摘要
针对脑电信号(EEG)分类过程中无效数据影响准确度的问题,提出一种数据筛选的方法。基于脑-机接口(BCI)系统,通过视觉诱发刺激产生左向和右向两种运动想象任务对应的脑电信号,提取该信号的统计特征,并利用BP神经网络实现运动想象分类识别。在数据处理过程中,首先利用β节律的能量特征对无效数据进行剔除,再结合μ节律信号的均值、标准差、能量谱、功率谱、自相关函数等多个特征进行分类。对筛选后的数据进行分析,所得特征更具代表性,信号分类的准确率由78.25%提高至84.11%。
In order to solve the problem that invalid data affects the accuracy of EEG classification,a method of data screening is proposed.Based on brain computer interface(BCI)system,this paper presents an approach that using BP neural network to classify the EEG data generated by visual stimulation.The statistical characteristics of EEG signals corresponding to left and right motor imagery tasks are input to the BP neural network.First,the invalid data are eliminated by using the energy characteristics ofβrhythm signal,and then classified by combining the mean value,standard deviation,energy spectrum,power spectrum,autocorrelation function and other features ofμrhythm signal.The using ofβrhythm signal makes the characteristics more accurate and improves the accuracy of signal classification from 78.25%to 84.11%.
作者
蔡靖
李玉涛
宋雪丰
张帆
刘光达
Cai Jing;Li Yutao;Song Xuefeng;Zhang Fan;Liu Guangda(College of Instrumentation&Electrical Engineering,Jilin University,Changchun 130061,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2020年第6期176-182,共7页
Journal of Electronic Measurement and Instrumentation
