Time-Varying Nonlinear Causality Detection Using Regularized Orthogonal Least Squares and Multi-Wavelets With Applications to EEG

Li, Yang; Lei, Meng-Ying; Guo, Yuzhu; Hu, Zhongyi; Wei, Hua‐Liang

doi:10.1109/access.2018.2818789

Cited by 18 publications

(7 citation statements)

References 54 publications

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“…A major application of the proposed method in our study is to investigate the TV model of nonstationary systems including EEG signals. Actually, the works of Li et al have shown that an effective model can assist reveal the underlying mechanisms of biological signals, for example, the studies of the causality between signals in different channels [30,39]. Thus, a promising research direction is the further applications in timefrequency distribution and causality detection of biomedical signals.…”

Section: Discussionmentioning

confidence: 99%

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

Zhang

Cui

et al. 2020

IEEE Trans. Instrum. Meas.

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Section: Discussionmentioning

confidence: 99%

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

Zhang

Cui

et al. 2020

IEEE Trans. Instrum. Meas.

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“…Equations (2) and (3) are autoregressive models, where

and

are the estimation of single-variable AR coefficients of the p-order AR model, and

and

are the residual errors (prediction error) of the AR process. Compared with the prediction error of the autoregression model, if the variance of the prediction error in the regression model decreases significantly, then the time series y ( t ) has a causal relationship with x ( t ) [ 26 ]. Quantified Granger causality index (GCI) is:

…”

Section: Methodsmentioning

confidence: 99%

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

Guo

et al. 2022

Brain Sciences

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Motor imagery (MI) refers to the mental rehearsal of movement in the absence of overt motor action, which can activate or inhibit cortical excitability. EEG mu/beta oscillations recorded over the human motor cortex have been shown to be consistently suppressed during both the imagination and performance of movements, although the specific effect on brain function remains to be confirmed. In this study, Granger causality (GC) was used to construct the brain functional network of subjects during motor imagery and resting state based on EEG in order to explore the effects of motor imagery on brain function. Parameters of the brain functional network were compared and analyzed, including degree, clustering coefficient, characteristic path length and global efficiency of EEG mu/beta rhythm in different states. The results showed that the clustering coefficient and efficiency of EEG mu/beta rhythm decreased significantly during motor imagery (p < 0.05), while degree distribution and characteristic path length increased significantly (p < 0.05), mainly concentrated in the frontal lobe and sensorimotor area. For the resting state after motor imagery, the changes of brain functional characteristics were roughly similar to those of the task state. Therefore, it is concluded that motor imagery plays an important role in activation of cortical excitability.

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“…Weighted sum of a set of basis functions is usually used to approximate the nonlinear function [29]. There are many choices of basis functions types, such as Gaussian function, polynomial, S function, wavelet function, etc.…”

Section: Performance Analysismentioning

confidence: 99%

LPV Modeling and Identification of Unsteady Aerodynamics for Fast Maneuvering Aircrafts

2019

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Fast maneuvers at large angles of attack are necessary for modern fighter aircrafts. The aerodynamic coefficients in this situation are unsteady, with the characteristics of hysteresis and fast timevarying. We propose an identification method based on the linear parameter varying (LPV) model. The model takes into account the hysteresis with respect to the angle of attack, and fast time varying with respect to the motion of post-stall maneuvers. By introducing the angular velocity as an exogenous variable, the model can guarantee the tracking ability of fast time-varying variations. We use the instrumental variable least squares (IVLS) algorithm to deal with the estimation problem. Finally, the experimental data obtained from maneuvers at large angles of attack from wind tunnel tests are used to validate the applicability and effectiveness of the proposed method. INDEX TERMS Instrumental variable least squares (IVLS) algorithm, large angle of attack, linear parameter varying (LPV) model, system identification, unsteady aerodynamics.

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Time-Varying Nonlinear Causality Detection Using Regularized Orthogonal Least Squares and Multi-Wavelets With Applications to EEG

Cited by 18 publications

References 54 publications

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

A Multiple Beta Wavelet-Based Locally Regularized Ultraorthogonal Forward Regression Algorithm for Time-Varying System Identification With Applications to EEG

Effects of Motor Imagery Tasks on Brain Functional Networks Based on EEG Mu/Beta Rhythm

LPV Modeling and Identification of Unsteady Aerodynamics for Fast Maneuvering Aircrafts

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