[Eeglablist] Component clustering and PCA reduction

[Makoto Miyakoshi]

Dear Clemens,

> As for example, I want to reduce the dimensions of ERSP/dipoles, spectra,
given all ICs, which dimensions, what are the dimensions that would be
reduced for the respective measures?

If I remember correctly, EEGLAB manual says

1. Set the parameters so that total number of dimensions across all the
measures are <20-30 because k-means clustering performance becomes worse if
>30.

2. Do not use 'final dimension' thing.

I think it's a good idea to keep it below 20 to see how it works.
I personally recommend you use just dipole and just a little bit (weight 1
- 3) of spectrum with dimension 10-15. It's a good idea to avoid the
measure which you'll test using statistics in the end to avoid 'double
dipping' issue (if you don't know what it is, google 'voodoo correlation'.)

Makoto


On Tue, May 24, 2016 at 1:42 AM, Clemens DICKHUT <clemens.dickhut at uni.lu>
wrote:

> Dear all,
>
> I have some trouble understanding the reduction of dimensionality on
> measures selected for component clustering. (STUDY —> build pre-clustering
> array)
>
> As for example, I want to reduce the dimensions of ERSP/dipoles, spectra,
> given all ICs, which dimensions, what are the dimensions that would be
> reduced for the respective measures?
>
> I am grateful for any input.
>
> Best,
> Clemens
>
> __________________________________
> Clemens Dickhut, M.Sc., (PhD Student)
> Institute for Health and Behaviour
> Research Unit INSIDE
> University of Luxembourg - Campus Belval
> Maison des Sciences Humaines
> 11, Porte des Sciences, R. 04 415
> L-4366 Esch-sur-Alzette
> Tel.: (+352) 46 66 44 9536
>
>
>
>
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-- 
Makoto Miyakoshi
Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego
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