[Eeglablist] Fwd: Applying ICA weight matrix on another dataset

Tue Feb 26 09:47:09 PST 2013

Dear Maarten,

>First of all, if I understand correctly, it is not the 2 different
sphering matrices that I need to compare (filt vs. continuous) but it is
the sphering*weights vs. the weights matrix of 1 dataset? And then check
which ones are closest to each other?

Right. Just an example how it should work and what you should expect to see.

>My code is actually completely the same as yours.

No, watch carefully. It should be icaweights*icasphere, not the other way
around. This order is kind of counterintuitive, isn't it? Try it again with
the correct order and tell me what you  get.

Makoto

2013/2/26 Maarten De Schuymer <maartendeschuymer at gmail.com>

> Hi again,
>
> First of all, if I understand correctly, it is not the 2 different
> sphering matrices that I need to compare (filt vs. continuous) but it is
> the sphering*weights vs. the weights matrix of 1 dataset? And then check
> which ones are closest to each other?
>
> My code is actually completely the same as yours.
>
>>
>> sphereMIR = getMIR(EEG.icasphere, EEG.data(:,:))
>> sphereMIR =
>>    27.4830
>> >> icaMIR = getMIR(EEG.icasphere*EEG.icaweights, EEG.data(:,:))
>> icaMIR =
>>   -19.3297
>
>
> This is the result I get when using the sphering matrix of the filtered
> epoched data (so the real sphering matrix automatically computed by the ICA
> algorithm).
>
> If I would have made a mistake in preprocessing, what should this be then?
> My preprocessing was quite straight forward: filter, epochation and then
> running ICA. I don't see how a mistake could have happened there :).
>
> I did not however reran the ICA with amica, but just used the regular ICA
> weights and spheres that I had by using the runica, might this explain this
> difference?
>
>
> 2013/2/26 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>
>> Dear Maarten,
>>
>> Hmm strange.
>> Here are my results. You must be seeing something like this.
>>
>> %%%%%%%%%%%%%%%
>> sphareMIR = getMIR(EEG.icasphere, EEG.data(:,:))
>> icaMIR    = getMIR(EEG.icaweights*EEG.icasphere, EEG.data(:,:))
>> dummyMIR  = getMIR(rand(68), EEG.data(:,:))
>>
>> sphareMIR =
>>
>>    88.0146
>>
>>
>> icaMIR =
>>
>>    90.7252
>>
>>
>> dummyMIR =
>>
>>  -197.7197
>> %%%%%%%%%%%%%%%%
>>
>> This tells you that the difference between sphering and ICA is very
>> little and distinguished from random matrix (which gives you mutual info
>> increase actually).
>>
>> Why don't you give me the whole code you used. If I don't find anything
>> wrong there you must have made a mistake in preprocessing.
>>
>> Make sure you use same data (EITHER epoched or continuous) for comparison.
>>
>> Makoto
>>
>> 2013/2/26 Maarten De Schuymer <maartendeschuymer at gmail.com>
>>
>>> Dear Makoto,
>>>
>>> I do have some problems interpreting the output that is given by the
>>> getMIR.
>>> In what way should this output be interpreted?
>>>
>>> In the following my results based on the epoched filtered data (so the
>>> sphering matrix obtained with the ICA dataset) and the newly computed
>>> sphering matrix (continuous). The inputs were in both case the same
>>> (EEG.data) and the W was either W_epoch = EEG.icaweights*sphere_epoch or
>>> W_cont = EEG.icaweights*sphere_continuous.
>>>
>>> Obtained outputs:
>>>> mir_epoch: -19.3297
>>>> v_epoch: 0.000046
>>>>
>>>
>>>
>>>> mir_cont: -30.7652
>>>> v_cont: 0.000045
>>>
>>>
>>> How come these values are negative? In what way should these be
>>> interpreted?
>>>
>>> Thanks for the help; when everything is clear, I will send the results
>>> to the list.
>>>  Maarten
>>>
>>>
>>> 2013/2/26 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>>>
>>>> Dear Maarten,
>>>>
>>>> Yes. If you find any problem let me know.
>>>>
>>>> Makoto
>>>>
>>>>
>>>> 2013/2/26 Maarten De Schuymer <maartendeschuymer at gmail.com>
>>>>
>>>>> Hi Makoto,
>>>>>
>>>>> thank you for the file. I will check how long it takes and in the
>>>>> worst case scenario I can also run it via a central server.
>>>>> So after running amica I can use the function getMIR() to check which
>>>>> W works better, right?
>>>>>
>>>>> Thanks for all the help.
>>>>>
>>>>> Maarten
>>>>>
>>>>>
>>>>> 2013/2/26 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>>>>>
>>>>>> Dear Maarten,
>>>>>>
>>>>>> Here is the file. Good luck.
>>>>>> I CCed to Jason so that he can check the repository.
>>>>>>
>>>>>> I'm not sure if it's a good idea to use AMICA in 4-8 core CPUs. We
>>>>>> usually use 32-64 (which is a dedicated server for parallel computation).
>>>>>> You can try it anyway to see how long it takes. The default iteration is
>>>>>> 2000 which may be too many in that case. Some of us uses 750 for your
>>>>>> information.
>>>>>>
>>>>>> Makoto
>>>>>>
>>>>>>
>>>>>>
>>>>>> 2013/2/25 Maarten De Schuymer <maartendeschuymer at gmail.com>
>>>>>>
>>>>>>> Dear Makoto,
>>>>>>>
>>>>>>> I am having some troubles installing the amica toolbox. I followed
>>>>>>> the steps on the site, but after running the command in terminal  (I have a
>>>>>>> mac, 64 bit), it didn't find the runamica12.m file anywhere. Subsequentely,
>>>>>>> I searched for this file and found it on Jason Palmer's site. However, I'm
>>>>>>> still not able to execute runamica because I don't find AMDIR in that file
>>>>>>> (I suppose it has been renamed for another version), which -according to
>>>>>>> the wiki- needs me to specify the path . Moreover, I don't find any info on
>>>>>>> the getMIR() function online.
>>>>>>> Any help on this would be highly appreciated.
>>>>>>>
>>>>>>> Best,
>>>>>>> Maarten
>>>>>>>
>>>>>>>
>>>>>>> 2013/2/22 Makoto Miyakoshi <mmiyakoshi at ucsd.edu>
>>>>>>>
>>>>>>>> Dear Maarten,
>>>>>>>>
>>>>>>>> I asked it to Nima offline at SCCN B177E. He said categorically no
>>>>>>>> because ICA weight matrix is very dependent of the sphering matrix.
>>>>>>>>
>>>>>>>> By the way you can download a function getMIR() which is included
>>>>>>>> in the amica toolbox downloadable from the following URL link:
>>>>>>>> http://sccn.ucsd.edu/wiki/Amica_Download
>>>>>>>> This function would eventually tells you which W works better in
>>>>>>>> terms of mutual information reduction (this unmixing matrix W should be a
>>>>>>>> product of sphering matrix x ica weight matrix). Let us know if you find
>>>>>>>> something interesting.
>>>>>>>>
>>>>>>>> Makoto
>>>>>>>>
>>>>>>>> 2013/2/22 Maarten De Schuymer <maartendeschuymer at gmail.com>
>>>>>>>>
>>>>>>>>> Dear list,
>>>>>>>>>
>>>>>>>>> I am still trying to figure out which is the correct sphering
>>>>>>>>> matrix when applying ICA weights to another version of the same dataset.
>>>>>>>>> Is there an expert who can weight in on this issue?
>>>>>>>>>
>>>>>>>>> Thanks a lot,
>>>>>>>>> Maarten De Schuymer
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> ---------- Forwarded message ----------
>>>>>>>>> From: Maarten De Schuymer <maartendeschuymer at gmail.com>
>>>>>>>>> Date: 2013/2/14
>>>>>>>>> Subject: Applying ICA weight matrix on another dataset
>>>>>>>>> To: eeglablist at sccn.ucsd.edu
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Dear list,
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I have a question concerning the role of the sphering matrix
>>>>>>>>> (which decorrelates the channels) in the rather common scenario where I
>>>>>>>>> compute an ICA on one version of a dataset, but then apply the ICA results
>>>>>>>>> to another version of the same data (e.g. epoched vs. continuous, filtered
>>>>>>>>> vs. unfiltered).
>>>>>>>>>
>>>>>>>>> To remove artifacts in my study, I compute the ICA on high-pass
>>>>>>>>> filtered (e.g. 1 Hz) data, because this results in much better ICA
>>>>>>>>> decompositions. However, I would like to apply the results of this ICA to
>>>>>>>>> my original, unfiltered version of the same dataset, because would like to
>>>>>>>>> keep slow potentials (< 1 Hz) in the data. After running ICA on the
>>>>>>>>> filtered data, I save both EEG.icaweights and EEG.icasphere.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> When I now apply the ICA weight matrix to the original data it is
>>>>>>>>> unclear to me which sphering matrix needs to be used.
>>>>>>>>>
>>>>>>>>> Should I (A) also import the ICA sphering matrix from the filtered
>>>>>>>>> data or (B) recompute the sphering matrix (cmd: sphere(EEG.data)) for the
>>>>>>>>> original unfiltered data and consequently use that one. Both possibilities
>>>>>>>>> result in different outcomes since sphering matrices are different for both
>>>>>>>>> versions of the datasets. Which of these possibilities are recommended and
>>>>>>>>> more importantly, why exactly?
>>>>>>>>>
>>>>>>>>> A related question concerns the exporting-importing of the weight
>>>>>>>>> matrix in the GUI of EEGLAB. When exporting weights, a single exported file
>>>>>>>>> contains the combined weight*sphering matrix. However, when importing, two
>>>>>>>>> different files need to be imported, i.e. both weight matrix and sphere
>>>>>>>>> matrix separately. This does not seem practical. Or is there a rationale
>>>>>>>>> behind this distinction between import and export?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Thanks for any input on this,
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Best
>>>>>>>>> Maarten De Schuymer
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> _______________________________________________
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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
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> 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
>>
>
>

-- 
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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