EMG assisted EEG intelligent wheelchair control system based on multi-class SVM

被引：0

作者：

Zhang, Yi ^{[1
]}

Zhu, Xiang ^{[1
]}

Luo, Yuan ^{[1
]}

机构：

[1] Key Laboratory of Optical Fiber Communication Technology, Information Accessibility Engineering R and D Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

来源：

Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2013年 / 44卷 / SUPPL.2期

关键词：

Motion estimation - Wavelet transforms - Wheelchairs;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The recognition rate and the stability of the EEG control system for intelligent wheelchair can not satisfy the demand of the application. While the Emotiv sensor can collect EEG and EMG signal at the same time, a novel hybrid EEG and EMG control system is designed. The feature vectors of EEG and EMG are extracted by the threshold method and wavelet transform separately and the feature vectors are integrated together. Then, the features are classified by multi-class SVM. The recognition results are regarded as control instructions of the system to control an intelligent wheelchair. Experimental results prove that the system has higher motion recognition rate and better stability compared with the single EEG control system.

引用

页码：73 / 76

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