Print Email Facebook Twitter Modeling the nonlinear cortical response in EEG evoked by wrist joint manipulation Title Modeling the nonlinear cortical response in EEG evoked by wrist joint manipulation Author Vlaar, M.P. (TU Delft Biomechatronics & Human-Machine Control) Birpoutsoukis, Georgios (Vrije Universiteit Brussel) Lataire, John (Vrije Universiteit Brussel) Schouten, A.C. (TU Delft Biomechatronics & Human-Machine Control; University of Twente; Northwestern University) Schoukens, Johan (Vrije Universiteit Brussel) van der Helm, F.C.T. (TU Delft Biomechatronics & Human-Machine Control; Northwestern University) Date 2018 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. Subject Brain modelingKernelElectroencephalographyRobot sensing systemsWristEstimationNonlinear dynamical systems To reference this document use: http://resolver.tudelft.nl/uuid:e803a245-b307-4c01-8f52-c3417e5d8ac7 DOI https://doi.org/10.1109/TNSRE.2017.2751650 Embargo date 2018-03-13 ISSN 1534-4320 Source IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (1), 205-215 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2018 M.P. Vlaar, Georgios Birpoutsoukis, John Lataire, A.C. Schouten, Johan Schoukens, F.C.T. van der Helm Files PDF Modeling_the_Nonlinear_Co ... lation.pdf 2.06 MB Close viewer /islandora/object/uuid:e803a245-b307-4c01-8f52-c3417e5d8ac7/datastream/OBJ/view