摘要
研究了一种基于运动想象识别的脑-机接口(BCI)系统,通过提取想象过程中的脑电信号(EEG)中Alpha波特征,采用多特征分类的方法,以提高脑-机接口系统运动想象识别的正确率。针对脑电信号单特征分类精确度低、耗时长等缺点,采用自回归模型法、统计特征提取和频域分析的方法对Alpha波提取多个特征值,利用BP神经网络进行分类,对运动想象进行识别。通过实验验证了其识别率较高,取得了预期的效果,证明了多特征融合结合BP神经网络运用于脑机接口系统的可行性。
This research is carried out re-designing a brain-computer interface( BCI) system based on motor imagery recognition through extracting features of Alpha wave in electroencephalography( EEG) signal during motor imagery process, using multi-feature classification method in order to increase the accuracy of classification. Aiming at the shortcomings such as low accuracy and time-consuming when one feature is adopted in the classification process, methods including AR model, statistical characteristics extrac-tion and frequency domain analysis, etc. are taken to extract various features of Alpha wave. BP neural network is used to classify features. The system is designed to identify motor imagery and through experimental verification, it has achieved expected effect with high classification accuracy. The research proves the feasibility of brain-computer interface system combining multi-feature integra-tion with BP neural network.
出处
《电子技术应用》
北大核心
2017年第9期72-75,共4页
Application of Electronic Technique
