Epilepsy is the fourth most frequent neurological disorder. Epileptic seizures are the result of temporary electrical disturbances in the brain. This disorder can be diagnosed by electroencephalograms (EEG). Accordingly, data mining supported by machine learning (ML) methods can be used to find patterns in EEG and to build classifiers. However, the presence of physiological abnormalities is considered rare in medical data. Also, rhythmic activities in an EEG of a healthy person varies in different situations, such as with closed or opened eyes. Other issue is that normal and interictal epileptic EEG can contain similar patterns. In this work, we developed classifiers in order to differentiate EEG of healthy volunteers, with opened and closed eyes, from epileptic EEG. This approach was applied in an EEG set with 500 segments, in which cross-correlation and power spectrum methods were performed for feature extraction. Subsequently, predictive models based on one-class classification were built using the one-class support vector machine (OSVM) technique in addition to traditional ML methods, such as nearest-neighbor, artificial neural network, and two-class support vector machine (TSVM). The polynomial and radial basis function (RBF) kernels were applied in OSVM and TSVM. To enable the use of traditional ML methods, the OCC was converted to two-class classification problem through assignment of negative (abnormal) class to each EEG segment that does not belong to normal (positive) class. In the evaluation, it was found that the OSVM classifier with RBF kernel reached the best values for all confusion matrix parameters, such as accuracy, positive predictive value, sensitivity, and false positive rate.