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：癫痫发作是一类以脑部异常过度电活动为特征的神经系统疾病，表现为反复发作的癫痫事件。脑电图（EEG）信号因其能够捕捉时空神经动力学而被广泛用于癫痫诊断。尽管近期深度学习方法在检测准确性上取得较高水平，但往往缺乏可解释性和神经生理学相关性。本研究提出一种基于发作期脑电分析的频率感知癫痫检测框架。原始EEG信号被分解为五个频段（δ、θ、α、低频β与高频β），并从各频段提取11种鉴别性特征。继而采用图卷积神经网络（GCN）建模以EEG电极为图节点表示的空间依赖性。在CHB-MIT头皮脑电数据集上的实验表明，该方法在相应频段上分别达到97.1%、97.13%、99.5%、99.7%和51.4%的检测准确率，全频段综合准确率为99.01%。结果凸显中频段的强区分能力，并揭示频率特异的发作模式。与传统宽带EEG方法相比，所提方法在可解释性与诊断精度上均有所提升。