Research on the correlation of brain function based on improved phase locking value_Journal of Biomedical Engineering

Authors：

 LI Xin ^1,2 , FAN Mengdi ^1,2 , SUN Xiaoqi ^1,2 , LI Quan ^1,2 , ZHANG Jie ^1,2

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China;

Corresponding author：

LI Xin, Email: yddylixin@ysu.edu.cn

Keywords：

electroencephalogram signal; brain function correlation; phase locking value; amplitude locking value; empirical mode decomposition

DOI：

10.7507/1001-5515.201706069

Video：

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Abstract Full text Figures/Tables Video References Cited by

The phase lock value(PLV) is an effective method to analyze the phase synchronization of the brain, which can effectively separate the phase components of the electroencephalogram (EEG) signal and reflect the influence of the signal intensity on the functional connectivity. However, the traditional locking algorithm only analyzes the phase component of the signal, and can’t effectively analyze characteristics of EEG signal. In order to solve this problem, a new algorithm named amplitude locking value (ALV) is proposed. Firstly, the improved algorithm obtained intrinsic mode function using the empirical mode decomposition, which was used as input for Hilbert transformation (HT). Then the instantaneous amplitude was calculated and finally the ALV was calculated. On the basis of ALV, the instantaneous amplitude of EEG signal can be measured between electrodes. The data of 14 subjects under different cognitive tasks were collected and analyzed for the coherence of the brain regions during the arithmetic by the improved method. The results showed that there was a negative correlation between the coherence and cognitive activity, and the central and parietal areas were most sensitive. The quantitative analysis by the ALV method could reflect the real biological information. Correlation analysis based on the ALV provides a new method and idea for the research of synchronism, which offer a foundation for further exploring the brain mode of thinking.

Citation： LI Xin, FAN Mengdi, SUN Xiaoqi, LI Quan, ZHANG Jie. Research on the correlation of brain function based on improved phase locking value. Journal of Biomedical Engineering, 2018, 35(3): 350-357. doi: 10.7507/1001-5515.201706069 Copy

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9.	Hassan M, Merlet I, Mheich A, et al. Identification of interictal epileptic networks from dense-EEG. Brain Topogr, 2017, 30(1): 60-76.
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16.	Dimitriadis S I, Sun Yu, Kwok K, et al. Cognitive workload assessment based on the tensorial treatment of EEG estimates of cross-frequency phase interactions. Ann Biomed Eng, 2015, 43(4): 977-989.
17.	魏金河, 严拱东, 赵仑, 等. 选择反应和选择心算时脑电相干谱的反应特点. 航天医学与医学工程, 1998, 11(5): 318-323.
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1. Sun J F, Hong X F, Tong S B. Phase synchronization analysis of EEG signals: an evaluation based on surrogate tests. IEEE Trans Biomed Eng, 2012, 59(8): 2254-2263.
2. Varela F, Lachaux J P, Rodriguez E, et al. The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci, 2001, 2(4): 229-239.
3. Subha D P, Joseph P K, Acharya U R, et al. EEG signal analysis: a survey. J Med Syst, 2010, 34(2): 195-212.
4. Fell J, Axmacher N. The role of phase synchronization in memory processes. Nat Rev Neurosci, 2011, 12(2): 105-118.
5. Sun Junfeng, Small M. Unified framework for detecting phase synchronization in coupled time series. Phys Rev E Stat Nonlin Soft Matter Phys, 2009, 80(4 Pt 2): 046219.
6. Aviyente S, Bernat E M, Evans W S, et al. A phase synchrony measure for quantifying dynamic functional integration in the brain. Hum Brain Mapp, 2011, 32(1): 80-93.
7. Lachaux J P, Rodriguez E, Martinerie J, et al. Measuring phase synchrony in brain signals. Hum Brain Mapp, 1999, 8(4): 194-208.
8. Yang Q, Jiang D N, Sun J F, et al. Cortical synchrony change under mental stress due to time pressure//Biomedical Engineering and Informatics, IEEE, 2010: 2004-2007.
9. Hassan M, Merlet I, Mheich A, et al. Identification of interictal epileptic networks from dense-EEG. Brain Topogr, 2017, 30(1): 60-76.
10. Park C, Worth R M, Rubchinsky L L. Fine temporal structure of beta oscillations synchronization in subthalamic nucleus in Parkinson’s disease. J Neurophysiol, 2010, 103(5): 2707-2716.
11. Stam C J, De Haan W, Daffertshofer A, et al. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain, 2009, 132(Pt 1): 213-224.
12. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci, 2005, 9(10): 474-480.
13. Singer W. Neuronal synchrony: a versatile code for the definition of relations? Neuron, 1999, 24(1): 49-65.
14. 裘嘉恒. 同步算法及其在癫痫脑电信号处理中的应用. 天津: 南开大学, 2008.
15. Quiroga R Q, Kraskov A, Kreuz T, et al. On the performance of different synchronization measures in real data: a case study on EEG signals. Physical Review E, 2002, 65:041903.
16. Dimitriadis S I, Sun Yu, Kwok K, et al. Cognitive workload assessment based on the tensorial treatment of EEG estimates of cross-frequency phase interactions. Ann Biomed Eng, 2015, 43(4): 977-989.
17. 魏金河, 严拱东, 赵仑, 等. 选择反应和选择心算时脑电相干谱的反应特点. 航天医学与医学工程, 1998, 11(5): 318-323.
18. 宋利清, 祖红月, 王索刚. 不同复杂度心算任务基于脑电的因果连接流增益研究. 科学技术与工程, 2016, 16(15): 54-59.
19. 李颖洁, 石菁, 朱春妍, 等. 心算任务下脑电信息传输. 上海大学学报: 自然科学版, 2007, 13(4): 439-443.

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