Zhang Yi, Yang Qin, Zhang Lifu, Ran Yu, Wang Guan, Celler Branko, Su Steven, Xu Peng, Yao Dezhong
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
J Neural Eng. 2021 Mar 30;18(4). doi: 10.1088/1741-2552/abecf2.
Noise-assisted multivariate empirical mode decomposition (NA-MEMD) based causal decomposition depicts a cause and effect relationship that is not based on the term of prediction, but rather on the phase dependence of time series. Here, we present the NA-MEMD based causal decomposition approach according to the covariation and power views traced to Hume and Kant:cause-effect interaction is first acquired, and the presence of a candidate cause and of the effect is then computed from the sensory input somehow.Based on the definition of NA-MEMD based causal decomposition, we show such causal relation is a phase relation where the candidate causes are not merely followed by effects, but rather produce effects.The predominant methods used in neuroscience (Granger causality, empirical mode decomposition-based causal decomposition) are validated, showing the applicability of NA-MEMD based causal decomposition, particular to brain physiological processes in bivariate and multiscale time series.We point to the potential use in the causality inference analysis in a complex dynamic process.
基于噪声辅助多元经验模态分解（NA-MEMD）的因果分解描绘了一种因果关系，这种关系不是基于预测项，而是基于时间序列的相位依赖性。在此，我们根据可追溯到休谟和康德的协变和幂次观点，提出基于NA-MEMD的因果分解方法：首先获取因果相互作用，然后以某种方式从感官输入中计算候选原因和结果的存在。基于基于NA-MEMD的因果分解的定义，我们表明这种因果关系是一种相位关系，其中候选原因不仅后面跟着结果，而且产生结果。对神经科学中使用的主要方法（格兰杰因果关系、基于经验模态分解的因果分解）进行了验证，表明基于NA-MEMD的因果分解的适用性，特别是对于双变量和多尺度时间序列中的大脑生理过程。我们指出了其在复杂动态过程的因果推断分析中的潜在用途。
