摘要癫痫是一种严重的慢性神经系统疾病，可通过分析由脑神经元产生的脑电信号对其进行检测，因此脑电图成为诊断癫痫的关键工具。应用特异性方法对脑电信号进行处理和分析，在探索大脑工作机制和脑神经系统疾病的诊断方面具有重要意义。本文通过对脑电图信号的特征提取、特征分类等相关分析方法（如主成分分析、独立成分分析、小波变换、线性判别分析、支持向量机、人工神经网络和决策树等）进行总结，阐述了其在癫痫治疗中的应用，概括展示了近年来的研究进展。为癫痫发作的检测和分类以及未来的研究方向提供了一定的借鉴和参考。

abstractsEpilepsy is a serious chronic neurological disorder that can be detected by analysing the brain signals generated by brain neurons, with electroencephalography (EEG) becoming a key tool in the diagnosis of epilepsy. The application of specific methods for processing and analysing EEG signals is important in exploring the working mechanisms of the brain and in the diagnosis of neurological disorders of the brain. The article describes the application of EEG signals in epilepsy treatment through feature extraction, feature classification and other related analysis methods. The article presents an overview of recent research advances through the use of principal component analysis, independent component analysis, wavelet transform, linear discriminant analysis, support vector machine, artificial neural network and decision tree. It provides some reference for the detection and classification of seizures and future research directions.