基于ICA的脑电信号P300少次自动提取

doi:10.3969/j.issn.0372-2112.2012.06.032

电子学报

基于ICA的脑电信号P300少次自动提取

陈洪波¹, 李蓓蕾^1,2, 陈真诚¹

1. 桂林电子科技大学生命与环境科学学院,广西桂林 541004;
2. 桂林电子科技大学电子工程与自动化学院,广西桂林 541004

收稿日期:2011-06-28 修回日期:2011-12-21 出版日期:2012-06-25
- 作者简介:
- 陈洪波 男,博士,副教授,1972年出生于湖南株洲.桂林电子科技大学生命与环境科学学院副院长,主要从事生物医学信息处理方面研究. E-mail:hongbochen@163.com
  李蓓蕾 女,硕士研究生,1986年出生于河北邢台.研究方向为生物医学信号检测与信息处理. E-mail:beilei040810@163.com
  陈真诚 男,博士,教授,博士生导师,1965年出生于湖南永州.桂林电子科技大学生命与环境科学学院院长,主要从事生物传感器与智能仪器、生物医学信息处理方面的研究. E-mail:chenzhcheng@163.com
- 基金资助:
- 国家863高技术研究发展计划 (No.2006AA06Z105); 广西教育厅科研项目 (No.201012MA084)

Automatically Extract P300 Within Several Trials from EEG Based on ICA

CHEN Hong-bo¹, LI Bei-lei^1,2, CHEN Zhen-cheng¹

1. School of Life and Environmental Science,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China;
2. School of Electronic Engineering and Automatic,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China

Received:2011-06-28 Revised:2011-12-21 Online:2012-06-25 Published:2012-06-25
- Supported by:
- National 863 High-tech R&D Program of China (No.2006AA06Z105); Research Program of Education Department of Guangxi Zhuang Autonomous Region (No.201012MA084)

摘要/Abstract

摘要： 提出一种基于Infomax ICA少次自动提取脑电信号P300成分的方法.为了提高ICA分解的有效性,对原始数据中的自发脑电信号和P300成分进行了均衡.混合信号经过ICA分解后,根据IC的固定时间模式的标准差来自动选择P300成分IC,最后重构得到P300成分.实验结果是:利用6试次实验数据经过本文方法处理后能自动得到P300成分,与29试次平均结果(标准信号)相比,它们之间的Pearson相关系数达0.9035,而6试次实验数据平均的结果与标准信号之间的Pearson相关系数为0.5105.结果表明,该方法能有效的获取P300成分,同时增强了P300成分少次提取的客观性.

关键词: 独立分量分析, P300, 脑电, 固定时间模式

Abstract: This paper puts forward a method for automatically extracting the P300 from electroencephalography (EEG) signals within several trials based on Infomax independent component analysis (ICA).An algorithm for signaling equilibrium is proposed to enhance the effectiveness of ICA decomposition.After the mixed signal is decomposed by Infomax ICA,the independent component (IC) of P300 is automatically selected according to the standard deviation of the fixed-temporal-pattern of the IC,and applied in P300 reconstruction.Experimental results show that the P300 can be obtained automatically after six trials on the experimental data,and the result of its Pearson correlation coefficient (PCC) within the average of 29 trials (standard signal) is 0.9035.However,the PCC of the average result of six trials and standard signal is only 0.5105,demonstrating the practical applicability of Infomax ICA.This algorithm enhances the objectivity of P300 extraction within several trials.

Key words: independent component analysis(ICA), P300, electroencephalography(EEG), fixed-temporal-pattern

中图分类号:

陈洪波, 李蓓蕾, 陈真诚. 基于ICA的脑电信号P300少次自动提取[J]. 电子学报, DOI: 10.3969/j.issn.0372-2112.2012.06.032.

CHEN Hong-bo, LI Bei-lei, CHEN Zhen-cheng . Automatically Extract P300 Within Several Trials from EEG Based on ICA[J]. Acta Electronica Sinica, DOI: 10.3969/j.issn.0372-2112.2012.06.032.

参考文献

[1] 陶彩林,邹凌,马正华,周仁来.诱发电位单次提取技术的研究进展[J].生物医学工程学杂志,2009,26(5):1158-1161. TAO Cai-lin,LING Zou,MA Zheng-hua,ZHOU Ren-lai.Progress in single extraction of evoked potential[J].Journal of Biomedical Engineering,2009,26(5):1158-1161.(in Chinese)

[2] 张和发,李立萍,杨小牛,李万春.一种适用于微弱信号盲提取的白化方法[J].电子学报,2011,39(6):1297-1301. ZHANG He-fa,LI Li-ping,YANG Xiao-niu,LI Wan-chun.An efficient whitening method for weak signal extraction[J].Acta Electronica Sinica,2011,39(6):1297-1301.(in Chinese)

[3] Rahman MZU,Shaik RA,Reddy DVRK.Cancellation of artifacts in ECG signals using block adaptive filtering techniques[J].Software Tools and Algorithms for Biological Systems,2011,696:505-513.

[4] Feng Zhen,Zhang Suo-di.Intelligent diagnosis on ERP performance by using classified wavelet neural network[J].Computer Engineering and Applications,2010,46(4):243-244,248.

[5] 罗志增,李文国.基于小波变换和盲信号分离的多通道肌电信号处理方法[J].电子学报,2009,37(4):823-827. LUO Zhi-zeng,LI Wen-guo.A method of multi-channel EMG disposal based on wavelet transform and blind signal separation[J].Acta Electronica Sinica,2009,37(4):823-827.(in Chinese)

[6] S Aviyente,E M Bernat,S M Malone,et al.Time-frequency data reduction for event related potentials:Combining principal component analysis and matching pursuit [J].EURASIP Journal on Advances in Signal Processing,2010,11(55):289-571.

[7] 万柏坤,朱欣,杨春梅,高扬.ICA去除EEG中眼动伪差和工频干扰方法研究[J].电子学报,2003,31(10):1571-1574. WAN Bai-kun,ZHU Xin,YANG Chun-mei,GAO Yang.Study on applying independent component analysis to remove blink artifacts and power noise in EEG[J].Acta Electronica Sinica,2003,31(10):1571-1574.(in Chinese)

[8] 张宇,张建华,王行愚,等.基于Fast ICA 的P300 电位快速提取方法[J].华东理工大学学报(自然科学版),2009,35(5):750-755. ZHANG Yu,ZHANG Jian-hua,WANG Xing-yu,et al.A fast ICA-based approach to extracting P300 potential[J].Journal of East China University of Science and Technology(Natural Science Edition),2009,35(5):750-755.(in Chinese)

[9] Jan R Wessel,Markus Ullsperger.Selection of independent components representing event-related brain potentials:A data-driven approach for greater objectivity[J].NeuroImage,2011,54:2105-2115.

[10] Ling Zou,Suolin Duan,Zhenghua ma,Changchun Yang.Single-trial event related potentials extraction by using independent component analysis .2nd International Conference on Biomedical Engineering and Informatics (BMEI’09) .Tianjin:IEEE Press,2009.1-5.

[11] Darshan Iyer,George Zouridakis.Single-trial evoked potential estimation:Comparison between independent component analysis and wavelet denoising[J].Clinical Neurophysiology,2007,118:495-504.

[12] Hung Chih-I,Yang Yea-Ru,Wang Ray-Yau,et al.Enhancement of signal-to-noise ratio of peroneal somatosensory evoked potential using independent component analysis and time-frequency template[J].Journal of Medical and Biological Engineering,2008,28(3):161-166.

[13] 陈洪波,陈真诚,汤井田.基于ICA的同场景多幅图像中的强噪声抑制[J].电子科技大学学报(自然科学版),2010,39(2):209-213. CHEN Hong-bo,CHEN Zhen-cheng,TANG Jing-tian.Strong noise pollution control in multiple copy images[J].Journal of the University of Electronic Science and Technology of China,2010,39(2):209-213.(in Chinese)

[14] Lee TW,et al.Independent component analysis using an extened infomax algorithm for mixed Subgaussian and Supergaussian sources[J].Neural Computation,1999,11(2):409-33.

[1]	李明爱, 张圆圆. 基于连续小波变换和符号传递熵的脑功能网络构建方法[J]. 电子学报, 2022, 50(7): 1600-1608.
[2]	罗映雪, 贾博, 裘旭益, 邓平煜, 任和, 吴奇. 基于Gamma深度信念网络的飞行员脑疲劳状态识别[J]. 电子学报, 2020, 48(6): 1062-1070.
[3]	张学军, 景鹏, 何涛, 孙知信. 基于变分模态分解的癫痫脑电信号分类方法[J]. 电子学报, 2020, 48(12): 2469-2475.
[4]	彭丝雨, 周到, 张家琦, 王宇, 高军峰. 基于互信息的脑网络及测谎研究[J]. 电子学报, 2019, 47(7): 1551-1556.
[5]	司慧芳, 谢天, 高军峰, 官金安, 向州州, 兰长有, 卿训华. 基于相位延迟指数的脑功能网络及测谎研究[J]. 电子学报, 2018, 46(7): 1742-1747.
[6]	陈冉, 王鹏, 高军峰, 顾凌云, 黄文涛, 晏丹丹, 余雨蝶, 丁志雄. 利用相锁值算法的脑电相同步测谎研究[J]. 电子学报, 2018, 46(6): 1289-1293.
[7]	孙曜, 文成林, 韦巍. 基于脑电和眼电的运动想象多尺度识别方法研究[J]. 电子学报, 2018, 46(3): 714-720.
[8]	高军峰, 司慧芳, 余彬, 顾凌云, 梁莹, 杨勇. 基于脑电样本熵的测谎分析[J]. 电子学报, 2017, 45(8): 1836-1841.
[9]	谢松云, 张娟丽, 段绪, 刘畅, 李亚兵. 基于矩阵灰建模的单次P300检测新方法[J]. 电子学报, 2017, 45(7): 1660-1667.
[10]	刘志勇, 孙金玮. 基于序列连通度的睡眠分期算法研究[J]. 电子学报, 2017, 45(1): 225-231.
[11]	顾凌云, 吕文志, 杨勇, 高军峰, 官金安, 周到. 基于PCANet和SVM的谎言测试研究[J]. 电子学报, 2016, 44(8): 1969-1973.
[12]	常文文, 王宏, 化成诚. 基于听觉ERP功能脑网络特征和SVM的测谎方法研究[J]. 电子学报, 2016, 44(7): 1757-1762.
[13]	张少白, 王勇, 刘友谊. 基于DIVA模型的脑电信号去噪方法研究[J]. 电子学报, 2015, 43(4): 700-707.
[14]	余南南, 刘海宽, 王晓燕. 联合稀疏表示的双次诱发电位提取算法[J]. 电子学报, 2014, 42(5): 852-857.
[15]	李明爱, 崔燕, 杨金福. 脑电信号中眼电伪迹自动去除方法的研究[J]. 电子学报, 2013, 41(6): 1207-1213.

基于ICA的脑电信号P300少次自动提取

Automatically Extract P300 Within Several Trials from EEG Based on ICA

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics