| 王清杰,全海燕.基于单形进化算法优化支持向量机的
运动想象脑电分类研究[J].电子测量与仪器学报,2021,35(9):157-163 |
| 基于单形进化算法优化支持向量机的
运动想象脑电分类研究 |
| Research on the classification of motor imagery EEG by optimized SVMbased surface-simplex swarm evolution |
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| DOI： |
| 中文关键词: 脑机接口 单形进化算法 脑电信号 支持向量机 |
| 英文关键词:brain-computer interface( BCI) surface-simplex swarm evolution ( SSSE) electroencephalogram ( EEG) support vector
machine(SVM) |
| 基金项目:国家自然科学基金(41364002)项目资助 |
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| 摘要点击次数: 810 |
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| 中文摘要: |
| 由于支持向量机(support vector machine, SVM)优化算法存在易陷入局部最优解、控制参数较多的问题,提出一种基于
单形进化(surface-simplex swarm evolution, SSSE)算法优化的 SVM 并对运动想象(motor imagery, MI)脑电信号的分类进行了研
究。 提取 MI 脑电信号模糊熵和 AR (auto regressive)模型参数作为输入特征,然后将 SSSE 应用在 SVM 的参数寻优中,实现对
MI 脑电信号的分类。 测试实验中,对 2003 国际 BCI 竞赛 Data set Ⅲ和 2008 国际 BCI 竞赛 Data sets 2b 进行左右手分类,结果表
明,所提方法的平均分类正确率和 Kappa 值分别为 82. 47%和 0. 88,单形进化算法减少了控制参数且有效避免粒子陷入局部最
优,验证了该方法在 MI 脑电信号分类的有效性。 |
| 英文摘要: |
| Because the optimization algorithm of support vector machine ( SVM) falls into local optimum easily and has many control
parameters, a SVM optimized by surface-simplex swarm evolution (SSSE) algorithm is proposed and the classification of Motor imagery
EEG signals is studied. The fuzzy entropy and AR model parameters of MI EEG signals were extracted as input features, and then SSSE
is applied to parameters optimization of SVM to classify MI EEG signals. In the test experiment, which classified the 2003 international
brain-computer interface (BCI) competition Data sets Ⅲ and the 2008 BCI competition Data sets 2b by left-hand and right-hand. The
results showed that the average classification accuracy and Kappa value of the proposed method were 82. 47% and 0. 88 respectively.
SSSE reduced the control parameters and effectively avoided the particles falling into the local optimum. The validity of this method in
the classification of MI EEG signals was verified. |
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