脑波信号控制轨道小车系统的研制

发布时间：2018-04-27 19:37

本文选题：脑机接口(BCI) + 脑波信号　；参考：《计算机工程与应用》2017年11期

【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。
[Abstract]:Brain wave signal recognition is one of the key problems to be solved in the brain machine interface (BCI) technology. In view of the data processing and feature extraction of brain wave signals, the wavelet transform coefficients are used to calculate the "concentration" characteristics to describe the intensity of the concentration of mental forces and the state of the EEG jump, and the different mental states are obtained in combination with the actual test. A brain wave control track car system based on ARM processor is designed, which sends the "concentration" value to the car system and realizes the control of the speed of the car in different mental states. The experimental results show that the characteristic of the concentration is better than the single frequency characteristic of FFT in the recognition effect. And the system can be used. It is of practical value to extract the characteristics of brain waves quickly and control the track car in real time.

【作者单位】：西安科技大学电气与控制工程学院;
【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)
【分类号】：TN911.7;TP23

【相似文献】