ORCID Profile
0000-0001-5263-5823
Current Organisation
University of Electronic Science and Technology of China
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Publisher: IOP Publishing
Date: 30-03-2021
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.
Publisher: Public Library of Science (PLoS)
Date: 10-07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: American Chemical Society (ACS)
Date: 05-07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Location: China
No related grants have been discovered for Yi ZHANG.