# SVM results - worse when standardizing data than without

I have been testing a SVM binary classification on EEG data set. We use some features of 240 components, which are already defined in the interval 0-1. With them I obtain an AUC of approximately 80% (for a reasonable ROC).

When I standardize the data (mean 0 and var 1) the performance decreases to approx AUC 50% (with a ROC more or less equal to random classification). I found this effect of standardization very strange, since all texts advise to standardize data for improving performance.

Any clues? Has anybody observed similar effects?