Noise-assisted multivariate empirical mode decomposition based causal decomposition for brain-physiological network in bivariate and multiscale time series. (30th March 2021)

• Record Type: Journal Article
• Authors: Zhang, Yi; Yang, Qin; Zhang, Lifu; Ran, Yu; Wang, Guan; Celler, Branko; Su, Steven; Xu, Peng; Yao, Dezhong
• Is Part Of: Journal of neural engineering. Volume 18:Number 4(2021)
• Publication Date: 2021-03-30
• Subjects: noise-assisted multivariate empirical mode decomposition based causal decomposition -- causality analysis -- bivariate and multiscale time series -- brain physiological processes; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/abecf2
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective. 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: a priori 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. Approach. 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. Main results. 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. Significance. We point to the potential use in the causality inference analysis in a complex dynamic process.