低密度脑电自适应去噪方法  

An Adaptive Denoising Algorithm for Few-Channel EEG

作　　者：陈贺 张昊 柴一帆 李小俚[1] CHEN He;ZHANG Hao;CHAI Yifan;LI Xiaoli(State Key Laboratory of Cognitive Neuroscience and Learning,Beijing Normal University,Beijing 100875,China;School of Systems Science,Beijing Normal University,Beijing 100875,China)

机构地区：[1]北京师范大学认知神经科学与学习国家重点实验室,北京100875 [2]北京师范大学系统科学学院,北京100875

出　　处：《数据采集与处理》2023年第4期824-836,共13页Journal of Data Acquisition and Processing

基　　金：国防基础科研计划(JCKY2021208B019)。

摘　　要：便携式和可穿戴设备的低密度脑电图更便于实际使用,但会受到多种不可预知的噪声影响,给去噪带来极大的困难。脑活动成分较为相似,在特征空间分布较为紧密,而噪声成分与脑电成分不同,差异性大,在特征空间分布较为分散。本文提出了一种低密度脑电自适应去噪方法,采用小波分解和盲源分离方法提取潜在成分,并基于脑电和噪声成分在特征空间的分布特性,采用单类支持向量机识别并去除远离成分分布中心的异常成分。仿真数据的定量分析结果表明,提出的方法在肌电、眼电和工频等噪声抑制方面均优于现有方法;通过对真实脑电数据的成分簇可视化分析,直观展示了低密度脑电噪声有效去除的原因。结合盲源分离和异常检测的思路进行低密度脑电去噪,不需要设定特定噪声相关的特征参数,能够自适应地去除多种类型噪声同时有效保留脑活动成分,具有优良的性能和实用性。Few-channel electroencephalogram(EEG)is more suitable and affordable for practical use as a portable or wearable device,but it is subject to a variety of unpredictable artifacts,making removal of artifacts extremely difficult.In the feature space,the artifact-related components are dispersed while the components related to brain activities are closely distributed.We propose an outlier detection-based method for artifact removal under the few-channel condition.The underlying components(sources)are extracted using wavelet decomposition and blind source separation methods,and the artifact-related components far from the center of distribution of all components are considered as outliers and are identified using one-class support vector machine.In the quantitative analyses with semi-simulated data,the proposed method outperforms the threshold-based methods for various artifacts,including EMG,electro-oculogram(EOG)and power line noise.The visualization of the clusters of components demonstrates the effectiveness of the hypothesis.This study innovatively combines the ideas of blind source separation and outlier detection,without setting artifact-specific parameters,and is capable of adaptively removing various artifacts while effectively retaining brain activities,showing excellent performance and usability.

关 键 词：低密度脑电 异常检测 去噪 盲源分离 聚类 

分 类 号：R318[医药卫生—生物医学工程]