去除脑电信号眨眼伪差的独立成分时域相关算法

盛恒松 1; 田洪君 2; 郑崇勋 1; 徐进 1

doi:10.7652/xjtuxb201310022

您当前的位置：

首页 >

文章列表页 >

去除脑电信号眨眼伪差的独立成分时域相关算法

更新时间：2025-07-09

- 去除脑电信号眨眼伪差的独立成分时域相关算法
- A Method for Removing Eye-Blink Artifacts from EEG Signals by Temporal Correlation of Independent Component Analysis
- 西安交通大学学报 2013年47卷第10期页码：127-131
- 作者机构：
  
  西安交通大学生物医学信息工程教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2013-04-23。作者简介: 盛恒松(1978—),男,博士生;徐进(通信作者),女,教授。基金项目: 国家自然科学基金资助项目(31271061,81271659)
- DOI：10.7652/xjtuxb201310022
  中图分类号： R318.4
- 网络首发：2013-10-10，
  
  纸质出版：2013
- 稿件说明：
移动端阅览
盛恒松 1, 田洪君 2, 郑崇勋 1, 等. 去除脑电信号眨眼伪差的独立成分时域相关算法[J]. 西安交通大学学报, 2013,47(10):127-131.

A Method for Removing Eye-Blink Artifacts from EEG Signals by Temporal Correlation of Independent Component Analysis[J]. 2013, 47(10): 127-131.
盛恒松 1, 田洪君 2, 郑崇勋 1, 等. 去除脑电信号眨眼伪差的独立成分时域相关算法[J]. 西安交通大学学报, 2013,47(10):127-131. DOI： 10.7652/xjtuxb201310022.

A Method for Removing Eye-Blink Artifacts from EEG Signals by Temporal Correlation of Independent Component Analysis[J]. 2013, 47(10): 127-131. DOI： 10.7652/xjtuxb201310022.

摘要

针对脑电信号易受眨眼动作干扰的问题

提出了一个自动地去除眨眼伪差的独立成分时域相关算法。该算法使用独立成分进行分析

并分解脑电信号

然后利用眨眼伪差独立成分与某些导联的脑电信号之间在时域存在较大相关性的特点

计算每个独立成分与前额附近的5个导联(Fp1

Fp2

Fz)信号的相关值的累加值

并对该值进行排序

将具有最大值的独立成分识别为眨眼伪差独立成分

将其设置为0

最后重建干净的脑电信号。通过对脑电信号的去除伪差实验表明:眨眼伪差引起的干扰基本被消除

伪差检测算法的敏感度和特异度分别是97.7% 和98.3%

同时该算法能有效保持脑电信号基本不变。

Abstract

A novel method is proposed to remove eye-blink artifacts from EEG signals automatically. The EEG signals are decomposed by independent component analysis(ICA)

and the features of temporal correlation between ICs and observed EEG signals from some electrodes are extracted. The sum of the correlation between each IC and EEG signal from respective electrodes Fp1

Fp2

F4 and Fz is then calculated. These correlation values are sorted in descending order. The IC with the biggest correlation value in all the ICs is picked out for the eye-blink artifact component

and is finally counted as zero to reconstruct clean EEG signals. Experiments for purifying contaminated EEG signals show that eye-blink artifacts are successfully removed from EEG signals

the sensitivity and specificity of the artifacts detection algorithm get to 97.7% and 98.3%

respectively

remaining the original EEG signal feature.

关键词

Keywords

references

KLADOS M A, PAPADELIS C, BRAUN C, et al. REG-ICA: a hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts [J]. Biomedical Signal Processing and Control, 2011, 6(3): 291-300.

洪波, 唐庆玉, 杨福生, 等. ICA在视觉诱发电位的少次提取与波形分析中的应用 [J]. 中国生物医学工程学报, 2000, 19(3): 334-341.

HONG Bo, TANG Qingyu, YANG Fusheng, et al. ICA in the single-trial estimation and analysis of VEP [J]. Chinese Journal of Biomedical Engineering, 2000, 19(3): 334-341.

HSU W Y, LIN C H, HSU H J, et al. Wavelet-based envelope features with automatic EOG artifact removal: application to single-trial EEG data [J]. Expert Systems with Applications, 2012, 39(3): 2743-2749.

LIU Z, DE ZWART J A, VAN GELDEREN P, et al. Statistical feature extraction for artifact removal from concurrent fMRI-EEG recordings [J]. Neuroimage, 2012, 59(3): 2073-2087.

CHEN C K, CHUA E, HSIEH Z H, et al. An EEG-based brain-computer interface with real-time artifact removal using independent component analysis [C]∥2012 IEEE International Conference on Consumer Electronics - Berlin. Piscataway, NJ, USA: IEEE, 2012: 13-14.

GAO J F, YANG Y, LIN P, et al. Automatic removal of eye-movement and blink artifacts from EEG signals [J]. Brain Topography, 2010, 23(1): 105-114.

DAMMERS J, SCHIEK M, BOERS F, et al. Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings [J]. IEEE Transactions on Biomedical Engineering, 2008, 55(10): 2353-2362.

CHEN J L, ROS T, GRUZELIER J H. Dynamic changes of ICA-derived EEG functional connectivity in the resting state [J]. Human Brain Mapping, 2013, 34(4): 852-868.

SAFIEDDINE D, KACHENOURA A, ALBERA L, et al. Removal of muscle artifact from EEG data: comparison between stochastic(ICA and CCA)and deterministic(EMD and wavelet-based)approaches [J]. EURASIP Journal on Advances in Signal Processing, 2012, 2012(1): 127-141.

高军峰, 郑崇勋, 王沛. 基于独立成分分析和流形学习的眼电伪差去除 [J]. 西安交通大学学报, 2010, 44(2): 113-118.

GAO Junfeng, ZHENG Chongxun, WANG Pei. Real-time removal of ocular artifacts from EEG signals using ICA and manifold algorithm [J]. Journal of Xi'an Jiaotong University, 2010, 44(2): 113-118.

CHOI J H, KIM M H, FENG L, et al. A new weighted correlation coefficient method to evaluate reconstructed brain electrical sources [J/OL]. Journal of Applied Mathematics, 2012, 2012: Article ID 251295. [2013-04-02]. http:∥downloads.hindawi.com/journals/jam/2012/251295.pdf.

IWASAKI M, KELLINGHAUS C, ALEXOPOULOS A V, et al. Effects of eyelid closure, blinks, and eye movements on the electroencephalogram [J]. Clinical Neurophysiology, 2005, 116(4): 878-885.

CAFFIER P P, ERDMANN U, ULLSPERGER P. Experimental evaluation of eye-blink parameters as a drowsiness measure [J]. European Journal of Applied Physiology, 2003, 89(3/4): 319-325.

DELORME A, MAKEIG S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis [J]. Journal of Neuroscience Methods, 2004, 134: 9-21.

LEE T W, GIROLAMI M, SEJNOWSKI T J. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources [J]. Neural Computation, 1999, 11(2): 417-441.

HALDER S, BENSCH M, MELLINGER J, et al. Online artifact removal for brain-computer interfaces using support vector machines and blind source separation [J/OL]. Computational Intelligence and Neuroscience, 2007, 2007: Article ID 82069. [2013-04-02]. http:∥www.hindawi.com/journals/cin/2007/082069/abs/.

JUNG T P, MAKEIG S, HUMPHRIES C, et al. Removing electroencephalographic artifacts by blind source separation [J]. Psychophysiology, 2000, 37(2): 163-178.

TARASSENKO L, KHAN Y U, HOLT M R G. Identification of inter-ictal spikes in the EEG using neural network analysis [J]. IEE Proceedings: Science, Measurement and Technology, 1998, 145(6): 270-278.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

脑电信号中肌电伪差的实时去除方法研究

采用独立成分分析的局部放电脉冲双通道提取技术研究

基于P300和机器学习的测谎方法研究

电子级超纯氮深冷空分流程节能优化研究

语义区分和子图整合驱动的知识图谱表示学习方法