Modeling the Nonlinear Cortical Response in EEG Evoked by Wrist Joint Manipulation

Martijn P. Vlaar, Georgios Birpoutsoukis, John Lataire, Maarten Schoukens, Alfred C. Schouten, Johan Schoukens, Frans C. T. van der Helm

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)


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 paper 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.

Original languageEnglish
Article number8036235
Pages (from-to)205-215
Number of pages11
JournalIEEE transactions on neural systems and rehabilitation engineering
Issue number1
Publication statusPublished - Jan 2018
Externally publishedYes


  • Electroencephalography
  • nonlinear system identification
  • regularization
  • robotic manipulator
  • Volterra series estimation


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