Epilepsy is one of the common neurological disorders characterized by a sudden and recurrent malfunction of the brain that is termed “seizure”, affecting over 50 million individuals worldwide. The Electroencephalogram (EEG) is the most influential technique in detection of epileptic seizures. In recent years, many research works have been devoted to the detection of epileptic seizures based on analysis of EEG signals. Despite remarkable work on seizure detection, there is no generic seizure detection scheme which performs reasonably well for different patients and different brain locations. In this paper we present a generic approach for feature extraction of preictal (pre-stage of seizure onset) and interictal (period between seizures) EEG signals using empirical mode decomposition (EMD) along with discrete cosine transformation (DCT) by exploit temporal correlation for detection of preictal phase of epileptic seizure. Then least square support vector machine is applied on the features for classifications. Results demonstrate that our proposed method outperforms the state-of-the-art methods in terms of sensitivity, specificity and accuracy to classify preictal and interictal EEG signals to the benchmark dataset extracted from different brain locations of different patients.