Common Spatial Pattern for EEG Signals
I am not sure I understand your question, but for the particular question


  How are features of the EEG Signal extracted then?


CSP gives you a matrix $W$ that represents a spatial transformation of your EEG $X$. When using CSP, $W^TX$ will have the property of maximal variance for one class while minimal variance on the other class. Therefore, when using CSP for classification, the features are the variance of $W^TX$. In practice, the log-variance has a better distribution.

An interesting and brief explanation of CSP can be found on doi:10.1109/MMSP.2010.5662067 (until section II, then it goes on Riemannian geometry which might be out of your scope).