[Eeglablist] Problem with ICA decomposition

[Antonio Maffei]

​Hi Tarik​,

thank you for your quick response.

I have one more questions about this issue:
- When I check data rank of my raw CNT file I have a rank of 37, that is
the effective number of rows of my matrix. Then, when I check the rank of
my data after average re-referencing and adding Cz back I have a rank of
38, that is the effective number of rows of my new matrix. Thus, if I
understand correctly, for both my datasets the matrix is full-ranked.
Should I find instead a data rank of N-1, rather than a data rank of N, due
to the rank deficiency determined by the average reference?

Antonio

Antonio Maffei, Ph.D. Student

Department of General Psychology (DPG)
University of Padova
Via Venezia 8 - 35131
Padova, Italy

email: antonio.maffei at phd.unipd.it
office: 049 8276256

2016-07-21 21:48 GMT+02:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:

> Hello Antonio, You should have enough data to make ICA happy, many groups
> use much less time and few channels but get valid-enough artifact-ICs. Your
> issue might be with rereferencing and adding CZ, and perhaps not fixing the
> rank. If you haven't had a chance to, please review Makoto's pipeline
> mentioned in the eeglablist, and the online eeglab tutorial. Googling
> eeglablist may also help. Some other notes are listed below.
>
> ***************
>
> remember to remove 1 channel to reduce rank for ICA, to reflect reduced
> rank due to the average rereferencing. Please google eeglablist for past
> mentions of this topic.
>
> Yes initial steps should take a long time, in general. It does not seem
> like it's an issue with your computer or installation.
>
> consider downsampling the signal, or taking just half of the total signal
> time. This should confirm the speedups you expect.
>
> consider going to 1-50 hz initially, this should "catch" any stereotyped
> components ICA can "see" in the data.
>
> consider demeaning and/or detrending the data
>
> consider detect+remove bad channels and not interpolating them before
> average rereferencing
> [try to make sure you end up with at least 25 channels and that they are
> well distributed across the scalp.]
>
> consider the Cz in via interpolation before average rereferencing [or just
> leave out for now and interpolate it in after ICA-cleaning). In or out,  it
> should not make much of difference in the hunt for ICA-derived stereotyped
> artifacts.
>
>
>
>
>
>
>
>
> On Wed, Jul 20, 2016 at 4:42 AM, Antonio Maffei <
> antonio.maffei at phd.unipd.it> wrote:
>
>> Dear all,
>>
>> I am stepping in some problems when running ICA decomposition for
>> artifact detection.
>>
>> My dataset consists in a continous 38 channels 70 minutes long recording,
>> sampled at 500 Hz referenced to Cz.
>>
>> My preprocessing steps are the following:
>> - Re-reference to the average reference and adding Cz to the recording
>> -Filter with a band-pass filter set at 1 - 100 Hz
>> - Visual inspection of the recording and removal of big noisy artifacts,
>> mainly movement artifacts, as suggested in the EEGLAB tutorials
>>
>> After these steps my dataset consists of 1864585 data points on which I
>> perform *runica* with the default options ('extended', '1').
>>
>> I noticed that the process is very slow, and the algorithm needs to
>> lowering the learning rate many times at the beginning but even so it seems
>> that it fails to converge, since the wchange values does not decrease
>> progressively (as they should) and it fails to reach the stop criterium
>> (wchange <1e-07).
>>
>> As a consequence I get a bad decomposition with uninterpretable
>> components that prevent their use for artifact correction.
>>
>> I am wondering if this problem is related to the amount of data points
>> fed to the ICA, since when I preprocessed shorter recordings I have not
>> encountered such difficulties, or I am making some mistakes during my
>> pipeline.
>>
>> A great thank to anyone who can help me.
>>
>> Antonio
>>
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>
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