| 基于小波包最优基的运动想象EEG自适应特征提取方法 |
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| 引用本文： | 李明爱,林琳,杨金福. 基于小波包最优基的运动想象EEG自适应特征提取方法[J]. 计算机测量与控制, 2011, 19(11) |
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| 作者姓名： | 李明爱 林琳 杨金福 |
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| 作者单位： | 北京工业大学电子信息与控制工程学院,北京,100124 |
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| 基金项目： | 北京市自然科学基金项目(4082004),北京市自然科学基金项目(4112011) |
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| 摘 要： | 针对运动想象脑机接口系统存在分类正确率低、自适应能力差等不足,提出一种基于小波包最优基的自适应特征提取方法;该方法首先对运动想象EEG进行小波包分解;其次,对传统的距离准则进行改进,通过引入权重因子表征对类内距离和类间距离的关注程度,获得一种既可满足小波包最优基评价准则的可加性条件,又有效地增强了频带特征信息的可分离性的评价准则;进而,采用自底向顶、自左至右的快速搜索策略获取小波包最优基,并选取最优基对应的分类性能评价值较高的部分频带小波包系数构成分类特征;仿真结果表明本方法最高分类正确率可达93.4%,与常用的时频分析方法对比,验证了本算法具有较高的分类正确率和较小的时间花费。
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| 关 键 词： | 运动想象脑电 特征提取 个体自适应 小波包最优基 评价准则 |
Adaptive Feature Extraction Based on Best Basis of Wavelet Packet for Motor Imagery EEG |
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| Li Mingai,Lin Lin,Yang Jinfu. Adaptive Feature Extraction Based on Best Basis of Wavelet Packet for Motor Imagery EEG[J]. Computer Measurement & Control, 2011, 19(11) |
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| Authors: | Li Mingai Lin Lin Yang Jinfu |
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| Affiliation: | Li Mingai,Lin Lin,Yang Jinfu(College of Electronic Information & Control Engineering,Beijing University of Technology,Beijing 100124,China) |
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| Abstract: | In brain-computer interfaces of imagery movement,a new method which can adaptively extract features on the basis of the best wavelet package basis is proposed to solve the problems such as the low classification accuracy and weak self-adaptation,etc.First,wavelet packet is exploited to decompose the motor imagery EEG signal;second,the traditional distance criterion is optimized by introducing a weight parameter which reflects the importance of including both the interclass and the intraclass inertias,and th... |
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| Keywords: | imagery movement EEG feature extraction self-adaptation best wavelet package basis evaluation criterion |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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