[Eeglablist] ICA component

[Makoto Miyakoshi]

Dear Dorian,

> Which is the meaning of "trials" in the quotation above? Would be better
to have longer registrations?

There is not description about 'trials' in the tutotial you cited.

> When I run ICA I got 20 components. Why are there some examples with 256
components?

The example uses 256ch system. In this case, ICA returns 256 ICs (if full
rank i.e. no channels bridged).

> I don't know if each of my datasets has enough datapoints for performing
an ICA.

Let's assume your data are sampled at 250 Hz. Your data length is 2 min, so
you have 250 x 120 = 30,000 data points. Your k is 30,000 / (20^2) = 75,
which is way above 30. You'll be fine.

Makoto

On Tue, Oct 27, 2015 at 4:02 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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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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