## 1 Introduction  
Worldwide, around 60 million people are affected by epilepsy disorder, indicating frequent occurrence of seizure events due to hyper-synchronous firing of neuron clusters [1]. Electroencephalogram (EEG) signals are frequently used as a convenient and inexpensive tool for the diagnosis of epilepsy. Manual monitoring of EEG signals for the detection of epileptic seizure is a time-consuming task even for highly-trained neurologists. Thus, automated methods of seizure detection based on advanced signal processing techniques will significantly reduce the burden of monitoring lengthy EEGsignals by clinicians. Many studies have been carried out in developing automated systems, which extract discriminative features for the classification of epileptic EEG signals [2; 3]. Classification of epileptic seizure EEG signals has been performed by exploiting the underlying non-stationarity and nonlinearity of EEG signals in previous studies. In [2], the authors used the area of the phase space representations of the intrinsic mode functions (IMFs) as features and achieved a classification accuracy of 98.67% in classifying seizure-free and seizure EEG signals. In [3], bandwidth contributions due to amplitude and frequency modulations of IMFs have been considered as features for the classification of non-seizure and seizure EEG signals and achieved classification accuracy of 99.5-100%. Epileptic seizure EEG signal classification has been performed by extracting area features from the second order difference plot and analytic signal representation of IMFs extracted from EEG signals using empirical mode decomposition (EMD) [4]. Non-stationary signal decomposition techniques and time-frequency domain analysis for the classification of epileptic EEG signals have been studied extensively. In [5], the features extracted from time-frequency distributions, namely Wigner-Ville distribution and short time Fourier transform (STFT), are used for the classification of epileptic seizure EEG signals with the artificial neural-network classifier. They achieved 100% classification accuracy when classifying seizure EEG signals from normal EEG signals. [PERSON] et al. [6] have extracted texture features from scalogram images of EEG signals with higher order spectra (HOS) features and classified seizure, seizure-free and normal EEG signals with a classification accuracy of 96%. In [7], the authors applied wavelet transform on EEG signals to decompose into sub-bands and computed statistical features from these sub-bands. The data dimension was reduced using linear discriminant analysis (LDA), independent component analysis (ICA) and principal component analysis (PCA) followed by support vector machine (SVM) and obtained the highest classification of 100% in classifying seizure and normal classes of EEG signals. In [8], epileptic seizure EEG signals have been classified using discrete wavelet transform and generalized fractal dimensions method. In [9], the authors decomposed EEG signals using tunable-Q wavelet transform (TQWT) and extracted Kraskov entropy (measured from the mathematical expression of K-nearest neighbors (K-NN) entropy) features from the decomposed sub-band signals to classify seizure and seizure-free EEG signals. They achieved classification accuracy of 97.75% with the least squares support vector machine (LS-SVM) classifier. In [10], the authors decomposed EEG signals using TQWT and extracted spectral features from the TQWT decomposed sub-band signals followed by the bagging algorithm to classify epileptic EEG signals. They achieved classification accuracy of 98.40% in classifying seizure, seizure-free and the normal classes of EEG signals. The classification of epileptic seizure EEG signals has been carried out by several other nonlinear features, namely recurrence quantification analysis (RQA) [11], Hurst exponent [12] and approximate entropy (ApEn) [13]. Local binary pattern (LBP)-based methods have been suggested in recent studies for the classification of epileptic seizure EEG signals.