A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

Yifan Zhao; Steve A. Billings; Hualiang Wei; Fei He; Ptolemaios G. Sarrigiannis

doi:10.1016/j.jneumeth.2012.09.019

A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

Yifan Zhao
, Steve A. Billings
, Hualiang Wei
, Fei He
, Ptolemaios G. Sarrigiannis

Research output: Contribution to journal › Article › peer-review

52 Citations (Scopus)

Abstract

A new NARX-based Granger linear and nonlinear casual influence detection method is presented in this paper to address the potential for linear and nonlinear models in data with applications to human EEG data analysis. Considering two signals initially, the paper introduces four indexes to measure the linearity and nonlinearity of a single signal, and one signal influencing the second signal. This method is then extended to the time-varying and multivariate cases. An adaptation of an Orthogonal Least Squares routine is employed to select the significant terms in the models. A numerical example is provided to illustrate the effectiveness of the new algorithms together with the application to real EEG data collected from 4 patients.

Original language	English
Pages (from-to)	79-86
Number of pages	8
Journal	Journal of Neuroscience Methods
Volume	212
Issue number	1
DOIs	https://doi.org/10.1016/j.jneumeth.2012.09.019
Publication status	Published - 15 Jan 2013
Externally published	Yes

Funding

The authors gratefully acknowledge that part of this work was financed by the Engineering and Physical Sciences Research Council (EPSRC), UK , and by the European Research Council (ERC) . Appendix A

Keywords

Causality
Linearity
NARMAX
Nonlinearity

ASJC Scopus subject areas

General Neuroscience

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10.1016/j.jneumeth.2012.09.019

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abstract = "A new NARX-based Granger linear and nonlinear casual influence detection method is presented in this paper to address the potential for linear and nonlinear models in data with applications to human EEG data analysis. Considering two signals initially, the paper introduces four indexes to measure the linearity and nonlinearity of a single signal, and one signal influencing the second signal. This method is then extended to the time-varying and multivariate cases. An adaptation of an Orthogonal Least Squares routine is employed to select the significant terms in the models. A numerical example is provided to illustrate the effectiveness of the new algorithms together with the application to real EEG data collected from 4 patients.",

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AU - Billings, Steve A.

AU - Wei, Hualiang

AU - He, Fei

AU - Sarrigiannis, Ptolemaios G.

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AB - A new NARX-based Granger linear and nonlinear casual influence detection method is presented in this paper to address the potential for linear and nonlinear models in data with applications to human EEG data analysis. Considering two signals initially, the paper introduces four indexes to measure the linearity and nonlinearity of a single signal, and one signal influencing the second signal. This method is then extended to the time-varying and multivariate cases. An adaptation of an Orthogonal Least Squares routine is employed to select the significant terms in the models. A numerical example is provided to illustrate the effectiveness of the new algorithms together with the application to real EEG data collected from 4 patients.

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KW - Linearity

KW - NARMAX

KW - Nonlinearity

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A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data

Abstract

Funding

Keywords

ASJC Scopus subject areas

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