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Title:: EEG Signal Recognition and Its Application in the ?Control of Mechanical Arm

Author(s):: LIU Gang; LI Xiaoou; 1.School of Medical Instrument and Food Engineering, University of Shanghai For Science and Technology, Shanghai 200093, China;2.College of Medical Instruments, Shanghai University of Medicine & Health Science, Shanghai 201318

Keywords:: Common spatial pattern; Support vector machine; Serial port; Motor imagery; Mechanical arm

摘要:: 传统的时频分析方法利用的空间信息少，分类准确率低, 针对此问题，本研究采用共空间模式算法对脑电信号进行特征提取，并利用支持向量机对特征进行分类，最后通过串口将分类识别的结果用于控制机械手臂的运动。实验结果表明，共空间模式算法适用于基于运动想象的脑电信号特征提取，能有效克服传统时频特征提取方法空间信息利用不足的缺点，本次实验中的平均分类精度为86.1%。

Abstract:: The traditional time-frequency analysis method uses little spatial information and the obtained classification accuracy is very low. To solve this problem, firstly，the common spatial patterns algorithm was applied to extract the feature of EEG,and then the feature was classified by the support vector machine, finally, the classification results was transmitted to the control system of mechanical arm by the serial port. The experimental results show that the CSP algorithm can be applied effectively to extract the feature of collected EEG signal based on motor imagery, and this algorithm also can effectively overcome the disadvantage of the traditional time-frequency analysis method based on few spatial information. The average classification accuracy is 86.1%.