摘要
采用基于小波包分析的FastICA方法对单通道脑电信号进行去噪,由于脑电信号并不是独立的,分离信号和源信号对应关系也不确定,直接采用FastICA对多通道脑电信号进行处理,最终的去噪效果并不理想。因此先用小波包对单通道脑电信号进行分解,从而得到频带较窄的子带信号,可保证源信号具有一定的独立性,有利于噪声独立分量的分离和去除,也为单通道脑电信号的去噪提供了思路。从时域分析、傅里叶变换、功率谱密度分析以及信噪比和均方根误差的角度,对比多导联FastICA的去噪效果,可以发现所提出的方法较为有效。
The monopolar channel EEG signal is denoised with FastICA method based on wavelet packet analysis.For multi-channel EEG signals are not independent,and the corresponding relationship between the separated signals and the source signals is uncertain,it is not effective to denoise multi-channel EEG signals by FastICA alone.Therefore,a wavelet packet is used to decompose the monopolar-channel EEG signal first to obtain the sub-band signal with narrower frequency band,which can ensure that the source signals have a certain independence,so as to facilitate the separation and removal of independent components of noise.Therefore,it provides an idea for the denoising of monopolar-channel EEG signals.In the aspects of time domain analysis,Fourier transform,power spectral density analysis,signal-to-noise ratio(SNR)and root-mean-square error(RMSD),the denoising effect of multi-channel FastICA is compared with that of the method proposed in this study,from which it is found that the proposed method is more effective.
作者
姚健康
熊根良
YAO Jiankang;XIONG Genliang(Jiangxi Key Laboratory of Robotic and Welding Automation,School of Mechatronics Engineering,Nanchang University,Nanchang 330031,China)
出处
《现代电子技术》
2021年第7期60-65,共6页
Modern Electronics Technique
基金
国家自然科学基金资助项目(61763030)。
关键词
小波包分解
独立成分分析
脑电信号去噪
信号处理
信号分解
去噪效果对比
wavelet packet decomposition
independent component analysis
EEG signal denoising
signal processing
signal decomposition
denoising effect comparison
作者简介
姚健康(1995—),男,安徽明光人,硕士研究生,主要从事脑电信号研究;熊根良(1978—),男,江西高安人,博士,副教授,主要研究方向为人机交互及信号处理分析。
