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Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition
- Chinese Journal of Intelligent Science and Technology Vol. 2, Issue 3, Pages: 240-250(2020)
- 作者机构：
1. 浙江大学智能系统与控制研究所，浙江 杭州 310027
2. 云南大学信息学院电子工程系，云南 昆明 650091
3. 森赫电梯股份有限公司，浙江 湖州 313000
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202026
CLC： TP391-
Published Online：2020-09，
Published：20 September 2020
- 稿件说明：
移动端阅览
Qian ZHENG, Dan QIAO, Xun LANG, et al. Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition[J]. Chinese journal of intelligent science and technology, 2020, 2(3): 240-250.
Qian ZHENG, Dan QIAO, Xun LANG, et al. Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition[J]. Chinese journal of intelligent science and technology, 2020, 2(3): 240-250. DOI： 10.11959/j.issn.2096-6652.202026.
摘要
脑机接口是一项新兴的技术，它可以处理分析采集到的运动想象脑电信号，从而实现对外部辅助设备的控制。针对目前运动想象脑电信号处理方法计算效率低、分类准确率不高等问题，提出了一种新的基于噪声辅助快速多维经验模式分解（NA-FMEMD）的运动想象脑电信号分类方法。该方法首先利用 NA-FMEMD 得到全部的多维本征模式函数和趋势项；接着，根据平均频率选取特定的信号层，构建出新的多维信号；然后，通过共空间模式提取出脑电信号的特征向量；最后，将特征向量输入支持向量机分类器中进行分类。分别采用仿真数据和BCI Competition IV数据进行测试，并与基于噪声辅助多维经验模式分解（NA-MEMD）的方法进行比较，验证了所提方法的有效性和优势。
Abstract
The brain-computer interface is an emerging technology
which can analyze the collected motor imagery signals to control the external auxiliary equipment.A new method based on the noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD) algorithm was proposed for electroencephalogram signal feature extraction and classification.The method outperformed state-of-the-art methods based on noise-assisted multivariate empirical mode decomposition in not only computational efficiency but also classification accuracy.Firstly
all multivariate intrinsic mode functions and trend signals were obtained by the NA-FMEMD.Secondly
the multivariate signals with specific frequency bands were selected by computing their average frequencies.Thirdly
the common spatial pattern was applied to extract features.Finally
the feature vectors were classified using a support vector machine.Simulation data and BCI Competition IV data are used to verify the effectiveness and advantage of the new method
and the method is compared with noise-assisted multivariate empirical mode decomposition.
关键词
Keywords
references
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