[Eeglablist] ICA component

[Dorian Grelli]

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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>
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