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

ida miokovic ida.miokovic at gmail.com
Fri Feb 22 13:32:54 PST 2013 

Dear Makoto,

thank you for so much patience with me.

My problem is that I can recognize component that has potential to be an
eye artifact or muscle artifact - but with Reject components by map. This
is acceptable after second ICA (re-running).

But after first ICA that is not recommended because than the problem with
the rank of the data occurs. In one of your answers (and later I found it
in other eeglablist discussions) there was a suggestion not to reject ICs
by map after first ICA but to use other methods where only bad epochs of
the ICs are rejected. In my previous mail I pasted the link suggestion to
use Reject data (all methods) for this. The problem I have is not knowing
the criteria for the good and the bad data in ICs (other than those seen by
visual inspection of the each component by map - which cannot be helpful
after first ICA). What are the limits of the parameters that should be
given in this "Reject data (all methods)"?

I hope I explained it better this time...

Ida

On Fri, Feb 22, 2013 at 10:17 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:

> Dear Ida,
>
> I still don't get where are are stuck.
> In the bottom right of 'Reject data (all methods)' GUI, you see 'reject
> marked epochs'. Is this what you are looking for? If you are asking
> parameters, it depends on how clean your data are, so you should either
> eyeball the results (press the 'plot' button) or use some statistics in the
> command line to optimize them.
>
> If these are still missing the point please explain the problem again. I
> hope I'm not making a silly misunderstanding.
>
> Makoto
>
>
> 2013/2/22 ida miokovic <ida.miokovic at gmail.com>
>
>> Dear Makoto,
>>
>> > How can you accurately measure 0.13-0.35 Hz activity with epochs
>> shorter than 7.7 sec long? Isn't it theoretical requirement to have at
>> least 1(sec)/0.13(Hz) = 7.7 sec long window to calculate 0.13 Hz?
>>
>> You are totally right with this. Theoretically, relation between period
>> and frequency is T = 1/f, so with lower limit of 0.13 Hz frequency, period
>> has to be 7.7 s. But also, for example for frequency 0.25 HZ, it is enough
>> to have 4 s. I think I haven't explained my data set detailed enough - the
>> trick is that I expect interesting activity at the moment of inhaling. In
>> the same time, subjects are breathing natural, without anything giving them
>> a rhythm. Therefore, epoching of their data (I have markers in the moments
>> of inhalation) is done around those markers. For the subjects I have, the
>> largest possible epoching is done having in mind to not have epochs
>> overlapping. That means that the lowest frequency of my interest is 1/4s =
>> 0.25 Hz. This is kind of "aha" moment for me too.
>>
>> Thanks for the cleanline.
>>
>> About this question of mine: > Is there any paper suggesting the limits
>> of parameters of ICs that are required for the methods in this dialog box
>> (for finding abnormal values, trends, impropable data, abnormal
>> distributions, abnormal spectra)?
>>
>> To say it simple - I can recognize a bad component by its scalp power map
>> (colored in different head positions) - I know what is expected position of
>> sources for eye/muscle artifacts and the shape of their magnitude and
>> expected activity around the marker (I found the explanation here
>>
>> http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA
>>
>> but now when I have no possibility to eliminate component this way, after
>> first ICA, I'm stuck. Because, as you said the criteria how to judge which
>> epoch is good and which is bad is up to me.
>>
>> Here http://sccn.ucsd.edu/wiki/Chapter_01:_Rejecting_Artifacts in the
>> chapter "Rejection based on independent data components" should be done
>> with the Tools --> Reject data using ICA-->Reject data (all methods). When
>> the pop up window appears, there are parameters that should be adjusted in
>> order to reject bad data (meaning bad trials of independent components). Am
>> i missing somenthing here? I don't know how to eliminate IC (or its part)
>> in any other way but visually...So after first ICA troubles for me start...
>>
>> Thanks a lot, I look forward to your email.
>>
>> All the best
>> Ida
>>
>> On Thu, Feb 21, 2013 at 4:04 AM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:
>>
>>> Dear Ida,
>>>
>>> > Yes, for me it is very important to to lose the freq range of
>>> breathing.
>>>
>>> How can you accurately measure 0.13-0.35 Hz activity with epochs shorter
>>> than 7.7 sec long? Isn't it theoretical requirement to have at least
>>> 1(sec)/0.13(Hz) = 7.7 sec long window to calculate 0.13 Hz?
>>>
>>> > Now I installed V12.0.1.0b and will use as *the first step* Tools-->Basic
>>> FIR filter (new) with lower edge input as 0.1 Hz (range is from 0.1 - 0.35
>>> Hz) which uses  pop_eegfiltnew() .
>>>
>>> Looks fine.
>>>
>>> > I installed a plug in you suggested (cleanline) and there is still no
>>> Help provided,
>>>
>>> Sorry that's most likely a bug. I know it's been while since I had
>>> noticed it. I reported it to Arno. Meanwhile, type 'help cleanline' in the
>>> comannd window please. You can pretty much use the default anyway.
>>>
>>> > *Fourth step - * epoching: Freq of my interest is 0.13-0.35, but
>>> markers of interesting events are put manually (depending on value on
>>> additional pressure sensor measuring inhalation and exhalation). I often
>>> have considerable epochs overlapping (even 2 markers within one epoch) if I
>>> use larger range than [-3,2] so I suppose it is better not to have a large
>>> overlap. 7.7 sec long epoch would result very large overlap. Only I can do
>>> to avoid it is to erase markers, but than I will lose a lot of data.
>>>
>>> As I wrote above, I'm very concerned with this. To capture a single
>>> cycle of 0.13 Hz sinusoid you need at least 7.7 sec. Think about it again.
>>>
>>> > Is there any paper suggesting the limits of parameters of ICs that are
>>> required for the methods in this dialog box (for finding abnormal values,
>>> trends, impropable data, abnormal distributions, abnormal spectra)?
>>>
>>> What do you mean by 'limits of parameters'? In the Delorme et al. (2007)
>>> it says 'total number of epochs to be rejected should be around 10%' and
>>> that's it. It's up to you what criteria  to use to judge 'good' or 'bad'.
>>> Again, I recommend you should not overtrust kurtosis nor improbability. Use
>>> it moderately.
>>>
>>> Makoto
>>>
>>> 2013/2/20 ida miokovic <ida.miokovic at gmail.com>
>>>
>>>> Dear Makoto,
>>>>
>>>> Yes, for me it is very important to to lose the freq range of breathing.
>>>> I am using EEGLAB version V11.0.1.1b and with "Tools --> Filter the
>>>> data --> Basic FIR filter" data is filtered with pop_eegfilt function (fir1
>>>> is checked).
>>>> Now I installed V12.0.1.0b and will use as *the first step*Tools-->Basic FIR filter (new) with lower edge input as 0.1 Hz (range is
>>>> from 0.1 - 0.35 Hz) which uses  pop_eegfiltnew() .
>>>>
>>>> I installed a plug in you suggested (cleanline) and there is still no
>>>> Help provided, so I am not sure how to set the parameters in the pop up
>>>> window (p, bandwith (what would be realistic value?), sliding window length
>>>> and step size, window overlap smoothing factor, FFT padding factor)? Can I
>>>> leave it default? What is a suggested range if I would like to remove 50Hz
>>>> noise? Is 48 - 52 Hz too sharp? I would use this as *the second step.* Although,
>>>> I read that it is possible that ICA finds this line noise, so if I do not
>>>> understand these parameters, I could skip this step.
>>>>
>>>> I will definitly *skip the step of automatical removal of bad channels*and try with your suggestion and use standard deviation:
>>>>
>>>> stdData = std(EEG.data,0,2);
>>>>
>>>> figure; bar(stdData)
>>>>  *Third step* would be re-reference to the average reference.
>>>> *Fourth step - * epoching: Freq of my interest is 0.13-0.35, but
>>>> markers of interesting events are put manually (depending on value on
>>>> additional pressure sensor measuring inhalation and exhalation). I often
>>>> have considerable epochs overlapping (even 2 markers within one epoch) if I
>>>> use larger range than [-3,2] so I suppose it is better not to have a large
>>>> overlap. 7.7 sec long epoch would result very large overlap. Only I can do
>>>> to avoid it is to erase markers, but than I will lose a lot of data.
>>>>
>>>> *Fifth step* would be running ICA for the first time.
>>>> *Sixth step* would be this semi-automatic rejection of bad epochs
>>>> (recommended to be done on ICs) with Tools-->Reject data using ICA-->Reject
>>>> data (all methods). I saw in Tutorial that *"The user should begin by
>>>> visually rejecting epochs from some test data, then adjust parameters for
>>>> one or more of the rejection measures, comparing the visually selected
>>>> epochs with the results of the rejection measure. All the measures can
>>>> capture both simulated and real data artifacts. In our experience, the most
>>>> efficient measures seem to be frequency threshold and linear trend
>>>> detection."*. Is there any paper suggesting the limits of parameters
>>>> of ICs that are required for the methods in this dialog box (for finding
>>>> abnormal values, trends, impropable data, abnormal distributions, abnormal
>>>> spectra)?
>>>>
>>>> As* the last step -* ICA will be re-run and components visually
>>>> inspected/selected/removed.
>>>>
>>>> Your guidelines helped me a lot to see what have been my mistakes in my
>>>> procedure by now. I am very grateful for your help.
>>>>
>>>> All the best,
>>>>
>>>> Ida
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> On Mon, Feb 18, 2013 at 7:37 PM, Makoto Miyakoshi <mmiyakoshi at ucsd.edu>wrote:
>>>>
>>>>> 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
>>>>>>> >
>>>>>>> >
>>>>>>> >
>>>>>>> >
>>>>>>> > _______________________________________________
>>>>>>> > 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
>>>>>>> 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
>>>>>
>>>>
>>>>
>>>> _______________________________________________
>>>> 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
>>> 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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