which COEF values to keep in PRINCOMP for EEG feature dimension reduction ?
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My EEG data contains 118 channels. Suppose for each trial I extract 4 features. Since all 118 channels have data for that trial, I have to extract 4 features from all those 118 channels.
So my feature matrix for each trial becomes 118*4 (channels*feature).
X is of size 118*4
[coef score latent]=princomp(X) %
Should I use princomp(X) or princomp(X')? If I use princomp(X), COEF is 4*4.
If LATENT shows that first 2 values have the most variance, should I use COEF(1:2,:) or COEF(:,1:2) as features? I mean first 2 rows or columns should I consider?
Will I use these 2*4 values as features instead of whole 118*4?
Please help
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