Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures that can be identified in an Electroencephalogram (EEG) signal. This paper proposes a low computational complex method to classify epileptic EEG signals by using a suitable Ramanujan Periodic Subspace (RPS). Initially, this method divides the given single channel EEG signal into multiple non-overlapping EEG blocks, which are projected onto a particular RPS. The energy of the projection is used as a feature to classify each block into epileptic or non-epileptic, using an SVM binary classifier. Here, in order to choose that particular RPS, a few sample blocks from the epileptic (ictal), interictal, and healthy EEG signal are projected onto the divisor RPSs of that block. The difference between the average subspace energy of healthy vs ictal or interictal vs ictal blocks is used as a measure to choose the suitable RPS. Finally, the class of each block of the EEG signal is combined using the majority voting scheme to classify the epileptic EEG signal. A publicly available benchmark EEG database from Bonn University, Germany, is used to evaluate the performance of the proposed method. Further, the EEG signals are added with White Gaussian Noise (WGN) and ocular artifact for testing the robustness of the method against noise. Evaluation results in terms of accuracy, sensitivity, specificity, and F-score demonstrate that the proposed method is comparable with the state-of-the-art techniques and also robust against artifacts and noise. IEEE