Nonlinear Modeling of Cortical Responses to Mechanical Wrist Perturbations Using the NARMAX Method

Yuanlin Gu, Yuan Yang, Julius P.A. Dewald, Frans C.T. Van Der Helm, Alfred C. Schouten, Hua Liang Wei*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 autoregressive-moving-Average model with exogenous inputs (NARMAX). Methods: EEG was recorded from ten participants when 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.

Original languageEnglish (US)
Article number9153940
Pages (from-to)948-958
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number3
DOIs
StatePublished - Mar 2021

Keywords

  • closed-loop system
  • EEG
  • NARMAX
  • nonlinear system identification
  • sensorimotor control

ASJC Scopus subject areas

  • Biomedical Engineering

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