摘要
利用单通道脑电传感器mindset采集脑电波原始数据,并利用小波变换对采集的脑电信号进行分解,以小波变换系数为特征提取方法,首次从脑波节律能量的角度判断大脑注意力状态;另外,通过感知机准则算法,提出以系数加子带能量为特征的新方法,实现以脑波数据检测眼睛眨动情况。通过将大脑注意力状态以及眼睛眨动强度发送给智能车,实现对其速度和方向的精确控制。此算法为智能车控制提供了一种真正的意念控制方法。
With the development of Brain-Computer Interface （BCI）,mind control it will become an inevitable development tendency in area of control.In this paper,we acquire raw EEG data by one-channel EEG sensor and decompose them into different frequency ranges with wavelet transform.Then we extract features of each frequency through wavelet transform coefficients and distinguish the state of brain attention in term of brainwave rhythm energy.Besides,by perceptron approach,we propose a new method of extracting feature with fusion of coefficient and subband energy,in order to detect eye blink from EEG data.By sending the attention and strength of eye blink to intelligent vehicle,we can accurately control the speed and direction.The algorithm we propose provides a true way of mind control for intelligent vehicle control.
出处
《太原理工大学学报》
CAS
北大核心
2013年第6期739-743,共5页
Journal of Taiyuan University of Technology
基金
国家自然科学基金资助项目(61170136
61070077)
关键词
脑-机接口
小波变换系数
感知机准则
子带能量
脑电图
意念控制
brain-computer interface
wavelet transform
coefficients perceptron approach
sub-band energy
electroencephalogram
mind control
作者简介
陈东伟（1982-）,男,山西太原人,讲师,博士,主要从事智能信息处理,脑信息学,嵌入式系统,（Tel）15989763155
