[Eeglablist] Rejection based on independent data components

[Bagas Isadewa]

hello,

I little bit confused about the method sequence described in eeglab wikitorial 
to reject artifactual component based on ICA

here step-by-step methods :

1. Visually reject unsuitable (e.g. paroxysmal) portions of the continuous data.
2. Separate the data into suitable short data epochs.
3. Perform ICA on these epochs to derive their independent components.
4. Perform semi-automated and visual-inspection based rejection on the derived 
components.*
5. Visually inspect and select data epochs for rejection.
6. Reject the selected components and data epochs.
7. Perform ICA a second time on the pruned collection of short data epochs -- 
This may improve the quality of the ICA decomposition, revealing more 
independent components accounting for neural,  as opposed to mixed artifactual 
activity. If desired, the ICA unmixing and sphere matrices may then be applied 
to (longer) data epochs from the same continuous data. Longer data epochs are 
useful for time/frequency analysis, and may be desirable for tracking other slow 
dynamic features.

from the step above we must run ICA twice, my question is :
1. what is ICA exactly did (step no. 7) on the decomposed data ? is there anyone 
can explain ?

2. is it necessary to run ICA two times ? what if I just run ICA once and then I 
perform visual inspection on the decomposed result in order to find artefact 
signal ?
example :
    - first, I reject visually unsuitable epoch data
    - second, run ICA
    - third. perform visually inspection in order to find bad components 

I appreciate all respond to my question, thank you :)


best regards,
bagas isadewa



      
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