Gupta Kajari, Paluš Milan
Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Pod Vodárenskou Věží 2, 182 07 Prague 8, Czech Republic.
Entropy (Basel). 2021 Apr 25;23(5):526. doi: 10.3390/e23050526.
An information-theoretic approach for detecting causality and information transfer was applied to phases and amplitudes of oscillatory components related to different time scales and obtained using the wavelet transform from a time series generated by the Epileptor model. Three main time scales and their causal interactions were identified in the simulated epileptic seizures, in agreement with the interactions of the model variables. An approach consisting of wavelet transform, conditional mutual information estimation, and surrogate data testing applied to a single time series generated by the model was demonstrated to be successful in the identification of all directional (causal) interactions between the three different time scales described in the model. Thus, the methodology was prepared for the identification of causal cross-frequency phase-phase and phase-amplitude interactions in experimental and clinical neural data.
一种用于检测因果关系和信息传递的信息论方法被应用于与不同时间尺度相关的振荡成分的相位和幅度，这些振荡成分是通过对癫痫发作模型生成的时间序列进行小波变换得到的。在模拟的癫痫发作中识别出了三个主要时间尺度及其因果相互作用，这与模型变量的相互作用一致。一种由小波变换、条件互信息估计和替代数据测试组成的方法，应用于该模型生成的单个时间序列，被证明能够成功识别模型中描述的三个不同时间尺度之间的所有方向性（因果）相互作用。因此，该方法可用于识别实验和临床神经数据中的因果跨频率相位-相位和相位-幅度相互作用。
