I end up with.

>>3. At some point you will reach some decisions that no one has made
before or only few researchers have explored. I would say your best bet is
to try the analysis yourself on a few cases so you can have some first-hand
information as to what seems to be working best
This is very encouraging point, I hope this would happen eventually...On my
trip to these conclusions, EEGLAB list is helping me very much.

>>4. Yes, there are many kinds of analyses and processing you can do with
the ICs you have, so you should try to see what you have when you try to
interpret your ICs in relation to the task and the relevant eeg literature
about the kind of cognition or behavior you are studying.
I agree to this and these discussions are very helpful in this process.

>>5. When you have a chance, consider sharing some of your results with the
list, as this would be beneficial for other people just learning about ICA.
Yes of course, when I reach the point when I definetly have some
conclusions I will be happy to share it with the list, since I started a
lot of discussions that helped me to decide in which direction to move.

Cheers!

Ida

On Thu, Apr 4, 2013 at 2:34 AM, Tarik S Bel-Bahar
<tarikbelbahar at gmail.com>wrote:

> Hello Ida,
> Just a few quick points:
> 1. I would ask how was the data truly continuous if you did artifact
> rejection on it ?
>
> 2.Epoching the data won't hurt the components. You should eventually end
> up with a (potentially) clean(er) ICA decomposition, and then apply that
> back to a full continuous data set, or a continuous (or epoched) dataset
> free of artifactual time periods.
>
> 3. At some point you will reach some decisions that no one has made before
> or only few researchers have explored. I would say your best bet is to try
> the analysis yourself on a few cases so you can have some first-hand
> information as to what seems to be working best.
>
> 4. Yes, there are many kinds of analyses and processing you can do with
> the ICs you have, so you should try to see what you have when you try to
> interpret your ICs in relation to the task and the relevant eeg literature
> about the kind of cognition or behavior you are studying.
>
> 5. When you have a chance, consider sharing some of your results with the
> list, as this would be beneficial for other people just learning about ICA.
>
> Cheers!
>
>
>
>
> 3.
>
> Tarik Bel-Bahar, Postdoctoral Fellow
> Perception, Performance & Psychophysiology Lab
> tarik.777 at duke.edu/ 919 328 9573
> Div. of Brain Stimulation and Neurophysiology
> Dep. of Psychiatry and Behavioral Sciences
> Duke University Medical Center, Duke Clinics
> Red Zone, 5th Floor, Rm. 54236
> 200 Trent Drive, Durham, NC 27710
>
>
> On Sat, Mar 30, 2013 at 5:40 AM, ida miokovic <ida.miokovic at gmail.com>wrote:
>
>> Hello everyone,
>>
>> thank you Marco, Tarik, Makoto, Arnaud for your suggestions.
>>
>> I will surely consider using regepochs function. As I understood its
>> functioning correctly - it is epoching data set in way that it is possible
>> to merge them together later. Since I already calculated ICA algorithm on
>> continuous data, does it make sense to epoch the data now, before second
>> ICA, what would it "do" to components? Or it is better to do the epoching
>> before first ICA and do it again?
>>
>> At this moment I see also this option - Since I read that the experience
>> of some researchers is that second ICA does not mean that much better
>> decomposition - I could remove components (calculated on continuous data)
>> recognized as artifacts and prune the dataset and see what I have.
>>
>> I appreciate your opinion.
>>
>> Thanks
>>
>> Ida
>>
>>
>> On Thu, Mar 28, 2013 at 10:26 AM, Arnaud Delorme <arno at ucsd.edu> wrote:
>>
>>> Dear Ida,
>>>
>>> there is the menu "Tools > Automatic continuous rejection" although this
>>> does not use ICA component activities. It would be a good idea to add
>>> component to that function as well as channels.
>>>
>>> Arno
>>>
>>> On 27 Mar 2013, at 17:53, Makoto Miyakoshi wrote:
>>>
>>> Dear Ida,
>>>
>>> >Are there any suggestions how to implement any kind of "bad" parts of
>>> the components removal, when having continuous data (before running second
>>> ICA)?
>>>
>>> When you plot raw EEG, you can choose time period (marked as light blue)
>>> to register as bad data. You can reject the marked data portion later.
>>>
>>> If you are interested in automated algorithm for that you may want to
>>> check BCILAB.
>>>
>>> Makoto
>>>
>>> 2013/3/24 ida miokovic <ida.miokovic at gmail.com>
>>>
>>>> Dear Makoto, Mikolaj and Marco (and everyone else =)),
>>>>
>>>> thank you very much for your answers.
>>>>
>>>> Regarding 1st issue - Yes, I have been informed that there is a bug
>>>> that causes this channels locations problem, I have seen that others
>>>> reported the same issue, so I didn't reported it myself since it was said
>>>> that this will be corrected in the next EEGLAB version. Marco, is it
>>>> necessary for me to report this in order to get the developers version? How
>>>> could I get it?
>>>>
>>>> Regarding 2nd issue - I didn't realize that mentioned methods are
>>>> applicable only on epoched data. Are there any suggestions how to implement
>>>> any kind of "bad" parts of the components removal, when having continuous
>>>> data (before running second ICA)? Does EEGLAB support that? If not, any
>>>> pipeline in MATLAB itself would be appreciated...
>>>>
>>>> Thank you very much again
>>>>
>>>> Ida
>>>>
>>>>
>>>> On Fri, Mar 22, 2013 at 4:32 PM, Marco Montalto <
>>>> montaltomarco at onvol.net> wrote:
>>>>
>>>>> Dear Ida,
>>>>>
>>>>> Regarding Problem no. 1, I was having the same problem with the same
>>>>> version of EEGLAB you are using. When I plotted channel locations by
>>>>> pressing plot-2D I used to get a plot with all channels squashed in the
>>>>> bottom left hand corner of the plot. I reported the problem. Arno
>>>>> reproduced the problem and made the necessary changes to the developer's
>>>>> version of EEGLAB. I am using the developer's version now and the problem
>>>>> is indeed solved. So I would recommend that you use the developer's version
>>>>> to overcome this problem.
>>>>>
>>>>> Regarding Problem no. 2, (corroborating what was said previously
>>>>> by Mikołaj Magnuski), Reject data (all methods) is greyed out because you
>>>>> have not extracted any epochs. Since you are using continuous data you
>>>>> cannot make use of this function, .
>>>>>
>>>>> Regards,
>>>>> Marco
>>>>>
>>>>>
>>>>> 2013/3/17 ida miokovic <ida.miokovic at gmail.com>
>>>>>
>>>>>> Dear list,
>>>>>>
>>>>>> I am running ICA decomposition on continuous data. I have several
>>>>>> questions regarding this:
>>>>>>
>>>>>> 1. After applying lowpass filter and removing line noise (using
>>>>>> Cleanline plug-in), I import the channel locations of the dataset. I am
>>>>>> using EEGLAB v12.0.1.0b which is having issues with channel locations
>>>>>> importing, but when I obtain ICA components, scalp maps seem to be plotted
>>>>>> correctly, so I suppose that this bug does not influence this step? After
>>>>>> that, I do the average reference.
>>>>>>
>>>>>> 2. Since I plan to run ICA second time, I should remove bad trials of
>>>>>> the components (not entire component/s) using Tools --> Reject data using
>>>>>> ICA --> Reject data (all methods). After doing all described above, I do
>>>>>> not have an option to click on this tab "Reject data (all methods)" (it is
>>>>>> grey). It is possibe to click only on "Reject components by map". Does
>>>>>> anyone have an idea what could be the reason for this?
>>>>>>
>>>>>> 3. There is a suggestion that not more than 10% of the dataset should
>>>>>> be rejected after applying all the methods I am interested in. Are some of
>>>>>> the methods given it this menu (Find abnormal values, Find abnormal
>>>>>> trends, Find impropable data, Find abnormal distributions, Find abnormal
>>>>>> spectra (slow)) more convenient that others and which? Which way I can
>>>>>> calculate what is the percent of the rejected data?
>>>>>>
>>>>>> 4. Should the mean of the all channels be removed somewhere in this
>>>>>> procedure and where is it suggested to be done? As I mentioned, here
>>>>>> contiuous data is used, so there are no epochs and no need for baseline
>>>>>> removal.
>>>>>>
>>>>>>
>>>>>> Thank you very much for any help.
>>>>>>
>>>>>> All the best
>>>>>>
>>>>>> 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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>>
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