Bahrami Farzaneh, Taghizadeh Maryam, Shayegh Farzaneh
Faculty of Technology and Engineering, Shahrekord University, Shahrekord, Iran.
Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.
J Med Signals Sens. 2024 Aug 6;14:24. doi: 10.4103/jmss.jmss_15_24. eCollection 2024.
Unlike other functional integration methods that examine the relationship and correlation between two channels, effective connection reports the direct effect of one channel on another and expresses their causal relationship. In this article, we investigate and classify electroencephalographic (EEG) signals based on effective connectivity. In this study, we leverage the Granger causality (GC) relationship, a method for measuring effective connectivity, to analyze EEG signals from both healthy individuals and those with autism. The EEG signals examined in this article were recorded during the presentation of abstract images. Given the nonstationary nature of EEG signals, a vector autoregression model has been employed to model the relationships between signals across different channels. GC is then used to quantify the influence of these channels on one another. Selecting regions of interest (ROI) is a critical step, as the quality of the time periods under consideration significantly impacts the outcomes of the connectivity analysis among the electrodes. By comparing these effects in the ROI and various areas, we have distinguished healthy subjects from those suffering from autism. Furthermore, through statistical analysis, we have compared the results between healthy individuals and those with autism. It has been observed that the causal relationship between these two hemispheres is significantly weaker in healthy individuals compared to those with autism.
与其他研究两个通道之间关系和相关性的功能整合方法不同，有效连接反映了一个通道对另一个通道的直接影响，并表达了它们之间的因果关系。在本文中，我们基于有效连接对脑电图（EEG）信号进行研究和分类。在这项研究中，我们利用格兰杰因果关系（GC），一种测量有效连接的方法，来分析来自健康个体和自闭症患者的EEG信号。本文所研究的EEG信号是在呈现抽象图像期间记录的。鉴于EEG信号的非平稳特性，采用向量自回归模型来对不同通道信号之间的关系进行建模。然后使用GC来量化这些通道之间的相互影响。选择感兴趣区域（ROI）是关键步骤，因为所考虑时间段的质量会显著影响电极间连接性分析的结果。通过比较ROI和各个区域中的这些影响，我们区分了健康受试者和自闭症患者。此外，通过统计分析，我们比较了健康个体和自闭症患者之间的结果。据观察，与自闭症患者相比，健康个体中这两个半球之间的因果关系明显较弱。
