Gu, Y., Yang, Y., Dewald, J. et al. (3 more authors) (2021) Nonlinear modeling of cortical responses to mechanical wrist perturbations using the NARMAX method. IEEE Transactions on Biomedical Engineering, 68 (3). pp. 948-958. ISSN 0018-9294
Abstract
Objective: Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigating abnormal cortical responses associated with sensorimotor impairments. Thus, the goal of our study is to identify this common model structure and therefore to build a nonlinear dynamic model of cortical responses, using nonlinear autoregressivemoving-average model with exogenous inputs (NARMAX). Methods: EEG was recorded from ten participants when they were receiving continuous wrist perturbations. A common model structure detection method was developed for identifying a common NARMAX model structure across all participants, with individualized parameter values. The results were compared to conventional subject-specific models. Results: The proposed method achieved 93.91% variance accounted for (VAF) when implementing a one-step-ahead prediction and around 50% VAF for a k-step ahead prediction (k = 3), without a substantial drop of VAF as compare to subject-specific models. The estimated common structure suggests that the measured cortical response is a mixed outcome of the nonlinear transformation of external inputs and local neuronal interactions or inherent neuronal dynamics at the cortex. Conclusion: The proposed method well determined the common characteristics across subjects in the cortical responses to wrist perturbations. Significance: It provides new insights into the human sensorimotor nervous system in response to somatosensory inputs and paves the way for future translational studies on assessments of sensorimotor impairments using our modeling approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | EEG; nonlinear system identification; NARMAX; closed-loop system; sensorimotor control |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/I011056/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/H00453X/1 NORWEGIAN RESEARCH COUNCIL UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Aug 2020 08:37 |
Last Modified: | 24 Jan 2022 14:11 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/tbme.2020.3013545 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164370 |