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Automatic Classification of epileptic signals plays a critical role in long term monitoring and diagnosis. This review paper provides a survey of novel features of extraction and classification techniques for classifying epilepsy using Extreme Learning Machine (ELM) and Support Vector Machines (SVM) for Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD). The EEG signal is decomposed in Intrinsic Mode Functions (IMFs). Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. ELM and SVM are used as a classifier to classify EEG signals. For VMD, the EEG signal is decomposed to Intrinsic Mode Functions (IMFs) in the first place which are to be extracted as a feature after determining optimal features using classifiers like SVM and ELM. Keywords— Epileptic, Electroencephalogram (EEG), Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Support Vector Machines (SVM), Extreme Learning Machine (ELM)

A Survey of Classification of EEG signals using EMD and VMD for Epileptic Seizure Detection