脑波信号控制轨道小车系统的研制  

Controlling rail car by EEG signal

作　　者：秦学斌[1] 张一哲[1] 汪梅[1] QIN Xuebin;ZHANG Yizhe;WANG Mei(College of Electrical and Control Engineering, Xi’an University of Science & Technology, Xi’an 710054, China)

机构地区：[1]西安科技大学电气与控制工程学院,西安710054

出　　处：《计算机工程与应用》2017年第11期260-264,270,共6页Computer Engineering and Applications

基　　金：陕西省教育厅基金(No.15JK1472);陕西省工业科技攻关基金项目(No.2015GY020)

摘　　要：脑波信号识别是脑机接口(BCI)技术需要解决的关键问题之一。针对脑波信号的数据处理以及特征提取,提出了采用小波变换系数计算"专心度"的特征来描述精神力集中强烈程度以及脑电波活跃状况,并结合实际测试得出不同精神状态下的检测算法。设计了一款基于ARM处理器的脑波控制轨道小车系统,将"专心度"值发送给小车系统,实现了不同精神状态下对小车车速的控制。实验结果表明,采用专心度的特征在识别效果方面要优于FFT的单一频率特征。并且该系统能够快速提取出脑波特征并实时地控制轨道小车行驶,系统性能稳定具有一定的实用价值。EEG recognition is among the key issues in the Brain Computer Interface(BCI)technology.For the data processing and feature extraction of EEG signal,the concept of attention calculated by wavelet transform coefficient is presented to describe the concentrate intensity and the activity of brain waves,and the detection algorithms are obtained under different concentration states.An EEG signal control rail car system is designed based on ARM processor.The value of attention is sent to the system,which realizes the control of the rail car speed under different concentration states.Experimental results show that the feature of attention is better than the single frequency of FFT in terms of recognition results.This system can quickly extract the feature of EEG and real-time control rail car driving,and the performance is stable and it has a certain practical value.

关 键 词：脑机接口(BCI) 脑波信号 小波变换 ARM处理器 轨道小车 

分 类 号：TN911.7[电子电信—通信与信息系统]