Li Zhaohui, Wang Xinyan, Xing Yanyu, Zhang Xi, Yu Tao, Li Xiaoli
School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China; Hebei Key Laboratory of information transmission and signal processing, Yanshan University, Qinhuangdao, 066004, China.
School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China.
Neural Netw. 2023 Oct;167:838-846. doi: 10.1016/j.neunet.2023.07.007. Epub 2023 Jul 8.
Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.
相位同步是大脑中神经元信息处理的重要机制。当前大多数相位同步测量方法是双变量的，主要关注成对时间序列之间的同步。然而，这些方法无法全面呈现神经系统中的全局相互作用。考虑到多变量神经信号分析的普遍性和重要性，迫切需要对神经网络中的全局相位同步（GPS）进行量化。因此，我们提出了一种名为符号相位差和排列熵（SPDPE）的新测量方法，它对多变量神经信号中的相位差进行符号化，并根据符号序列的排列模式估计GPS。使用Kuramoto和Rössler模型生成的模拟数据对SPDPE的性能进行了评估。结果表明，SPDPE对数据长度的敏感性较低，在准确表征GPS和有效抵抗噪声方面优于现有方法。此外，为了用真实数据验证该方法，通过计算从10名癫痫患者发作起始区记录的立体脑电图（SEEG）数据的GPS，将其应用于癫痫发作和非癫痫发作的分类。我们相信，SPDPE将在许多应用中改善GPS的估计，例如基于脑电图的脑机接口、脑建模以及同步脑电图-功能磁共振成像分析。
