| CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK |
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| 引用本文： | 薛建中,郑崇勋,闫相国. CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK[J]. 西安交通大学学报(英文版), 2004, 16(2): 97-100,109 |
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| 作者姓名： | 薛建中 郑崇勋 闫相国 |
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| 作者单位： | [1]KeyLaboratoryofBiomedicalInformationEngineeringofEducationMinistry,InstituteofBiomedicalEngineering,Xi＇anJiaotongUniversity,Xi＇an710049,China [3]KeyLaboratoryofBiomedicalInformationEngineeringofEducationMinistry,InstituteofBiomedicalEngineering,Xi＇anJiaotongUniversity,Xi＇an710049,China |
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| 基金项目： | ThisworkwassupportedbytheNationalNaturalScienceFoundationofChina (No .3 0 3 70 3 95 ) |
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| 摘 要： | Objective This paper presents classifications of mental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCI) schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0%. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.
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| 关 键 词： | 脑电图 脑电信号 自适应径向基函数神经网络 RBF on-line EEG |
CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK |
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| Xue Jianzhong,Zheng Chongxun,Yan Xiangguo Key Laboratory of Biomedical Information Engineering of Education Ministry,Inst itute of Biomedical Engineering,Xi'an Jiaotong University,Xi'an ,China.. CLASSIFICATIONS OF EEG SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK[J]. Academic Journal of Xi’an Jiaotong University, 2004, 16(2): 97-100,109 |
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| Authors: | Xue Jianzhong Zheng Chongxun Yan Xiangguo Key Laboratory of Biomedical Information Engineering of Education Ministry Inst itute of Biomedical Engineering Xi'an Jiaotong University Xi'an China. |
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| Affiliation: | Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, China. |
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| Abstract: | Objective This paper presents classifications of m ental tasks based on EEG signals using an adaptive Radial Basis Function (RBF) n etwork with optimal centers and widths for the Brain-Computer Interface (BCI) s chemes. Methods Initial centers and widths of the network are s elected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during train ing phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three t ask pairs over four subjects achieves 87.0%. Moreover, this network runs fast du e to the fewer hidden layer neurons. Conclusion The adaptive RB F network with optimal centers and widths has high recognition rate and runs fas t. It may be a promising classifier for on-line BCI scheme. |
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| Keywords: | adaptive RBF network EEG mental task |
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