Epilepsy is a common neurological disorder, characterized by recurrent seizures. Electroencephalogram (EEG), a useful measure for analysing the brain's electrical activity, has been widely used for the detection of epileptic seizures. Most existing classification techniques are primarily aimed at increasing detection accuracy, while the interpretability of the methods have received relatively little attention. In this work, we concentrate on the epileptic classification of EEG signals with interpretability. We propose an epilepsy detection framework, followed by a comparative study under this framework to evaluate the accuracy and interpretability of four rule-based classifiers, namely, the decision tree algorithm C4.5, the random forest algorithm (RF), the support vector machine (SVM)-based decision tree algorithm (SVM+C4.5), and the SVM-based RF algorithm (SVM+RF), in two-group, three-group, and–the most challenging of all–five-group classifications of EEG signals. The experimental results showed that RF outperformed the other three rule-based classifiers, achieving average accuracies of 0.9896, 0.9600, and 0.8260 for the two-group, three-group, and five-group seizure classifications respectively, and exhibiting higher interpretability.