[Eeglablist] About ICA

[Leonardo Lion]

Hi EEGLAB list,

I have quesnito reslted to the impplimentation of ICA, my quesitno belong
to the link

http://sccn.ucsd.edu/wiki/Chapter_09:_Decomposing_Data_Using_ICA

where there is this note:

*Important note:* we believe an optimal strategy is to:

   1. Run ICA
   2. Reject bad epochs (see the functions we developed to detect
   artifactual epochs and channels, if any, in the tutorial on artifact
   rejection <http://sccn.ucsd.edu/wiki/Chapter_01:_Rejecting_Artifacts>).
   In some cases, we do not hesitate to remove more than 10% of the trials,
   even from 'relatively clean' EEG datasets. We have learned that it is often
   better to run this first ICA composition on very short time windows.
   3. Run ICA a second time on the 'pruned' dataset.
   4. Apply the resulting ICA weights to the same dataset or to longer
   epochs drawn from the same original (continuous or epoched) dataset. For
   instance, to copy ICA weights and sphere information from dataset 1 to 2:
   First, call menu Edit > Dataset info of dataset 2. Then enter
   *ALLEEG(1).icaweights* in the *ICA weight array ...* edit box,
   *ALLEEG(1).icasphere* in the *ICA sphere array ...* edit box, and press
   *OK*.


1- As I understood, we need to apply the ICA data because it is clean to
the (e.g., raw data that is not clean). Is this correct? I.E., do I need to
copy data from data number 7 to raw data (number one?)

Let's say I performed ICA and now my data reached number 7 in the
preprocess stage within eeglab. Now, I'm satisfy with the results of this
data after ICA, how can I apply this note on the data Is it necessaay to
apply the results (copy it) to the dataset 1(raw data)? if yes, then I need
to do the follwing steps:

Edit > Dataset info of dataset 1. Then enter *ALLEEG(7).icaweights* in the *ICA
weight array ...* edit box, *ALLEEG(7).icasphere* in the *ICA sphere array
...* edit box, and press *OK*.


2- What is the next step after this important note? after copying the data,
what should we do?

Thanks.
Leonardo
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