[Eeglablist] ICA component

Dorian Grelli dorian.grelli at gmail.com
Wed Oct 28 05:22:17 PDT 2015

I definetly got what you mean. I've to try different methods and try to
inderstand which is the best with my setting. I've to say that EEG cleaning
is quite a jungle. There are tons of different methods and it's very
difficult to say which is the most appropriate. I've also seen that many
authors arbitrarly choose a cleaning method but often no one can say why
they choose it and they don't explain their decision.
I've seen right know that if I process multiple datasets with runica(), a
pop windows will ask to me if I want to concatenate multiple dataset as
they are from the same subject. Would be possible that it gives back a more
reliable components with this concatenation?

2015-10-27 23:15 GMT+01:00 Tarik S Bel-Bahar <tarikbelbahar at gmail.com>:

> Addendum below, cheers!
> If the trials over your 2 minute recording periods are the same kind of
> trials, then you can join them to get a representative ICA decomposition. I
> think researchers tend to take all "similar cognitive periods or tasks" and
> put them together for ICA. When and if you get good components out, you'll
> then be able to re-apply to the continuous data, segment the different
> trials, and then check out component dynamics between conditions.
> You should find at least an eye-artifact IC in decompositions of brief
> periods (like just 5 minutes total given to ICA), that have been cleaned
> properly, but where eye blinks and eye artifacts occur multiple times in
> the data and are retained in the data. If you're using ICA strictly for
> cleaning out eye-artifacts, then your approach will likely yield one or two
> known artifactual ICs that you can remove with some confidence. It's best
> to run the analysis by cleaning in a traditional way and an ICA way, and
> comparing them for yourself.
> See also several articles over last 5 years summarizing various EEG
> cleaning approaches, including specific foci on eye-artifact and
> muscle-artifact detection. See also recent tools like the PREP and TAPEEG
> pipeline toolboxes for eeglab and matlab, respectively.
> At 20 channels you are at the lowest (or below) the limit of channels
> usually used for ICA-EEG approaches. You may want to consider a traditional
> non-ICA based approach. I think several past posts on eeglab list have
> talked about the appropriateness of ICA for sparse EEG.
> 20 channels with a lot of time should work to give you interpetable
> artifactual and "true/cortical/cognitive" ICs. you may even want to
> downsample in time. Though we're pro-ICA, you should take a look at
> microstate decompositions which I think have been done a lot more with <32
> channel data sets, and "produce" scalp maps that are similar to some of the
> main alphabet of ICs found with ICA.
> You should also see what you get, and definitely play with tools like
> SASICA or others. Note that there are at least ~5 to dozen ICs that show up
> regularly in most datasets, across most studies that use ICA. see the
> onton/makeig chapter, or other work, for those exemplars.
> You may want search "cleaning approaches for sparse EEG" on google
> scholar. Techniques from ~1950 to 2000 dealt primarily with sparse EEG, and
> there are plenty of matlab implementations (e.g., Gratton's regression
> based approach).
> On Tue, Oct 27, 2015 at 5:18 PM, Dorian Grelli <dorian.grelli at gmail.com>
> wrote:
>> Thank you again Stephan and thank you Tarik. Just for being sure..."124"
>> is the seconds of my dataset? Maybe you meant 120 Stephan.
>> My epochs are 4 seconds long and my datasets are parts of a single
>> session where we perform 2' minutes recording every 10' minutes
>> no-recording so, at the end, we got 6 datasets per each subject. Would be
>> ok to join the 6 datasets in a single file in order to get a more reliable
>> ICA? In this case, after removing artifactual components, I will have to
>> split again the data because we are interested in time course and we want
>> to analyse each recording phase.
>> I sent my first email because I really want to understand if it makes
>> sense or not running something like SASICA for removing artifacts. In order
>> to do that ICA has to be reliable.
>> Subjects were forced to keep eyes closed during the recording phases and,
>> maybe, I don't have too many artifacts. Maybe. I already reject bad epochs
>> which passed a treshold or the amplitude.
>> The problem is that I am self thought in this field (like many of you I
>> suppose) so I am quite ok in some aspects and very bad in other things
>> (like trails = epochs). However thank you for the suggestions Tarik!
>> Il 27/Ott/2015 18:13, "Stephen Politzer-Ahles" <
>> stephen.politzer-ahles at ling-phil.ox.ac.uk> ha scritto:
>>> In that case it sounds like this is probably ok. Using the formula
>>> listed above, you have (124*500)/(20^2) = 155 points per weight, which is
>>> much more than the sample dataset. Also now that I think of it, 2 minutes
>>> of epoched data is still a decent amount (that's e.g. 120 one-second
>>> epochs, I've done ICA on comparable sizes of data before).
>>> ---
>>> Stephen Politzer-Ahles
>>> University of Oxford
>>> Language and Brain Lab, Faculty of Linguistics, Phonetics & Philology
>>> http://users.ox.ac.uk/~cpgl0080/
>>> On Tue, Oct 27, 2015 at 4:56 PM, Dorian Grelli <dorian.grelli at gmail.com>
>>> wrote:
>>>> Thank you Stephan for claryfing many points. My sampling rate is 500 hz.
>>>> Il 27/Ott/2015 17:53, "Stephen Politzer-Ahles" <
>>>> stephen.politzer-ahles at ling-phil.ox.ac.uk> ha scritto:
>>>>> Hello Dorian,
>>>>> Regarding your last question, you get as many independent components
>>>>> as you have channels; you had 20 channels, which is why you got 20
>>>>> components. Examples with 256 components would have come from 256-channel
>>>>> caps.
>>>>> As for just how much data is enough, other people on the list can
>>>>> probably answer that better than me. 2 minutes does sounds very short. But
>>>>> this also depends on your sampling rate (as mentioned in the paragraph you
>>>>> quoted); 2 minutes of 1000 Hz data (i.e., sampled every millisecond) is a
>>>>> lot more than 2 minutes of 250 Hz data (i.e., sampled once every four
>>>>> milliseconds).
>>>>> Also, a trial is the same thing as an epoch.
>>>>> ---
>>>>> Stephen Politzer-Ahles
>>>>> University of Oxford
>>>>> Language and Brain Lab, Faculty of Linguistics, Phonetics & Philology
>>>>> http://users.ox.ac.uk/~cpgl0080/
>>>>> On Tue, Oct 27, 2015 at 11:02 AM, Dorian Grelli <
>>>>> dorian.grelli at gmail.com> wrote:
>>>>>> Hi guys,
>>>>>> I am very new with eeg data analysis and it would be great to have
>>>>>> some support from you!
>>>>>> I found the quotation below from this tutorial:
>>>>>> http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA
>>>>>> *"Very important note: We usually run ICA using many more trials that
>>>>>> the sample decomposition presented here. As a general rule, finding Nstable
>>>>>> components (from N-channel data) typically requires more than kN^2 data
>>>>>> sample points (at each channel), where N^2 is the number of weights in the
>>>>>> unmixing matrix that ICA is trying to learn and k is a multiplier. In our
>>>>>> experience, the value of k increases as the number of channels increases.
>>>>>> In our example using 32 channels, we have 30800 data points, giving
>>>>>> 30800/32^2 = 30 pts/weight points. However, to find 256 components, it
>>>>>> appears that even 30 points per weight is not enough data. In general, it
>>>>>> is important to give ICA as much data as possible for successful training.
>>>>>> Can you use too much data? This would only occur when data from radically
>>>>>> different EEG states, from different electrode placements, or containing
>>>>>> non-stereotypic noise were concatenated, increasing the number of scalp
>>>>>> maps associated with independent time courses and forcing ICA to mixture
>>>>>> together dissimilar activations into the N output components. The bottom
>>>>>> line is: ICA works best when given a large amount of basically similar and
>>>>>> mostly clean data. When the number of channels (N) is large (>>32) then a
>>>>>> very large amount of data may be required to find N components. When
>>>>>> insufficient data are available, then using the 'pca' option to jader.m
>>>>>> <http://sccn.ucsd.edu/eeglab/locatefile.php?file=jader.m>** to find
>>>>>> fewer than N components may be the only good option.*"
>>>>>> I don't know if each of my datasets has enough datapoints for
>>>>>> performing an ICA. Each dataset has 20 channels, last 2 minutes and is 4
>>>>>> seconds epoched, baseline corrected and pass band filtered. I also reject
>>>>>> bad epochs.
>>>>>> Which is the meaning of "trials" in the quotation above? Would be
>>>>>> better to have longer registrations?
>>>>>> When I run ICA I got 20 components. Why are there some examples with
>>>>>> 256 components?
>>>>>> Dorian
>>>>>> Dorian
>>>>>> _______________________________________________
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