Li Xin, Fan Mengdi, Sun Xiaoqi, Li Quan, Zhang Jie
Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004,
Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Jun 25;35(3):350-357. doi: 10.7507/1001-5515.201706069.
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.
锁相值(PLV)是分析大脑相位同步性的一种有效方法,它能有效分离脑电图(EEG)信号的相位成分,并反映信号强度对功能连接性的影响。然而,传统的锁相算法仅分析信号的相位成分,无法有效分析EEG信号的特征。为了解决这个问题,提出了一种名为幅度锁相值(ALV)的新算法。首先,改进算法使用经验模态分解获得本征模态函数,并将其用作希尔伯特变换(HT)的输入。然后计算瞬时幅度,最后计算ALV。基于ALV,可以测量EEG信号电极之间的瞬时幅度。收集了14名受试者在不同认知任务下的数据,并通过改进方法分析了算术过程中脑区的相干性。结果表明,相干性与认知活动之间存在负相关,中央和顶叶区域最为敏感。通过ALV方法进行的定量分析可以反映真实的生物学信息。基于ALV的相关性分析为同步性研究提供了一种新的方法和思路,为进一步探索大脑思维模式奠定了基础。