A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs.
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| Title: | A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs. |
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| Authors: | Li, Yang1 liyang@buaa.edu.cn, Lei, Mengying2 lmylei@buaa.edu.cn, Cui, Weigang3 cwg1994@buaa.edu.cn, Guo, Yuzhu3 yuzhuguo@buaa.edu.cn, Wei, Hua-Liang4 w.hualiang@sheffield.ac.uk |
| Source: | IEEE Transactions on Biomedical Engineering. Dec2019, Vol. 66 Issue 12, p3509-3525. 17p. |
| Subjects: | Granger causality test, Electroencephalography, Time-frequency analysis, Least squares, Sum of squares, Mathematical regularization |
| Abstract: | Objective: This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. Results: The proposed method is validated on two simulations, and then applied to source level motor imagery (MI) EEGs, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEGs are physiologically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. Conclusion: Experimental results confirm the effectiveness of the TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. Significance: The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 139785929 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Yang%22">Li, Yang</searchLink><relatesTo>1</relatesTo><i> liyang@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lei%2C+Mengying%22">Lei, Mengying</searchLink><relatesTo>2</relatesTo><i> lmylei@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cui%2C+Weigang%22">Cui, Weigang</searchLink><relatesTo>3</relatesTo><i> cwg1994@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Yuzhu%22">Guo, Yuzhu</searchLink><relatesTo>3</relatesTo><i> yuzhuguo@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wei%2C+Hua-Liang%22">Wei, Hua-Liang</searchLink><relatesTo>4</relatesTo><i> w.hualiang@sheffield.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Biomedical+Engineering%22">IEEE Transactions on Biomedical Engineering</searchLink>. Dec2019, Vol. 66 Issue 12, p3509-3525. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Granger+causality+test%22">Granger causality test</searchLink><br /><searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Time-frequency+analysis%22">Time-frequency analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Sum+of+squares%22">Sum of squares</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+regularization%22">Mathematical regularization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objective: This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. Methods: The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. Results: The proposed method is validated on two simulations, and then applied to source level motor imagery (MI) EEGs, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEGs are physiologically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. Conclusion: Experimental results confirm the effectiveness of the TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. Significance: The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Biomedical Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/TBME.2019.2906688 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 3509 Subjects: – SubjectFull: Granger causality test Type: general – SubjectFull: Electroencephalography Type: general – SubjectFull: Time-frequency analysis Type: general – SubjectFull: Least squares Type: general – SubjectFull: Sum of squares Type: general – SubjectFull: Mathematical regularization Type: general Titles: – TitleFull: A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Yang – PersonEntity: Name: NameFull: Lei, Mengying – PersonEntity: Name: NameFull: Cui, Weigang – PersonEntity: Name: NameFull: Guo, Yuzhu – PersonEntity: Name: NameFull: Wei, Hua-Liang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 00189294 Numbering: – Type: volume Value: 66 – Type: issue Value: 12 Titles: – TitleFull: IEEE Transactions on Biomedical Engineering Type: main |
| ResultId | 1 |
