Clustering technique-based least square support vector machine for EEG signal classification
Clustering technique-based least square support vector machine for EEG signal classification
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DOI：
10.1016/j.cmpb.2010.11.014
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发表时间：
2011-12-01
影响因子：
6.1
通讯作者：
Wen, Peng (Paul)
中科院分区：
文献类型：
--
作者：
Siuly;Li, Yan;Wen, Peng (Paul)
提出了一种基于聚类技术的最小二乘支持向量机（CT-LS-SVM）的脑电信号分类方法。决策过程分两个阶段进行。第一阶段，利用聚类技术（CT）提取脑电数据的代表性特征。第二阶段，将最小二乘支持向量机（LS-SVM）应用于提取的特征，对两类脑电信号进行分类。为了验证该方法的有效性，在三个公开的基准数据库上进行了实验，一个是癫痫性脑电数据，一个是心理意象任务脑电数据，另一个是运动意象脑电数据。该方法对癫痫脑电图数据的平均灵敏度、特异度和分类准确率分别为94.92%、93.44%和94.18%；运动意象脑电数据分别为83.98%、84.37%和84.17%；分别为64.61%、58.77%和61.69%。将CT-LS-SVM算法在分类精度和运行时间上与我们之前采用简单随机抽样和最小二乘支持向量机（SRS-LS-SVM）进行脑电信号分类的研究进行了比较。我们还将所提出的方法与文献中其他针对这三个数据库的方法进行了比较。实验结果表明，该算法比已有的分类方法具有更好的分类率，并且比SRS-LS-SVM技术的执行时间要短得多。研究结果表明，该方法对两类脑电信号的分类是非常有效的。2010爱思唯尔爱尔兰有限公司版权所有。
This paper presents a new approach called clustering technique-based least square support vector machine (CT-LS-SVM) for the classification of EEG signals. Decision making is performed in two stages. In the first stage, clustering technique (CT) has been used to extract representative features of EEG data. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted features to classify two-class EEG signals. To demonstrate the effectiveness of the proposed method, several experiments have been conducted on three publicly available benchmark databases, one for epileptic EEG data, one for mental imagery tasks EEG data and another one for motor imagery EEG data. Our proposed approach achieves an average sensitivity, specificity and classification accuracy of 94.92%, 93.44% and 94.18%, respectively, for the epileptic EEG data; 83.98%, 84.37% and 84.17% respectively, for the motor imagery EEG data; and 64.61%, 58.77% and 61.69%, respectively, for the mental imagery tasks EEG data. The performance of the CT-LS-SVM algorithm is compared in terms of classification accuracy and execution (running) time with our previous study where simple random sampling with a least square support vector machine (SRS-LS-SVM) was employed for EEG signal classification. We also compare the proposed method with other existing methods in the literature for the three databases. The experimental results show that the proposed algorithm can produce a better classification rate than the previous reported methods and takes much less execution time compared to the SRS-LS-SVM technique. The research findings in this paper indicate that the proposed approach is very efficient for classification of two-class EEG signals. (C) 2010 Elsevier Ireland Ltd. All rights reserved.