[Eeglablist] ICA component

Tarik S Bel-Bahar tarikbelbahar at gmail.com
Wed Oct 28 06:26:29 PDT 2015

Cheers Dorian,
I tend to concatenate my files with Merge Sets, and then do ICA.
However, you can certainly have a bunch of loaded sets and the runica
function should have option to concatenate the loaded sets as you
mentioned. I don't remember if the resultant ICA is applied to only the
last dataset, as then one needs to loop through the loaded files, apply the
new ICA, and save each loaded file.

Thinking metaphorically, Think about dark dangerous jungles as an
opportunity for researchers to systematize things: jungles are fit to be
explored, mapped, and civilized. And current base camps in the jungle are
likely to grow into warring cities.

Back to pragmatics: Major handbooks give good guidelines: have clean data,
have good tasks, have the right methods for your phenomenon, use automatic
methods knowledgeably, and provide good details about what you did. See for
example the recent Guidelines published in Psychophysiology, Mike Cohen's
recent book, and of course Luck's Introduction.

On Wed, Oct 28, 2015 at 8:22 AM, Dorian Grelli <dorian.grelli at gmail.com>

> 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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