Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework

► We present a novel automatic technique for epileptic EEG classification into the normal, interictal, and ictal activities. ► We use eigenvalues extracted from wavelet coefficients as features. ► Several supervised learning based classifiers were trained using selected features. ► We demonstrate that our proposed technique can yield 99% classification accuracy.