传感技术学报2017，Vol.30Issue(3)：378-384,7.DOI:10.3969/j.issn.1004-1699.2017.03.008
基于人工蜂群优化高斯过程的运动想象脑电信号分类
Classification of Motor Imagery EEG Based on GaussianProcess Optimized with Artificial Bee Colony
摘要
Abstract
The conjugate gradient method is used to determine the parameters in the traditional Gaussian process.However,the conjugate gradient method has a strong dependence on the initial value and is easy to fall into local optimum.In order to solve the problem,a Gaussian process classification(GPC)method is proposed based on artificial bee colony(ABC)optimization and applied for pattern recognition of EEG signals.Firstly,Gaussian process model is constructed,and suitable kernel function is chosen and the parameters to be optimized are specified.Then the reciprocal of the recognition error rate is selected as fitness function,and the parameters which are used to obtain optimal accuracy in a limited range are found out by employing the ABC algorithm.Finally,the Gaussian process classifier with optimized parameters is used to classify the samples.The efficiency of the propose method has been demonstrated by comparison with support vector machine(SVM),support vector machine optimized with Artificial bee colony(ABC-SVM)and GPC algorithms on both BCI Competition Ⅳ Data Set 1 in 2008 and BCI Competition Ⅲ Data Set Ⅳa in 2005.关键词
脑电信号/高斯过程分类/人工蜂群/运动想象Key words
EEG signal/Gaussian process classification/artificial bee colony/motor imagery分类
信息技术与安全科学引用本文复制引用
耿雪青,佘青山,韩笑,孟明..基于人工蜂群优化高斯过程的运动想象脑电信号分类[J].传感技术学报,2017,30(3):378-384,7.基金项目
浙江省自然科学基金资助项目(LY15F010009,LY14F030023) （LY15F010009,LY14F030023）
国家自然科学基金资助项目(61201302) （61201302）
