Modeling the nonlinear cortical response in EEG evoked by wrist joint manipulation

Journal article (2018)

Authors

M.P. Vlaar Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
Georgios Birpoutsoukis Vrije Universiteit Brussel
John Lataire Vrije Universiteit Brussel
A.C. Schouten Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering , Northwestern University, University of Twente
Johan Schoukens Vrije Universiteit Brussel
F.C.T. van der Helm Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering , Northwestern University
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 M.P. Vlaar, Georgios Birpoutsoukis, John Lataire, A.C. Schouten, Johan Schoukens, F.C.T. van der Helm
DOI
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https://doi.org/10.1109/TNSRE.2017.2751650
Estimation Wrist Electroencephalography Robot sensing systems Brain modeling Kernel Nonlinear dynamical systems
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

Joint manipulation elicits a response from the sensors in the periphery which, via the spinal cord, arrives in the cortex. The average evoked cortical response recorded using electroencephalography was shown to be highly nonlinear; a linear model can only explain 10% of the variance of the evoked response, and over 80% of the response is generated by nonlinear behavior. The goal of this study is to obtain a nonparametric nonlinear dynamic model, which can consistently explain the recorded cortical response requiring little a priori assumptions about model structure. Wrist joint manipulation was applied in ten healthy participants during which their cortical activity was recorded and modeled using a truncated Volterra series. The obtained models could explain 46% of the variance of the evoked cortical response, thereby demonstrating the relevance of nonlinear modeling. The high similarity of the obtained models across participants indicates that the models reveal common characteristics of the underlying system. The models show predominantly high-pass behavior, which suggests that velocity-related information originating from the muscle spindles governs the cortical response. In conclusion, the nonlinear modeling approach using a truncated Volterra series with regularization, provides a quantitative way of investigating the sensorimotor system, offering insight into the underlying physiology.