[2604.00163] Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals

Computer Science > Machine Learning arXiv:2604.00163 (cs) [Submitted on 31 Mar 2026] Title:Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals Authors:Ferdaus Anam Jibon, Fazlul Hasan Siddiqui, F. Deeba, Gahangir Hossain View a PDF of the paper titled Epileptic Seizure Detection in Separate Frequency Bands Using Feature Analysis and Graph Convolutional Neural Network (GCN) from Electroencephalogram (EEG) Signals, by Ferdaus Anam Jibon and 3 other authors View PDF Abstract:Epileptic seizures are neurological disorders characterized by abnormal and excessive electrical activity in the brain, resulting in recurrent seizure events. Electroencephalogram (EEG) signals are widely used for seizure diagnosis due to their ability to capture temporal and spatial neural dynamics. While recent deep learning methods have achieved high detection accuracy, they often lack interpretability and neurophysiological relevance. This study presents a frequency-aware framework for epileptic seizure detection based on ictal-phase EEG analysis. The raw EEG signals are decomposed into five frequency bands (delta, theta, alpha, lower beta, and higher beta), and eleven discriminative features are extracted from each band. A graph convolutional neural network (GCN) is then employed to model spatial dependencies among EEG electrodes, represented as graph nodes. Experiments on the CHB-MIT scalp...