Constructing Time-Varying Directed EEG Network by Multivariate Nonparametric Dynamical Granger Causality

Chanlin Yi; Yuan Qiu; Wanjun Chen; Chunli Chen; Yifeng Wang; Peiyang Li; Lei Yang; Xiabing Zhang; Lin Jiang; Dezhong Yao; Fali Li; Peng Xu

doi:10.1109/TNSRE.2022.3175483

IEEE Transactions on Neural Systems and Rehabilitation Engineering (Jan 2022)

Constructing Time-Varying Directed EEG Network by Multivariate Nonparametric Dynamical Granger Causality

Chanlin Yi,
Yuan Qiu,
Wanjun Chen,
Chunli Chen,
Yifeng Wang,
Peiyang Li,
Lei Yang,
Xiabing Zhang,
Lin Jiang,
Dezhong Yao,
Fali Li,
Peng Xu

Affiliations

Chanlin Yi: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Yuan Qiu: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Wanjun Chen: Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Chunli Chen: Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Yifeng Wang: Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Peiyang Li: ORCiD; School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
Lei Yang: Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Xiabing Zhang: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Lin Jiang: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Dezhong Yao: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Fali Li: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China
Peng Xu: ORCiD; Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, and the School of Life Science and Technology, Center for Information inMedicine, University of Electronic Science and Technology of China, Chengdu, China

DOI: https://doi.org/10.1109/TNSRE.2022.3175483
Journal volume & issue: Vol. 30
pp. 1412 – 1421

Abstract

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Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.

Published in IEEE Transactions on Neural Systems and Rehabilitation Engineering

ISSN: 1534-4320 (Print); 1558-0210 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Medical technology; Medicine: Therapeutics. Pharmacology
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333

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