[Homer-users] Question about PCA-filter function

[Dung C Nguyen]

Principal components analysis (PCA) is a procedure for identifying a
smaller number of uncorrelated variables, called "principal components",
from a large set of data. The goal of principal components analysis is to
explain the maximum amount of variance with the fewest number of principal
components.
Therefore, the number of principal components in PCA filter is less than or
equal to the number of original variables in your data set.

best,


On Mon, Sep 26, 2016 at 1:27 AM, Thien Nguyen <thien at gist.ac.kr> wrote:

> Hello all,
>
>
> I am now using homer2 to process a set of resting state fNIRS data.
>
>
> I want to use PCA-filter to remove motion artifact.
>
>
> When applying PCA-filter function, there is a text box for me to input a
> number.
>
>
> Can anyone tell me what is meaning of that number?
>
>
> I guess it is the percent of principle components that we want to remove,
> is it true?
>
>
> Then, what value should I put there? (maybe not a specific value, but then
> what is a typical range?)
>
>
> Thank you for your time,
>
> Thien Nguyen
>
>
>
>
>
> *Nguyen Thi Thien* PhD candidate, biophotonics labratory
> *Gwangju insitute of Science and Technology*
> 123 cheomdangwagi-ro, Buk-gu, Gwangju, 61005,  Repblic of KOREA
> Tel: (+82) 62 715 2636
> Mobile: (+82) 10 9456 8843
> E-mail: *nguyenthienysinh at gmail.com* <nguyenthienysinh at gmail.com>,
> *thien at gist.ac.kr* <thien at gist.ac.kr>
> Website: *biophotonics.gist.ac.kr* <http://biophotonics.gist.ac.kr/>
>
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