摘要
脑电采集后得到的脑电信号(Electroencephalogram,EEG)中含有噪声信号,为了有效去除噪声并保留有用信息,本文在软阈值去噪的基础上,提出一种改进阈值去除EEG噪声的算法。利用小波变换对EEG信号分解,得到多层的高频系数和低频系数;根据分解层次不同,对小波系数进行自适应的阈值处理;将缩放后的小波系数重构,得到去噪后的EEG信号。以信噪比、均方根误差作为去噪效果的定量指标,将改进算法与硬阈值法、软阈值法、Garrote阈值法进行比较,结果表明,改进阈值法优于其他3种阈值法。
In order to eliminate the noise mixed in Electroencephalogram( EEG) and retain useful EEG information,an EEG de￣noising method based on adaptive threshold is proposed,which is improved on the basis of soft threshol￣ding. Firstly,high frequency coefficients and low frequency coefficients of multilayer signals are obtained by wavelet decomposition. Then,detail coefficients is processed by using the adaptive threshold. Finally,the original EEG signal is resumed by reconstructing shrinked detail coefficients. The final results show that the proposed de￣noising algorithm has perspective of higher SNR and lower RMSE compared to soft thresholding,hard thresholding and Gar￣rote thresholding.
出处
《传感技术学报》
CAS
CSCD
北大核心
2014年第10期1368-1372,共5页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(61372023
61201300
61201302
61172134)
浙江省自然科学基金项目(LQ13F010014)
杭州电子科技大学研究生品牌课程建设项目(PPKC2013YB006)
