The research of brain-computer interface based on AAR parameters and neural networks classifier

摘要：

The brain-computer interface(BCI) based on motor imagery was investigated in this paper. A neural networks classifier was adopted to solve the problem of lower classification accuracy in BCI. Firstly, mu rhythm EEG was obtained with a bandpass filter from the subjects scalp electroencephalography (EEG). Secondly, the Kalman Filter algorithm was used to build the adaptive autoregressive model from EEG. The model parameters were used as features of EEG. Lastly, the AAR feature parameters were classified by the neural networks classifier. A compare on the performance between the neural networks and linear discriminant analysis(LDA) was conduct in the simulation. The results show the performance of neural networks is higher than linear discriminant analysis.

关键词： brain-computer interface motor imagery adaptive autoregressive model neural networks

作者: Xin Ma

作者单位: School of Electrical Engineering and Automation Tianjin Polytechic University Tianjin, China 300387

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 2561-2564

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）