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

2026-03-31

AI Summary

This study addresses the limitation of conventional broadband EEG approaches that overlook frequency-band-specific neurophysiological characteristics by proposing a band-aware framework for epileptic seizure detection. The method decomposes EEG signals into five canonical frequency bands, extracts discriminative time-frequency features from each, and models inter-electrode spatial dependencies using a graph convolutional neural network. Evaluated on the CHB-MIT dataset, the framework achieves an overall accuracy of 99.01%, with mid-frequency bands—particularly alpha and beta—yielding the highest performance at 99.7%. Beyond improving detection accuracy, this work enhances model interpretability through differential analysis across frequency bands, offering insights grounded in neurophysiological principles.

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 EEG dataset demonstrate high detection performance, achieving accuracies of 97.1%, 97.13%, 99.5%, 99.7%, and 51.4% across the respective frequency bands, with an overall broadband accuracy of 99.01%. The results highlight the strong discriminative capability of mid-frequency bands and reveal frequency-specific seizure patterns. The proposed approach improves interpretability and diagnostic precision compared to conventional broadband EEG-based methods.

Problem

• Epileptic Seizure Detection
• EEG Signals
• Frequency Bands
• Interpretability
• Neurophysiological Relevance

Innovation

• frequency-aware analysis
• graph convolutional neural network
• EEG feature extraction
• epileptic seizure detection
• multi-band decomposition