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.

翻译：癫痫发作是一种以脑内异常过度的电活动为特征的神经系统疾病，会导致反复发作事件。脑电图（EEG）信号因其能捕捉时空神经动力学而被广泛用于癫痫诊断。尽管近年深度学习方法取得了较高检测准确率，但往往缺乏可解释性与神经生理学关联性。本研究提出一种基于发作期EEG分析的频率感知框架用于癫痫发作检测。原始EEG信号被分解为五个频段（δ波、θ波、α波、低β波和高β波），并从每个频段提取十一个判别性特征。随后采用图卷积神经网络（GCN）对以图节点形式表示的EEG电极空间依赖性进行建模。在CHB-MIT头皮EEG数据集上的实验表明，该方法在各频段分别实现了97.1%、97.13%、99.5%、99.7%和51.4%的检测准确率，全频段综合准确率达99.01%。结果凸显了中频段的强判别能力，并揭示了频率特异性发作模式。与基于传统宽频EEG的方法相比，所提方法提升了可解释性与诊断精确度。