Electroencephalogram (EEG), a record of electrical signal to represent the human brain activity, has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification of EEG signals is the crucial task to detect the stage of ictal (i.e., seizure period) and interictal (i.e., period between seizures) signals for the treatment and precaution of the epileptic patient. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering their non-abruptness phenomena and inconsistency in different brain locations. In this paper, we present a new approach for feature extraction and classification by exploiting temporal correlation within an EEG signal for better seizure detection as any abruptness in the temporal correlation within a signal represents the transition of a phenomenon. In the proposed methods we divide an EEG signal into a number of epochs and arrange them into two-dimensional matrix and then apply different transformation/decomposition to extract a number of statistical features. These features are then used as an input to least square support vector machine to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark datasets and different brain locations.