  Electroencephalogram, an influential equipment for analyzing humans
activities and recognition of seizure attacks can play a crucial role in
designing accurate systems which can distinguish ictal seizures from regular
brain alertness, since it is the first step towards accomplishing a high
accuracy computer aided diagnosis system (CAD). In this article a novel
approach for classification of ictal signals with wavelet based cross frequency
coupling (CFC) is suggested. After extracting features by wavelet based CFC,
optimal features have been selected by t-test and quadratic discriminant
analysis (QDA) have completed the Classification.
