| Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines |
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| 作者姓名： | 刘海龙 王珏 郑崇勋 |
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| 作者单位： | Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi＇an Jiaotong University, Xi＇an 710049, China |
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| 摘 要： | Mental tusk classification is one of the most important problems in Brain-computer interface. This paper studies the classification of five-class mental tusks. The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM （support vector machines）. The averaged classification accuracy of 85. 6% over 7 subjects was achieved for 2-second EEG segments. And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett＇s. The results indicate that the parameter of mean period represents mental tusks well for classification, Furthermore, the method of mean period is less computationally demanding, which indicates its potential use for online BCI systems.
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| 关 键 词： | 多意识任务分类 脑电图 支持向量机 脑机接口 非线性参数 |
| 文章编号： | 1671-8267（2007）01-0070-03 |
Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines |
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| Liu Hailong,Wang Jue,Zheng Chongxun.Multiple mental tasks classification based on nonlinear parameter of mean period using support vector machines[J].Academic Journal of Xi’an Jiaotong University,2007,19(1):70-72. |
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| Authors: | Liu Hailong Wang Jue Zheng Chongxun |
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| Abstract: | Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems. |
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| Keywords: | electroencephalography (EEG) brain-computer interface (BCI) mental tasks classification mean period support vector machine (SVM) |
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