摘要
以OpenBCI为脑电信号采集平台,OpenViBE为脑电信号分析平台,并以源自大脑感觉运动皮层的μ节律和β节律为基础,采用共空间模式算法作为特征提取方法,结合高斯核支持向量机,研制用于机械臂控制的运动想象脑机接口,并通过实验对信号特征提取方法以及分类算法的效果进行评估。初步实验结果表明,采用共空间模式算法处理后的分类准确率高于表面拉普拉斯空间滤波器,且支持向量机的分类性能优于线性判别分析。本系统的控制准确率达95%以上,可实现机械臂的有效控制。未来的研究将探索如何通过自定义插件来提高OpenViBE的硬件控制功能。
A motor imagery brain-computer interface for the control of mechanical arms is designed based on theμrhythm andβrhythm from the ensorimotor cortex of brain,with OpenBCI as electroencephalogram(EEG)signal acquisition platform,OpenViBE as EEG analysis platform,common spatial pattern algorithm for feature extraction,and Gaussian kernel support vector machine for feature classification.Moreover,the performances of signal feature extraction method and classification algorithm were evaluated by experiments.The preliminary experimental results reveal that the classification accuracy after the signal extraction with common spatial pattern algorithm is higher than that after the signal extraction with surface Laplacian spatial filter,and that the classification performance of support vector machine is superior to linear discriminant analysis.The designed system can realize the effective control of mechanical arms,with a control accuracy higher than 95%.Future studies will focus on improving the hardware control functions of OpenViBE by custom plug-in.
作者
冯琴昌
FENG Qinchang(Guangdong Institude of Medical Instruments,Guangzhou 510500,China)
出处
《中国医学物理学杂志》
CSCD
2020年第2期210-219,共10页
Chinese Journal of Medical Physics
基金
广东省科技计划项目（2011B060500058）
广州市科技计划（产学研协同创新重大专项）（201604020144）
