Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

中文翻译：

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基于简单随机采样和顺序特征选择的癫痫脑电信号分类。

脑电图（EEG）信号广泛用于医疗领域。脑电信号的主要应用是对诸如癫痫，阿尔茨海默氏病，睡眠问题等疾病的诊断和治疗。本文提出了一种从多通道脑电信号中提取和选择特征的新方法。这项研究集中在三个主要方面。首先，使用简单随机采样（SRS）技术从EEG信号的时域提取特征。其次，采用顺序特征选择（SFS）算法选择关键特征并降低数据的维数。最后，将所选特征转发到最小二乘支持向量机（LS_SVM）分类器以对EEG信号进行分类。LS_SVM分类器对从SRS和SFS中提取和选择的特征进行分类。