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 | |
| Publication status | Published - 15 Jan 2013 |
| Externally published | Yes |
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Keywords
- Causality
- Linearity
- NARMAX
- Nonlinearity
ASJC Scopus subject areas
- Neuroscience(all)
Cite this
A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data. / Zhao, Yifan; Billings, Steve A.; Wei, Hualiang; He, Fei; Sarrigiannis, Ptolemaios G.
In: Journal of Neuroscience Methods, Vol. 212, No. 1, 15.01.2013, p. 79-86.Research output: Contribution to journal › Article
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TY - JOUR
T1 - A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data
AU - Zhao, Yifan
AU - Billings, Steve A.
AU - Wei, Hualiang
AU - He, Fei
AU - Sarrigiannis, Ptolemaios G.
PY - 2013/1/15
Y1 - 2013/1/15
N2 - 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.
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.
KW - Causality
KW - Linearity
KW - NARMAX
KW - Nonlinearity
UR - http://www.scopus.com/inward/record.url?scp=84867748196&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2012.09.019
DO - 10.1016/j.jneumeth.2012.09.019
M3 - Article
VL - 212
SP - 79
EP - 86
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
IS - 1
ER -