DocumentCode :
28290
Title :
Assessing Dynamic Spectral Causality by Lagged Adaptive Directed Transfer Function and Instantaneous Effect Factor
Author :
Haojie Xu ; Yunfeng Lu ; Shanan Zhu ; Bin He
Author_Institution :
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
Volume :
61
Issue :
7
fYear :
2014
fDate :
Jul-14
Firstpage :
1979
Lastpage :
1988
Abstract :
It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The nonzero covariance of the model´s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In this study, we first investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness i- time-variant spectral causality assessment was demonstrated through the mutual causality investigation of brain activity during the VEP experiments.
Keywords :
Gaussian processes; autoregressive processes; causality; electroencephalography; medical signal processing; neurophysiology; visual evoked potentials; Gaussian residual conditions; acyclic causal ordering; causality estimators; complex nervous system; conventional ADTF method; dynamic directed instantaneous connectivity; dynamic spectral causality assessment; instantaneous effect factor; instantaneous effect factor value; instantaneous effect phenomenon; lagged adaptive directed transfer function; nonzero covariance; physiological signals; practical estimators; real visual evoked potential data; spectral Granger causality; statistical analysis; time-lagged ADTF method; time-lagged adaptive directed transfer function; time-lagged dynamic interactions; time-varying multivariate autoregressive models; Adaptation models; Biological system modeling; Covariance matrices; Estimation; Heuristic algorithms; Matrix decomposition; Visualization; Adaptive directed transfer function (ADTF); causal ordering estimation; dynamic spectral causality; instantaneous effect factor (IEF); time-lagged causality; visual evoked potentials (VEPs);
fLanguage :
English
Journal_Title :
Biomedical Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9294
Type :
jour
DOI :
10.1109/TBME.2014.2311034
Filename :
6763074
Link To Document :
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