Epilepsy is a neurological condition marked by repeated involuntary seizures, requiring precise and prompt affective computing measures for its diagnosis. Epileptic seizures are characterized by a seizure phase, known as the ictal phase, which is followed by a non-seizure state referred to as the interictal phase. Accurate classification of ictal state and interictal state is challenging. Our study on the NeuCube based Brain Inspired Spiking Neural Network (BI-SNN) proposes a novel SNN based pipeline to classify seizure and non-seizure epileptic states based on Phase Locking Value (PLV) feature set. The Neucube extracts spiking activity and synaptic connectivity patterns from the PLV feature set to classify the ictal and interictal states. Classification performance metrics like Accuracy, Sensitivity, Specificity, AUC score, False Alarm, and F1 Score are used to establish the superiority of the proposed pipeline over the publicly available Siena dataset and the benchmark ML and DL methods. The suggested approach surpasses other machine learning and deep learning techniques in the Siena dataset, achieving an accuracy of 88.29%, making it feasible for accurate seizure detection.